CN114665766B - Permanent magnet synchronous motor force-position hybrid control system based on load moment estimation - Google Patents
Permanent magnet synchronous motor force-position hybrid control system based on load moment estimation Download PDFInfo
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Abstract
The invention discloses a force-bit hybrid control system of a permanent magnet synchronous motor based on load moment estimation, which comprises a force PI controller, a position control current given calculation module, a multi-target particle swarm algorithm module, a current PI controller, a IPark conversion module, a space vector pulse width modulation module (SVPWM), a three-phase inverter, a Permanent Magnet Synchronous Motor (PMSM), an encoder, a Clarke conversion module, a Park conversion module, a load moment estimation module, a derivative, a multiplier and first to fifth subtractors, wherein the first subtracter is used for calculating the load moment of the permanent magnet synchronous motor; compared with the traditional control structure, the method for controlling the permanent magnet synchronous motor by using the control system can simply and effectively realize force control and position control simultaneously, adopts a load moment algorithm to estimate load moment, and realizes force-bit hybrid control with simpler algorithm and lower cost.
Description
Technical Field
The invention relates to the field of motor control, in particular to a force-position hybrid control system of a permanent magnet synchronous motor based on load moment estimation.
Background
Permanent magnet alternating current servo systems using permanent magnet synchronous motors as executive components are widely applied to the fields of production and manufacture, medical facilities, national defense and military, and the like. The control method is mainly divided into vector control and direct torque control, and the vector control technology has the advantages of high precision, high dynamic response, wide speed regulation range and the like, and is generally suitable for occasions with higher requirements on control precision. However, the current control structure of the vector control technology of the permanent magnet synchronous motor is a three-ring series structure of position, speed and current. The position ring is the outermost ring, and when position control is performed, force control cannot be performed. Force control is achieved by means of the current loop of the inner loop, and when force control is performed, position control cannot be performed. A single motor is not suitable for applications where both force control and position control are required. In this regard, in the existing force-position hybrid control solution, a plurality of permanent magnet synchronous motors are often required to be cooperatively controlled at the same time, so as to realize force control in a certain plane and position control in another plane, but this greatly increases algorithm complexity and application cost, and reduces timeliness and accuracy of control. Moreover, the load moment of force closed-loop control is usually realized through a moment sensor, so that the application cost is further increased.
Accordingly, improvements in the art are needed.
Disclosure of Invention
The invention aims to solve the technical problem of providing a force-position hybrid control system of a permanent magnet synchronous motor based on load moment estimation, which changes a force and position control structure from a serial structure to a parallel structure through a multi-target particle swarm algorithm, realizes the integration and the optimization of force control and position control of a servo system of the permanent magnet synchronous motor, and realizes the load moment feedback of a torque-free sensor through load moment estimation.
In order to solve the technical problems, the invention provides a force-position hybrid control system of a permanent magnet synchronous motor based on load moment estimation, which comprises the permanent magnet synchronous motor, wherein two-phase currents i a and i b of the permanent magnet synchronous motor are used as inputs of a Clarke transformation module, and the actual position theta m of the permanent magnet synchronous motor is used as inputs of a derivative, a multiplier and a second subtracter respectively; given positionFor a preset constant as an input of the second subtracter, a given torque T * is a preset constant as an input of the first subtracter, and a given direct-axis current/>An input to a fifth subtractor;
the output of the derivative is respectively connected with the input of the third subtracter and the input of the load moment estimation module;
The output of the Clarke transformation module is connected with the input of the Park transformation module, the output of the multiplier is respectively connected with the input of the Park transformation module and the input of the IPark transformation module, and the output of the Park transformation module is respectively connected with the input of the load moment estimation module, the input of the fourth subtracter and the input of the fifth subtracter; the output of the load moment estimation module is connected with the input of the first subtracter;
The output of the second subtracter is respectively connected with the input of the multi-target particle swarm algorithm module and the input of the position P controller; the output of the position P controller is connected with the input of a third subtracter, the output of the third subtracter is connected with the input of a speed PI controller, and the output of the speed PI controller is connected with the input of a multi-target particle swarm algorithm;
The output of the first subtracter is respectively connected with the input of the multi-target particle swarm algorithm module and the input of the force PI controller, and the output of the force PI controller is connected with the input of the multi-target particle swarm algorithm module; the output of the multi-target particle swarm algorithm module is connected with the input of a fourth subtracter;
the output of the current PI controller is connected with the input of a three-phase inverter after passing through a IPark conversion module and a space vector pulse width modulation module in sequence, and the permanent magnet synchronous motor is driven by the three-phase inverter.
The invention also provides a method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force-position hybrid control system based on load moment estimation, which comprises the following specific steps:
Step 1, obtaining two-phase currents i a and i b by a method of collecting current signals through two Hall current sensors, and obtaining actual currents i a and i β under a static two-phase coordinate system through a Clarke transformation module to serve as input of a Park transformation module;
Step 2, acquiring an actual position theta m of the permanent magnet synchronous motor through an encoder, acquiring an actual electric angle theta e of the actual position theta m through a multiplier, acquiring actual direct-axis currents i a and i β and an actual electric angle theta e under a static two-phase coordinate system through a Park conversion module, outputting the actual direct-axis current i d to a fifth subtracter, outputting the actual quadrature-axis current i q to a fourth subtracter, and simultaneously outputting the actual direct-axis current i d and the actual quadrature-axis current i q to a load moment estimation module;
Step 3, the actual position theta m passes through a derivative to obtain an actual angular velocity omega m, and then the actual angular velocity omega m is respectively output to a load moment estimation module and a third subtracter, and the load moment estimation module outputs an estimated load moment As an input to a first subtractor;
step 4, given torque T * and estimated load moment After a first subtracter, a given torque T * and an estimated load torque/>, are obtainedThe error of (2) is used as the input of the force PI controller, and is output as the given current/>, of the force control after passing through the force PI controllerThe given torque T * is then combined with the estimated load torque/>Error, force control given current/>All are input into a multi-target particle swarm algorithm module;
Step 5, given position And the actual position theta m is subjected to a second subtracter to obtain a given position/>The error with the actual position theta m is input into a position P controller, and the given rotating speed/>, obtained through the position P controllerGiven rotational speed/>And the actual angular velocity omega m is subjected to a third subtracter to obtain a given rotating speed/>The error from the actual angular velocity omega m is taken as the input of a speed PI controller, the output of which controls the given current/>, for the position
Then the given position is givenError from actual position θ m, position control given current/>Inputting the multiple target particle swarm algorithm modules together;
step 6, the multi-target particle swarm algorithm module inputs the given position according to the input An objective function is constructed with the error of the actual position theta m, the error of the given torque T * and the error of the actual load torque T L, the force control and position control conditions of the current motor are evaluated, an optimal integrated optimization factor a is obtained through a multi-target particle swarm algorithm, and the force is controlled to give current/>, through the optimal integrated optimization factor aAnd position control given current/>Integrating output as force-bit hybrid control current set/>And input to a fourth subtractor;
Step 7, the current PI controller module comprises an quadrature axis current PI controller and a direct axis current PI controller, and the force-bit mixed control current is given The force bit mixed control current given/> is obtained after the actual quadrature axis current i q passes through a fourth subtracterError with actual quadrature axis current i q, then force-bit hybrid control current given/>The error with the actual quadrature axis current i q is calculated by a quadrature axis current PI controller to output a given quadrature axis voltage/>
Given the direct currentObtaining a given direct axis current/>, through a fifth subtracter, with the actual direct axis current i d Error from the actual straight axis current i d, then give the straight axis current/>The error with the actual direct-axis current i d is calculated by the direct-axis current PI controller to output a given direct-axis voltage/>Given quadrature axis voltage/>And given the direct axis voltage/>Together input to IPark transform modules;
Step 8, giving the quadrature axis voltage Given the direct axis voltage/>And the actual electrical angle theta e is converted into a given voltage/>, under a static two-phase coordinate system, through a IPark conversion moduleAnd/>And input to a space vector pulse width modulation module;
step 9, voltage given value under static two-phase coordinate system And/>Six paths of PWM signals are obtained through a space vector pulse width modulation module and used as input for controlling the three-phase inverter;
And step 10, the three-phase inverter performs switching action on the six switching tubes according to the six input PWM signals, and controls the bus voltage U dc to be input into the permanent magnet synchronous motor so as to realize the driving of the permanent magnet synchronous motor.
As the improvement of the method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force-position hybrid control system based on load moment estimation, the invention has the advantages that:
the formula of the first subtracter in the step 4 is as follows:
The calculation of the force PI controller is shown in equation (8):
Wherein K PT is the scaling factor of the force PI controller; k IT is the integral coefficient of the force PI controller; the form of equation (8) in a computer discrete system is:
where k is the sampling instant and T s is the sampling time.
As a further improvement of the method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force-position hybrid control system based on load moment estimation, the invention has the advantages that the load moment estimation is realized:
the formula of the second subtracter in the step 5 is as follows:
The formula of the third subtracter is as follows:
the speed PI controller is calculated as shown in equation (10):
Wherein K PP is the scaling factor of the position P controller; k PS is the scaling factor of the speed PI controller; k IS is the integral coefficient of the speed PI controller, and has
The form of equation (10) in a computer discrete system is:
As a further improvement of the method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force-position hybrid control system based on load moment estimation, the invention has the advantages that the load moment estimation is realized:
the specific steps of the implementation of the multi-target particle swarm algorithm module in the step6 are as follows:
(1) Initializing a particle swarm with a population size of N;
vi∈[vmin,vmax],xi∈[xmin,xmax],i＝1,2,…,N-1,N (12)
Wherein v i is the velocity of each particle, v min and v max are the domain ranges of velocity, x i is the position of each particle, x min and x max are the domain ranges of position, and N is the total number of particles;
(2) Constructing the objective function as shown in formula (6);
wherein f li is an index for evaluating force control, and f wei is an index for controlling position control;
(3) Constructing a general evaluation function:
wherein r (r is more than or equal to 0 and less than or equal to 1) is a force control emphasis coefficient;
calculating the fitness value of each particle through a formula (14), and obtaining a global extremum as shown in a formula (15);
gBest＝min[fi],i＝1,2,…,N-1,N (15)
(4) Calculating to obtain an individual extremum pBest i;
pBesti＝min[f(n)],n＝1,2,…,D-1,D (16)
wherein D is the iteration number;
(5) Updating the velocity and position of each particle;
vi(n)＝vi(n-1)+c1×rand()×(pBesti(n-1)-xi(n-1)) (17)
+c2×rand()×(gBest(n-1)-xi(n-1))
xi(n)＝xi(n-1)+vi(n)
Where v i (n) and x i (n) represent the speed and position of each particle of the current iteration, v i (n-1) and x i (n-1) represent the speed and position of each particle of the previous iteration, rand () is a random number between 0 and 1, and c 1 and c 2 represent learning factors for the individual itself and for the global whole, respectively;
(6) Evaluating the fitness value according to the termination condition formula (18), outputting an optimal optimization factor a=x i if the termination condition is satisfied, and outputting a force-bit mixture control current setting integrating force control and position control according to the formula (19)
f≤Thr (18)
Wherein Thr is a set overall evaluation function threshold;
Wherein a (a is more than or equal to 0 and less than or equal to 1) is an integrated optimization factor of force control and position control;
(7) Returning to the step (2) without meeting the termination, and entering the iteration process again until the termination condition is met or the highest iteration times are reached;
(8) After training, an offline table is generated, and each torque setting and position setting can find an optimal integrated optimization factor a, so that the force control and the position control of the motor are both optimal.
As a further improvement of the method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force-position hybrid control system based on load moment estimation, the invention has the advantages that the load moment estimation is realized:
the fourth subtracter in step 7 has the following formula:
The formula of the fifth subtracter is as follows:
The calculation formula of the current PI controller module is as follows:
Wherein K Piq is the proportionality coefficient of the quadrature axis current PI controller; k Iiq is the integral coefficient of the quadrature axis current PI controller; k Pid is the scaling factor of the direct current PI controller; k Iid is the integral coefficient of the direct current PI controller;
the form of equation (20) in a computer discrete system is:
As a further improvement of the method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force-position hybrid control system based on load moment estimation, the invention has the advantages that the load moment estimation is realized:
the implementation steps of the load moment estimation module in the step 3 are as follows:
the electromagnetic torque equation of the permanent magnet synchronous motor is as follows:
Wherein T e is electromagnetic torque, L d is motor direct axis inductance, L q is motor quadrature axis inductance, Is a permanent magnet flux linkage, and P n is the pole pair number of the permanent magnet synchronous motor;
the mechanical equation of motion of the permanent magnet synchronous motor is as follows:
wherein J is the rotational inertia of the motor, T L is the actual load moment, and B is the viscosity coefficient;
The selected state variables are:
x＝[ωm TL]T (5)
Wherein, T L is the actual load moment, which is estimated by a moment observer formula (7):
According to the formulas (3) and (4), the state space equations are written as follows:
Wherein, Is the first derivative of the mechanical angular velocity,/>Is the first derivative of the actual load moment;
the load moment observer is then designed as follows:
Wherein, To estimate the first derivative of the mechanical angular velocity,/>To estimate the first derivative of the load moment, L 1 and L 2 are feedback coefficients, respectively.
As a further improvement of the method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force-position hybrid control system based on load moment estimation, the invention has the advantages that the load moment estimation is realized:
The transformation formula of the Clarke transformation module is as follows:
wherein, the phase current i c accords with: i a+ib+ic = 0;
the formula of the multiplier is as follows: θ e＝Pn·θm,Pn is the pole pair number of the permanent magnet synchronous motor;
The arithmetic formula of the derivative is as follows: omega m＝dθm/dt;
The Park transformation formula is as follows:
Wherein θ e is the actual electrical angle;
the IPark transform formula is:
The beneficial effects of the invention are mainly as follows:
Compared with the traditional three-ring series control structure of position, speed and current, the force-position hybrid control system of the permanent magnet synchronous motor based on load moment estimation can simply and effectively realize force control and position control simultaneously, adopts a load moment algorithm to estimate load moment, realizes force-position hybrid control with a simpler algorithm and lower cost, and is particularly suitable for occasions with higher requirements on force and position control of industrial robots and the like.
Drawings
The following describes the embodiments of the present invention in further detail with reference to the accompanying drawings.
FIG. 1 is a block diagram of a hybrid control system for the force and position of a permanent magnet synchronous motor based on load torque estimation according to the present invention;
FIG. 2 is a schematic block diagram of the calculation of the force control given current in FIG. 1;
FIG. 3 is a schematic block diagram of the calculation of the position control given current in FIG. 1;
FIG. 4 is a flowchart of a method for implementing the multi-target particle swarm algorithm in FIG. 1;
FIG. 5 is a schematic block diagram of the Clarke transformation module of FIG. 1;
FIG. 6 is a schematic block diagram of the Park transform module of FIG. 1;
FIG. 7 is a schematic block diagram of the current PI controller module of FIG. 1;
FIG. 8 is a schematic block diagram of the IPark transform module of FIG. 1;
FIG. 9 is a schematic block diagram of the space vector pulse width modulation module of FIG. 1;
Fig. 10 is a block diagram of a semi-physical motor test platform based on dsace in experiment 1.
Detailed Description
The invention will be further described with reference to the following specific examples, but the scope of the invention is not limited thereto:
Example 1,
The permanent magnet synchronous motor force-bit hybrid control system based on load moment estimation comprises a force PI controller, a position control current given calculation module (comprising a position P controller and a speed PI controller), a multi-target particle swarm algorithm module, a current PI controller, a IPark transformation module, a Space Vector Pulse Width Modulation (SVPWM) module, a three-phase inverter, a Permanent Magnet Synchronous Motor (PMSM), an encoder, a Clarke transformation module, a Park transformation module, a load moment estimation module, a derivative, a multiplier and first to fifth subtractors, as shown in figure 1;
Collecting two-phase currents (i a and i b) of a Permanent Magnet Synchronous Motor (PMSM) by adopting a two-phase Hall current sensor sampling method as input of a Clarke transformation module, and collecting the actual position theta m of the Permanent Magnet Synchronous Motor (PMSM) by adopting an encoder as input of a derivative, a multiplier and a second subtracter respectively; estimating an estimated load torque of a Permanent Magnet Synchronous Motor (PMSM) using a load torque estimation module The input to the first subtractor is the given torque T *, the other input to the second subtractor is the given position/>Given torque T * and given position/>Is a preset constant.
The output of the first subtracter is respectively connected with the input of the multi-target particle swarm algorithm module and the input of the force PI controller, and the output of the force PI controller is connected with the input of the multi-target particle swarm algorithm module; the output of the multi-target particle swarm algorithm module is connected with the input of a fourth subtracter;
The output of the second subtracter is respectively connected with the input of the multi-target particle swarm algorithm module and the input of the position P controller; the actual position theta m is respectively connected with the input of the load moment estimation module and the input of the third subtracter after passing through the derivative, the output of the position P controller is connected with the input of the third subtracter, the output of the third subtracter is connected with the input of the speed PI controller, and the output of the speed PI controller is connected with the input of the multi-target particle swarm algorithm;
The output of the Clarke transformation module is connected with the input of the Park transformation module, the output of the multiplier is respectively connected with the input of the Park transformation module and the input of the IPark transformation module, the output of the Park transformation module is respectively connected with the input of the load moment estimation module, the input of the fourth subtracter and the input of the fifth subtracter, and the output of the load moment estimation module is connected with one input of the first subtracter; another input of the fifth subtracter is given by Since the system adopts/>FOC basic control method of (2), thus given/>
The output of the fourth subtracter and the output of the fifth subtracter are connected with the input of a current PI controller, the output of the current PI controller is connected with a three-phase inverter through a IPark conversion module and a Space Vector Pulse Width Modulation (SVPWM) module in sequence, and a Permanent Magnet Synchronous Motor (PMSM) is driven through the three-phase inverter.
The method for controlling the permanent magnet synchronous motor by using the permanent magnet synchronous motor force-position hybrid control system based on load moment estimation comprises the following steps: the input of the force PI controller is the output of the first subtracterThe output is force control given current/>The input of the position control current given calculation module (comprising a position P controller and a speed PI controller) is the output/>, of the second subtracterThe output is a position control given current/>The multi-target particle swarm algorithm module is used for inputting/>/>Constructing an objective function, evaluating the force control and position control conditions of the current motor, obtaining an optimal optimization factor through a multi-objective particle swarm algorithm, and controlling the given current/>, by the force control through the optimal optimization factorAnd position control given current/>Integrating output as force-bit hybrid control current set/>Meanwhile, two-phase currents i a and i b are collected through a two-phase Hall current sensor and used as input of a Clarke transformation module, and actual currents i a and i β under a static two-phase coordinate system are obtained and used as input of a Park transformation module. The actual position theta m is acquired by an encoder and is input into a second subtracter and a multiplier, the calculated expression theta e＝Pn·θm(Pn of the multiplier is the pole logarithm of the motor, and the obtained actual electric angle theta e enters a Park conversion module and a IPark conversion module. The Park conversion module outputs an actual straight-axis current i d to the fifth subtracter, outputs actual quadrature-axis currents i q to the fourth subtracter, and simultaneously outputs an actual straight-axis current i d and an actual quadrature-axis current i q to the load moment estimation module; subsequently, the current PI controller outputs a given quadrature axis voltage/>And given the direct axis voltage/>As input to IPark transform modules; IPark the transformation module outputs a given voltage/>, under a static two-phase coordinate systemAnd/>As input to a space vector pulse width modulation module (SVPWM) module; and 6 paths of PWM signals output by a Space Vector Pulse Width Modulation (SVPWM) module control the three-phase inverter to output voltage, so as to drive a Permanent Magnet Synchronous Motor (PMSM). The specific process is as follows:
Step 1, clarke transformation module principle, as shown in FIG. 5.
Two-phase currents i a and i b under a three-phase coordinate system of the permanent magnet synchronous motor are obtained through a method of collecting current signals through two Hall current sensors and are input into a Clarke transformation module, and a transformation formula of the Clarke transformation module is as follows:
Wherein the phase current i c is calculated by the equation i a+ib+ic =0;
The outputs of the Clarke transformation module are the actual currents i a and i β in the stationary two-phase coordinate system, which are used as inputs of the Park transformation module.
Step 2, park conversion module principle, as shown in fig. 6. The current i a and i β under the static two-phase coordinate system input in the step 1 and the actual electric angle theta e obtained by the actual position theta m of the permanent magnet synchronous motor through a multiplier pass through a Park conversion module to obtain the actual direct axis current i d and the actual quadrature axis current i q under the synchronous rotation coordinate system, wherein the Park conversion formula is as follows:
Wherein, θ e is the actual electrical angle, and θ e,Pn is the pole pair number of the permanent magnet synchronous motor obtained by the actual position θ m of the permanent magnet synchronous motor through a multiplier θ e＝Pn·θm.
The actual direct axis current i d under the synchronous rotation coordinate system output by the Park conversion module is given to the fifth subtracter, the actual quadrature axis current i q is output to the fourth subtracter, and the actual direct axis current i d and the actual quadrature axis current i q are simultaneously output to the load moment estimation module.
Step3, load moment estimation module
Acquiring an actual position theta m of a Permanent Magnet Synchronous Motor (PMSM) by adopting an encoder, calculating by using a derivative to obtain an actual angular velocity omega m＝dθm/dt, and then respectively outputting the actual angular velocity omega m to a load moment estimation module and a third subtracter;
Step 2 of outputting the actual direct-axis current i d and the actual quadrature-axis current i q, and the actual angular velocity ω m of the output of the derivative as the input of the load moment estimating module, and outputting as the estimated load moment As input to a first subtractor. The specific steps of the implementation are as follows:
the electromagnetic torque equation of the permanent magnet synchronous motor is as follows:
Wherein T e is electromagnetic torque, L d is motor direct axis inductance, L q is motor quadrature axis inductance, Is a permanent magnet flux linkage.
The mechanical equation of motion of the permanent magnet synchronous motor is as follows:
Wherein J is the rotational inertia of the motor, T L is the actual load moment, and B is the viscosity coefficient.
The selected state variables are:
x＝[ωm TL]T (5)
the actual angular velocity ω m is measurable and the actual load moment T L is not measurable but estimated by a designed load moment observer.
According to the formulas (3) and (4), the state space equations are written as follows:
Wherein, Is the first derivative of the mechanical angular velocity,/>Is the first derivative of the actual load moment.
Then, according to the general design method of the lunberger (Luenberger) observer, a load moment observer is designed as in formula (7):
Wherein, To estimate the first derivative of the mechanical angular velocity,/>To estimate the first derivative of the load moment, L 1 and L 2 are feedback coefficients, respectively.
Therefore, a closed loop feedback algorithm of the semi-physical motor test platform based on dSPACE of Desbeck electromechanical control technology company as shown in fig. 10 is constructed, the actual load moment T L can be observed, a torque sensor is replaced, and the cost of the force-position hybrid control system is further reduced.
Step 4, the force PI controller, as shown in FIG. 2, inputs the given torque T * and the estimated load moment obtained by the load moment estimation moduleAfter a first subtracter, i.e. given torque T * and estimated load moment/>Error/>As input to the force PI controller, the force of the force PI controller controls a given current/>The calculation of (2) is shown in formula (8):
wherein K PT is the scaling factor of the force PI controller; k IT is the integral coefficient of the force PI controller.
The form of equation (8) in a computer discrete system is:
Where k is a certain sampling time and T s is a sampling time.
The given torque T * is compared with the estimated load torqueError of (i.e./>)) Sum force control given current/>Together input into a multi-target particle swarm algorithm module. The force PI controller functions to cause the motor to output a given torque T * to complete the force control.
Step 5, the position control current given calculation module includes a position P controller and a speed PI controller, as shown in figure 3,
Acquiring an actual position theta m of a Permanent Magnet Synchronous Motor (PMSM) by adopting an encoder, and calculating by using a derivative to obtain an actual angular speed omega m＝dθm/dt; given position of inputAnd the actual position theta m is used as the input of a position control current given calculation module after passing through a second subtracter, namely the given position/>Error from actual position θ m/>As an input to the position P controller, a given rotational speed/>, is output via the position P controllerThen the given rotational speed/>Given rotational speed/>, obtained by a third subtracter with the actual angular speed omega m Error from actual angular velocity omega m (i.e./>) As a speed PI controller input, the output of the speed PI controller is the position control given current/>As shown in formula (10):
Wherein K PP is the scaling factor of the position P controller; k PS is the scaling factor of the speed PI controller; k IS is the integral coefficient of the speed PI controller, and has
The form of equation (10) in a computer discrete system is:
Will give the position Error from actual position θ m (i.e./>) And position control given current/>Together input into a multi-target particle swarm algorithm module. The position control current given calculation module is used for enabling the motor to output a given position theta m to complete position control.
And 6, implementing a method flow of the multi-target particle swarm algorithm, as shown in fig. 4. The multi-objective particle swarm algorithm is widely applied to the application fields of function optimization, neural network training, fuzzy system control and other genetic algorithms. It is based on a particle swarm algorithm, which simulates birds in a bird swarm by designing a mass-free particle that has only two properties: speed, which represents the speed of movement, and position, which represents the direction of movement. Each particle independently searches an optimal solution in a search space, marks the optimal solution as a current individual extremum, shares the individual extremum with other particles in the whole particle swarm, finds the optimal individual extremum as a current global extremum of the whole particle swarm, and adjusts the speed and the position of each particle in the particle swarm according to the current individual extremum found by each particle and the current global extremum shared by the whole particle swarm.
The specific steps of the implementation are as follows:
(1) Initializing a particle swarm with a population size of N, namely randomly setting the initial speed and the position of each particle in a defined domain, as shown in a formula (12);
vi∈[vmin,vmax],xi∈[xmin,xmax],i＝1,2,…,N-1,N (12)
Where v i is the velocity of each particle, v min and v max are the domain ranges of velocity, x i is the position of each particle, x min and x max are the domain ranges of position, respectively, and N is the total number of particles.
(2) Based on the given torque T * and the estimated load torqueError of (i.e./>)) Given location/>Error from actual position θ m (i.e./>) Respectively constructing objective functions, which are respectively shown in a formula (13);
Wherein f li is an index for evaluating force control, and f wei is an index for controlling position control.
(3) According to different application requirements, an overall evaluation function is constructed as shown in a formula (14):
Wherein r (r is more than or equal to 0 and less than or equal to 1) is a force control emphasis coefficient, and the larger the force control emphasis coefficient is, the more the force control effect is more excellent.
Calculating the fitness value of each particle through the overall evaluation function, and obtaining a global extremum gBest, namely the minimum fitness value in all the current particles, as shown in a formula (15);
gBest＝min[fi],i＝1,2,…,N-1,N (15)
(4) According to formula (16), calculating an individual extremum pBest i, namely the minimum value of each individual in the past update;
pBesti＝min[f(n)],n＝1,2,…,D-1,D (16)
wherein D is the number of iterations.
(5) After global extremum and individual extremum are obtained through step (3) and step (4), updating the speed and position of each particle according to formula (17);
vi(n)＝vi(n-1)+c1×rand()×(pBesti(n-1)-xi(n-1)) (17)
+c2×rand()×(gBest(n-1)-xi(n-1))
xi(n)＝xi(n-1)+vi(n)
where v i (n) and x i (n) represent the speed and position of each particle of the current iteration, v i (n-1) and x i (n-1) represent the speed and position of each particle of the last iteration, rand () is a random number between 0 and 1, and c 1 and c 2 represent learning factors for the individual itself and for the global whole, respectively.
(6) Evaluating the fitness value according to the termination condition formula (18), outputting an optimal optimization factor a=x i if the termination condition is satisfied, and outputting a force-bit mixture control current setting integrating force control and position control according to the formula (19)
f≤Thr (18)
Wherein Thr is the set overall evaluation function threshold.
The integrated optimization factor a is automatically adjusted through the calculation of the multi-target particle swarm algorithm, and the integrated optimization method has the advantages of simplicity in calculation, high convergence speed, flexibility in parameter adjustment and the like, and can simultaneously realize the integrated optimization of the position control and the force control.
(7) And (3) returning to the step (2) without meeting the termination condition, and re-entering the iteration process until the termination condition is met or the highest iteration number is reached.
(8) After a large number of training, an offline table is generated, so that each torque setting and each position setting can find an optimal integrated optimization factor a, and therefore, the force control and the position control of the motor are both optimal.
Output force-bit hybrid control current settingInput to a fourth subtractor.
Step 7, the principle of the current PI controller module, as shown in fig. 7, includes a quadrature axis current PI controller and a direct axis current PI controller, where the input of the quadrature axis current PI controller is a force-bit mixed control current setForce bit mixed control current given/>, obtained by a fourth subtracter through actual quadrature axis current i q Error with actual quadrature axis current i q (i.e./>) After being calculated by the quadrature axis current PI controller, the output system outputs a given quadrature axis voltage/>Since the system adopts the FOC basic control method of i d =0, the method is/>The input of the direct-axis current PI controller is given direct-axis current/>Given direct axis current/>, obtained by a fifth subtracter from the actual direct axis current i d Error from the actual straight axis current i d (i.e./>) After calculation of the direct-axis current PI controller, a given direct-axis voltage/>, is outputAs shown in equation (20).
Wherein K Piq is the proportionality coefficient of the quadrature axis current PI controller; k Iiq is the integral coefficient of the quadrature axis current PI controller; k Pid is the scaling factor of the direct current PI controller; k Iid is the integral coefficient of the direct current PI controller.
The form of equation (20) in a computer discrete system is:
Output given quadrature axis voltage And given the direct axis voltage/>Together with the input to IPark transform modules.
Steps 8, IPark transform the module principle as shown in fig. 8. The function is to set the given quadrature axis voltage in the synchronous rotation coordinate systemAnd given the direct axis voltage/>Transformed to a given voltage/>, in a stationary two-phase coordinate systemAnd/>IPark the transformation formula is:
given voltage in output two-phase stationary coordinate system And/>Together input into a space vector pulse width modulation module (SVPWM); and theta e is the actual electrical angle, and is obtained by multiplying the actual position theta m of the permanent magnet synchronous motor.
Step 9, space Vector Pulse Width Modulation (SVPWM) principle, as shown in fig. 9. Step 7, voltage given value under static two-phase coordinate systemAnd/>And the obtained six paths of PWM signals are used as input for controlling the three-phase inverter through a Space Vector Pulse Width Modulation (SVPWM) module.
A Space Vector Pulse Width Modulation (SVPWM) module controls the three-phase inverter to output sine wave phase voltage waveforms with 120-degree electrical angles of three phases mutually according to space voltage vector switching, so that the motor stator winding obtains sine wave phase current waveforms with 120-degree electrical angles mutually. The method comprises the steps of calculating the current position of a rotor through the input voltage given value under the static two-phase coordinate system, and correspondingly outputting six PWM signals according to a seven-segment output method, so as to control a three-phase inverter and a motor.
And step 10, the three-phase inverter performs switching action on the six switching tubes according to the six PWM signals input in the step 9, and controls the bus voltage U dc to be input into the permanent magnet synchronous motor so as to realize the driving of the permanent magnet synchronous motor.
The three-phase inverter is a typical two-level three-phase voltage source inverter and consists of three upper and lower bridge arms (six switching devices), six PWM signals output by a Space Vector Pulse Width Modulation (SVPWM) algorithm are respectively applied to the six switching devices, and when a certain upper bridge arm switching device is turned on, bus voltage is applied to a motor stator winding from the upper bridge arm switching device. The bus voltage is regularly applied to the motor through a series of on-off combinations, thereby driving the motor.
Experiment 1:
Semi-physical simulation was performed on a permanent magnet synchronous motor force-bit hybrid control system based on load torque estimation as in example 1. In order to verify the effectiveness of the method, a semi-physical motor test platform of dSPACE of the base Yu Desi Bayesian electromechanical control technology company is designed, which is a semi-physical simulation system capable of rapidly carrying out algorithm research, as shown in figure 10. The platform mainly comprises a Permanent Magnet Synchronous Motor (PMSM), an encoder, a coupler, a magnetic powder brake, a torque and rotation speed sensor, a base, a driver (SD 800), an adapter plate, a dSPACE (DS 1202), a PC upper computer and the like. In the experiment, the given position can be set in the PC upper computer by itself And a given torque T *, after the algorithm steps 1-10 of the embodiment 1 are carried out, a control signal is output to a driver, the driver drives a motor to operate, and an encoder and a torque rotating speed sensor acquire signals such as an actual position theta m, an actual angular speed omega m, an actual load torque T L and the like and feed the signals back to dSPACE for closed-loop control.
The force-bit hybrid control system of the permanent magnet synchronous motor based on load torque estimation of example 1 was subjected to semi-physical simulation under conditions of no load (0 n.m), half load (3 n.m) and full load (6 n.m) for a given torque T * at 5000 pulses, 10000 pulses and 40000 pulses, respectively, and experimental results are shown in table 1 below.
Table 1 experimental data table of force-position hybrid control system of permanent magnet synchronous motor based on load moment estimation
According to experimental data, the force-position hybrid control system of the permanent magnet synchronous motor based on load moment estimation in the embodiment 1 can automatically select the optimal integration optimization factor under the algorithm under different given positions and given torques, and achieves integration and optimization of force and position control. And, under the condition that the emphasis position control is more desired (force emphasis coefficient r=0.1), the position error is minimum in the steady state; under the condition of combining force control and position control (force emphasis coefficient r=0.5), the torque error and the position error are moderate in a steady state; under the condition of more hopeful side gravity control (force emphasis coefficient r=0.9), the torque error is minimum in a steady state, which is beneficial to applying the algorithm under different requirements, and the aim of the invention is achieved.
Finally, it should also be noted that the above list is merely a few specific embodiments of the present invention. Obviously, the invention is not limited to the above embodiments, but many variations are possible. All modifications directly derived or suggested to one skilled in the art from the present disclosure should be considered as being within the scope of the present invention.
Claims (8)
1. The force-position hybrid control system of the permanent magnet synchronous motor based on load moment estimation comprises the permanent magnet synchronous motor and is characterized in that two-phase currents i a and i b of the permanent magnet synchronous motor are used as inputs of a Clarke transformation module, and the actual position theta m of the permanent magnet synchronous motor is used as inputs of a derivative, a multiplier and a second subtracter respectively; given positionFor a preset constant as an input of the second subtracter, the given torque T * is a preset constant as an input of the first subtracter, and the direct current is givenAn input to a fifth subtractor;
the output of the derivative is respectively connected with the input of the third subtracter and the input of the load moment estimation module;
The output of the Clarke transformation module is connected with the input of the Park transformation module, the output of the multiplier is respectively connected with the input of the Park transformation module and the input of the IPark transformation module, and the output of the Park transformation module is respectively connected with the input of the load moment estimation module, the input of the fourth subtracter and the input of the fifth subtracter; the output of the load moment estimation module is connected with the input of the first subtracter;
The output of the second subtracter is respectively connected with the input of the multi-target particle swarm algorithm module and the input of the position P controller; the output of the position P controller is connected with the input of a third subtracter, the output of the third subtracter is connected with the input of a speed PI controller, and the output of the speed PI controller is connected with the input of a multi-target particle swarm algorithm;
The output of the first subtracter is respectively connected with the input of the multi-target particle swarm algorithm module and the input of the force PI controller, and the output of the force PI controller is connected with the input of the multi-target particle swarm algorithm module; the output of the multi-target particle swarm algorithm module is connected with the input of a fourth subtracter;
the output of the current PI controller is connected with the input of a three-phase inverter after passing through a IPark conversion module and a space vector pulse width modulation module in sequence, and the permanent magnet synchronous motor is driven by the three-phase inverter.
2. The method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force-position hybrid control system based on load moment estimation according to claim 1, wherein:
The method comprises the following specific steps:
Step 1, obtaining two-phase currents i a and i b by a method of collecting current signals through two Hall current sensors, and obtaining actual currents i α and i β under a static two-phase coordinate system through a Clarke transformation module to serve as input of a Park transformation module;
Step 2, acquiring an actual position theta m of the permanent magnet synchronous motor through an encoder, acquiring an actual electric angle theta e of the actual position theta m through a multiplier, acquiring actual direct-axis currents i α and i β and an actual electric angle theta e under a static two-phase coordinate system through a Park conversion module, outputting the actual direct-axis current i d to a fifth subtracter, outputting the actual quadrature-axis current i q to a fourth subtracter, and simultaneously outputting the actual direct-axis current i d and the actual quadrature-axis current i q to a load moment estimation module;
Step 3, the actual position theta m passes through a derivative to obtain an actual angular velocity omega m, and then the actual angular velocity omega m is respectively output to a load moment estimation module and a third subtracter, and the load moment estimation module outputs an estimated load moment As an input to a first subtractor;
step 4, given torque T * and estimated load moment After a first subtracter, a given torque T * and an estimated load torque/>, are obtainedThe error of (2) is used as the input of the force PI controller, and is output as the given current/>, of the force control after passing through the force PI controllerThe given torque T * is then combined with the estimated load torque/>Error, force control given current/>All are input into a multi-target particle swarm algorithm module;
Step 5, given position And the actual position theta m is subjected to a second subtracter to obtain a given position/>The error with the actual position theta m is input into a position P controller, and the given rotating speed/>, obtained through the position P controllerGiven rotational speed/>And the actual angular velocity omega m is subjected to a third subtracter to obtain a given rotating speed/>The error from the actual angular velocity omega m is taken as the input of a speed PI controller, the output of which controls the given current/>, for the position
Then the given position is givenError from actual position θ m, position control given current/>Inputting the multiple target particle swarm algorithm modules together;
step 6, the multi-target particle swarm algorithm module inputs the given position according to the input An objective function is constructed with the error of the actual position theta m, the error of the given torque T * and the error of the actual load torque T L, the force control and position control conditions of the current motor are evaluated, an optimal integrated optimization factor a is obtained through a multi-target particle swarm algorithm, and the force is controlled to give current/>, through the optimal integrated optimization factor aAnd position control given current/>Integrating output as force-bit hybrid control current set/>And input to a fourth subtractor;
Step 7, the current PI controller module comprises an quadrature axis current PI controller and a direct axis current PI controller, and the force-bit mixed control current is given The force bit mixed control current given/> is obtained after the actual quadrature axis current i q passes through a fourth subtracterError with actual quadrature axis current i q, then force-bit hybrid control current given/>The error with the actual quadrature axis current i q is calculated by a quadrature axis current PI controller to output a given quadrature axis voltage/>
Given the direct currentObtaining a given direct axis current/>, through a fifth subtracter, with the actual direct axis current i d Error from the actual straight axis current i d, then give the straight axis current/>The error with the actual direct-axis current i d is calculated by the direct-axis current PI controller to output a given direct-axis voltage/>Given quadrature axis voltage/>And given the direct axis voltage/>Together input to IPark transform modules;
Step 8, giving the quadrature axis voltage Given the direct axis voltage/>And the actual electrical angle theta e is converted into a given voltage/>, under a static two-phase coordinate system, through a IPark conversion moduleAnd/>And input to a space vector pulse width modulation module;
step 9, voltage given value under static two-phase coordinate system And/>Six paths of PWM signals are obtained through a space vector pulse width modulation module and used as input for controlling the three-phase inverter;
And step 10, the three-phase inverter performs switching action on the six switching tubes according to the six input PWM signals, and controls the bus voltage U dc to be input into the permanent magnet synchronous motor so as to realize the driving of the permanent magnet synchronous motor.
3. The method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force-position hybrid control system based on load moment estimation according to claim 2, wherein:
the formula of the first subtracter in the step 4 is as follows:
The calculation of the force PI controller is shown in equation (8):
wherein K PT is the scaling factor of the force PI controller; k IT is the integral coefficient of the force PI controller, T L is the actual load moment; the form of equation (8) in a computer discrete system is:
where k is the sampling instant and T s is the sampling time.
4. A method for controlling a permanent magnet synchronous motor by a force-position hybrid control system of a permanent magnet synchronous motor based on load moment estimation according to claim 3, wherein:
the formula of the second subtracter in the step 5 is as follows:
The formula of the third subtracter is as follows:
the speed PI controller is calculated as shown in equation (10):
Wherein K PP is the scaling factor of the position P controller; k PS is the scaling factor of the speed PI controller; k IS is the integral coefficient of the speed PI controller, and has
The form of equation (10) in a computer discrete system is:
5. The method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force-position hybrid control system based on load moment estimation according to claim 4, wherein:
the specific steps of the implementation of the multi-target particle swarm algorithm module in the step6 are as follows:
(1) Initializing a particle swarm with a population size of N;
vi∈[vmin,vmax],xi∈[xmin,xmax],i＝1,2,…,N-1,N (12)
Wherein v i is the velocity of each particle, v min and v max are the domain ranges of velocity, x i is the position of each particle, x min and x max are the domain ranges of position, and N is the total number of particles;
(2) Constructing the objective function as shown in formula (6);
wherein f li is an index for evaluating force control, and f wei is an index for controlling position control;
(3) Constructing a general evaluation function:
wherein r (r is more than or equal to 0 and less than or equal to 1) is a force control emphasis coefficient;
calculating the fitness value of each particle through a formula (14), and obtaining a global extremum as shown in a formula (15);
gBest＝min[fi],i＝1,2,…,N-1,N (15)
(4) Calculating to obtain an individual extremum pBest i;
pBesti＝min[f(n)],n＝1,2,…,D-1,D (16)
wherein D is the iteration number;
(5) Updating the velocity and position of each particle;
xi(n)＝xi(n-1)+vi(n)
Where v i (n) and x i (n) represent the speed and position of each particle of the current iteration, v i (n-1) and x i (n-1) represent the speed and position of each particle of the previous iteration, rand () is a random number between 0 and 1, and c 1 and c 2 represent learning factors for the individual itself and for the global whole, respectively;
(6) Evaluating the fitness value according to the termination condition formula (18), outputting an optimal optimization factor a=x i if the termination condition is satisfied, and outputting a force-bit mixture control current setting integrating force control and position control according to the formula (19)
f≤Thr (18)
Wherein Thr is a set overall evaluation function threshold;
Wherein a (a is more than or equal to 0 and less than or equal to 1) is an integrated optimization factor of force control and position control;
(7) Returning to the step (2) without meeting the termination, and entering the iteration process again until the termination condition is met or the highest iteration times are reached;
(8) After training, an offline table is generated, and each torque setting and position setting can find an optimal integrated optimization factor a, so that the force control and the position control of the motor are both optimal.
6. The method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force-position hybrid control system based on load moment estimation according to claim 5, wherein:
the fourth subtracter in step 7 has the following formula:
The formula of the fifth subtracter is as follows:
The calculation formula of the current PI controller module is as follows:
Wherein K Piq is the proportionality coefficient of the quadrature axis current PI controller; k Iiq is the integral coefficient of the quadrature axis current PI controller; k Pid is the scaling factor of the direct current PI controller; k Iid is the integral coefficient of the direct current PI controller;
the form of equation (20) in a computer discrete system is:
7. The method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force-position hybrid control system based on load moment estimation according to claim 6, wherein:
the implementation steps of the load moment estimation module in the step 3 are as follows:
the electromagnetic torque equation of the permanent magnet synchronous motor is as follows:
Wherein T e is electromagnetic torque, L d is motor direct axis inductance, L q is motor quadrature axis inductance, Is a permanent magnet flux linkage, and P n is the pole pair number of the permanent magnet synchronous motor;
the mechanical equation of motion of the permanent magnet synchronous motor is as follows:
wherein J is the rotational inertia of the motor, and B is the viscosity coefficient;
The selected state variables are:
x＝[ωm TL]T (5)
Wherein, T L is the actual load moment, which is estimated by a moment observer formula (7):
According to the formulas (3) and (4), the state space equations are written as follows:
Wherein, Is the first derivative of the mechanical angular velocity,/>Is the first derivative of the actual load moment;
the load moment observer is then designed as follows:
Wherein, To estimate the first derivative of the mechanical angular velocity,/>To estimate the first derivative of the load moment, L 1 and L 2 are feedback coefficients, respectively.
8. The method for controlling the permanent magnet synchronous motor by the permanent magnet synchronous motor force-position hybrid control system based on load moment estimation according to claim 7, wherein:
The transformation formula of the Clarke transformation module is as follows:
wherein, the phase current i c accords with: i a+ib+ic = 0;
The formula of the multiplier is as follows: θ e＝Pn·θm;
The arithmetic formula of the derivative is as follows: omega m＝dθm/dt;
The Park transformation formula is as follows:
Wherein θ e is the actual electrical angle;
the IPark transform formula is:
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