CN111130408A - Improved Luenberger speed observation method and system - Google Patents
Improved Luenberger speed observation method and system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/0003—Control strategies in general, e.g. linear type, e.g. P, PI, PID, using robust control
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/13—Observer control, e.g. using Luenberger observers or Kalman filters
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02P—CONTROL OR REGULATION OF ELECTRIC MOTORS, ELECTRIC GENERATORS OR DYNAMO-ELECTRIC CONVERTERS; CONTROLLING TRANSFORMERS, REACTORS OR CHOKE COILS
- H02P21/00—Arrangements or methods for the control of electric machines by vector control, e.g. by control of field orientation
- H02P21/14—Estimation or adaptation of machine parameters, e.g. flux, current or voltage
- H02P21/18—Estimation of position or speed
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Abstract
The invention belongs to the technical field of speed estimation of a PMSM (permanent magnet synchronous motor) driving system, and particularly discloses a Luenberger speed observer improved by using a single neuron network. The method provided by the invention can obviously reduce the sensitivity of the Luenberger speed observer to noise, so that the Luenberger speed observer has more practical value. In addition, the method provided by the invention has the advantages of simple structure, small calculated amount and easy realization.
Description
Technical Field
The invention belongs to the technical field of speed estimation of a permanent magnet synchronous motor driving system, and particularly relates to an improved Luenberger speed observer by utilizing a single neuron network.
Background
In recent years, pmsm (permanent Magnet Synchronous motor) has been widely used in the fields of robots, electric vehicles, engineering machinery, household appliances, and the like because of its excellent dynamic performance. i.e. idThe vector control method of 0 is a commonly used control method for a PMSM drive system, which achieves dynamic decoupling of the PMSM and improves torque control performance. This control method requires accurate feedback speed information. Speed sensors are not generally used due to their expensive price and limited response capability, and a common method is to acquire an actual position using an encoder and obtain a feedback speed through a differential operation. However, this method causes a phase lag in the feedback speed, and the differentiation amplifies the noise. Especially at low speeds, the feedback speed calculated by this method will be very erroneous.
Typical PMSM drive systems have encoders that are used for position detection. It is a practical method to construct a velocity observer using the position signal detected by the encoder and then estimate the velocity signal, which can avoid the above problems well. From this idea, the Kalman filter, the sliding mode observer and the Luenberger observer are typical techniques that can be used. The Kalman filter has great advantages in noise suppression, but it is limited in real-time applications because the amount of computation is large and the setting of the covariance matrix is difficult to determine. The sliding mode observer has strong robustness to disturbance, but the sliding mode observer often faces the buffeting problem, and the performance is greatly influenced. Compared with Kalman filters and sliding-mode observers, the Luenberger observer is a speed observer that is often used, and is easier to implement, has a simpler structure and better steady-state performance. However, the literature (G.Ellis, Control System Design Guide,4th ed.Waltham, MA, USA: Butterworth-Heinemann,2012.) indicates that the Luenberger observer is susceptible to noise.
Disclosure of Invention
In view of the problems in the prior art, the invention provides an observation method of a Luenberger speed observer improved by using a single neuron network, which is characterized by comprising the following steps:
1) at the moment k, utilizing an encoder to detect in real time to obtain a feedback position and utilizing a Luenberger observer to observe the position and calculate an observation error;
2) acquiring observed total torque by using the single neuron network designed based on the artificial neural network;
3) calculating an observed angular acceleration using the total torque;
4) calculating an observation rotating speed through integral operation by utilizing the angular acceleration, wherein the observation rotating speed is the estimated speed;
5) carrying out integral operation by using the observation rotating speed to obtain an observation position at the k +1 moment;
6) let time k be k +1, repeat steps 1) to 5), thereby continuously acquiring the observed rotation speed.
Preferably, in the step 1), the observation error at the time k may be calculated as:
wherein, thetamFor the purpose of position feedback, an
To observe the location.
Preferably, in the step 2), based on the single neuron network designed by the artificial neural network, error (k),
the proportionality coefficient is 1, and the input signal is taken as the input signal; taking the observed total torque as the output of the single neuron network;
the output of the designed single neuron network can be expressed as:
where T is the sampling period, KpIs the proportionality coefficient, KiIs the integral coefficient, KdIs the differential coefficient, Kp、Ki、KdUpdated with the supervised Hebb rule,
for total torque observed, electromagnetic torque TeThe updating of (2) is realized by acquiring the electromagnetic torque in real time.
Preferably, the formula for calculating the observation angular acceleration in the step 3) is:
wherein J is the system inertia.
Preferably, in the step 4), the observed rotation speed may be calculated as:
preferably, in the step 5), the formula of the observation position is:
the invention also provides a Luenberger speed observation system improved by using a single neuron network, which is characterized by comprising: the system comprises an encoder, a Luenberger observer, a PMSM (permanent magnet synchronous Motor) driving system, a single neuron network designed based on an artificial neuron network and a model system;
detecting the PMSM driving system in real time by using the encoder to obtain a feedback position; acquiring the observation position of the PMSM driving system by using the Luenberger observer and calculating an observation error;
respectively taking the observation error, the integral of the observation error, the differential of the observation error and a proportionality coefficient as the input of the single neuron network to output the total torque;
and taking the total torque as an input of the model system, and outputting an observed rotating speed, namely the estimated speed.
Compared with the prior art, the invention at least has the following beneficial effects:
(1) the invention provides a Luenberger speed observer based on a single neuron network, which combines a self-adaptive neuron with PID control, and integrates an electromagnetic torque input end of a traditional Luenberger speed observer model system into the single neuron network, thereby integrally replacing a traditional PID compensator and an original electromagnetic torque input end, further directly utilizing the single neuron network to self-adaptively and self-organize to adjust the output of total torque, and further indirectly improving the robustness of noise interference resistance of rotating speed estimation.
(2) The Luenberger speed observer provided by the invention can obviously reduce the sensitivity of the observer to noise, which greatly improves the practicability of the Luenberger speed observer, and in addition, the provided method does not increase the complexity of an observer algorithm, has a simple structure and small calculation amount, and is easy to realize.
Drawings
FIG. 1 is a schematic diagram of a conventional Luenberger speed observer;
FIG. 2 is a structural model of an artificial neuron;
FIG. 3 is a schematic diagram of a Luenberger velocity observer modified with a single neuron network;
FIG. 4 is a comparative experimental result of a conventional Luenberger speed observer and an observer provided by the present invention in the absence of noise;
FIG. 5 is a comparative experimental result of a conventional Luenberger speed observer and an observer provided by the present invention in the presence of noise;
the present invention is described in further detail below. The following examples are merely illustrative of the present invention and do not represent or limit the scope of the claims, which are defined by the claims.
Detailed Description
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
To better illustrate the invention and to facilitate the understanding of the technical solutions thereof, typical but non-limiting examples of the invention are as follows:
the traditional Luenberger speed observer mainly consists of three parts, including a physical system, a model system and a compensator, wherein the compensator is usually realized by using a PID controller, as shown in fig. 1, the principle of the traditional Luenberger speed observer is shown. The physical system intuitively reflects the partial components of the actual PMSM drive system. The model system has two inputs, an electromagnetic torque input and an observed disturbance input, the observed disturbance being derived from the output of the PID compensator. In the model system, the total observed torque can be obtained by adding the electromagnetic torque and the observed disturbance, the quotient of the total torque and the system inertia can be used for obtaining the observed angular acceleration, the observed angular acceleration can be subjected to one-time integral operation to obtain the observed rotating speed, and the observed position can be obtained by performing one-time integral operation. Finally, the deviation between the actual position and the observed position is used as the input of the PID compensator. And extracting the observed rotating speed from the model system, namely the estimated rotating speed. The Luenberger speed observer is easy to realize, has a simple structure, is easy to be influenced by noise, and has great practical significance for solving the technical problem to improve the practicability of the Luenberger speed observer.
The present invention seeks to solveThe defects of the prior art provide an improved Luenberger speed observer by using a single neuron network, firstly, an observation error is calculated by using a feedback position and an observation position, then, the observation error, the integral of the observation error and the differential sum +1 of the observation error are respectively used as the input of the single neuron network, and simultaneously, a proportionality coefficient K is usedpIntegral coefficient KiCoefficient of differentiation KdAnd the actual electromagnetic torque TeRespectively used as corresponding synaptic weights, and then K is subjected to supervised Hebb rulep，KiAnd KdIs updated, and TeThe updating is realized by acquiring the electromagnetic torque in real time, then selecting a direct proportional function with a proportionality coefficient of 1 as an activation function, taking the observed total torque as the output of the single neuron network, then dividing the total torque by the system inertia to obtain an observed angular acceleration, further obtaining an observed rotating speed through integral operation, namely the estimated speed, finally obtaining an observed position through integral operation on the observed rotating speed for one time, and repeating the steps, thereby continuously obtaining the estimated speed.
The method specifically comprises the following steps:
1) calculating an observation error using the feedback position and the observation position
The feedback position is detected in real time by using an encoder, the observation position is obtained by using a Luenberger observer, and the observation error at the time k can be calculated as:
wherein, thetamFor the purpose of position feedback, an
To observe the location.
2) Obtaining observed total torque by using single neuron network based on artificial neural network design
For the PID compensator of the traditional Luenberger observer, the input is the observation error, and the output is the observation disturbance. The specific form of the PID compensator that can be obtained according to the PID control principle is as follows:
wherein the content of the first and second substances,
to observe disturbances; kp，KiAnd K anddrespectively, proportional coefficient, integral coefficient and differential coefficient. In practical applications, PID is usually implemented in a discrete form, where continuous time t is replaced by a series of sampling time points kT, time integral is replaced by numerical integral, and time differential is replaced by first-order backward difference, so as to obtain the following formula:
where T denotes the sampling period. A discrete form of the PID compensator can thus be obtained as:
further, the expression of the conventional observed total torque can be obtained as follows:
the artificial neural network has strong self-adaption and self-learning capabilities and is widely applied to the field of automation. The basic processing unit of an artificial neural network is an artificial neuron, which is a simulation and simplification of a biological neuron. Fig. 2 shows a structural model of an artificial neuron, whose input-output relationship can be expressed as:
wherein: o ismAn output signal representing an artificial neuron; psi (·) is the activation function; w is amnRepresents fromThe synaptic weights from the neuron n to the neuron m are updated by using a specific learning rule; i isn(N-1, 2, …, N) is an input signal to an artificial neuron;
as a threshold, it is an external parameter of neuron m, which can be considered as an input I0Is +1 and has a weight of
The input signal of (1). Therefore, the input-output relationship of the artificial neuron can be re-expressed as:
as a practical artificial neural network, the single neuron network has the advantages of simple algorithm design, easy realization, small calculated amount and strong self-adaption and self-learning capabilities. The single neuron network is integrated in the Luenberger speed observer algorithm, so that the Luenberger speed observer has good self-adaptive capacity, and the robustness of the Luenberger speed observer is improved. As can be seen from the above-mentioned principle of the Luenberger observer, in order to make the rotational speed obtained from the Luenberger speed observer robust to noise, we should first ensure robustness of the observed total torque to noise. The observed total torque consists of the sum of the output of the PID compensator and the actual electromagnetic torque. If the single-neuron network is used for integrally replacing the part and then the single-neuron network is used for adaptively adjusting the output of the total torque, the robustness of the observed total torque to noise is increased, and the robustness of the estimated rotating speed to noise interference is improved.
The invention utilizes the single neuron network to integrally replace the traditional PID compensator and the original electromagnetic torque input end, and integrates the PID control and the electromagnetic torque input end into the single neuron network. Specifically, the calculated observation errors error (k) are first state-converted to obtain error (k) respectively,
and +1, then taking them as the input of the single neuron network, and taking the proportionality coefficient KpIntegral coefficient KiCoefficient of differentiation KdAnd the actual electromagnetic torque TeAnd taking the corresponding synapse weight value, selecting a direct proportional function with a proportionality coefficient of 1 as an activation function (which means that the proportionality coefficient of the neuron is 1), and finally taking the observed total torque as the output of the single neuron network. Fig. 3 is the principle of the Luenberger speed observer modified by the present invention. According to the above expression describing the input-output relationship of the artificial neural network, the output of the designed single neuron network can be expressed as follows:
comparing the above formula with the expression of the traditional observed total torque, it can be known that the original calculation relationship is not changed after the single neuron network is adopted, and the difference is that the improved total torque is directly obtained by the single neuron network in a self-adaptive manner.
In a single neuron network, unsupervised Hebb learning rules, supervised Delta learning rules and supervised Hebb learning rules are common synaptic weight learning algorithms. The supervised Hebb learning rule combines the former two, adopted by the present invention, so Kp，KiAnd KdThe update rule of (a) may be expressed as:
wherein, ηp，ηi，ηdThe learning rates of proportion, integration and differentiation respectively satisfy ηp＞0，ηi＞0，ηd＞0。
Electromagnetic torque TeThe updating of (2) is realized by acquiring the electromagnetic torque in real time. The PMSM drive system specifically adopts idIn the vector control method of 0, the electromagnetic torque is represented by the formula Te＝KTiqComputingIn which K isTAnd iqThe torque coefficient and q-axis current of the motor, respectively. i.e. iqThe three-phase current of the motor is obtained through Clark conversion and Park conversion.
3) Calculating observed angular acceleration using observed torque
The observed angular acceleration can be easily calculated using the total torque obtained in step 2):
wherein J is the system inertia.
4) The observed speed is calculated by integral operation, namely the estimated speed
Since the observation angular acceleration is obtained, only one integral calculation is needed to obtain the observation rotating speed. In practical applications, numerical integration is usually used instead of time integration, and the observed rotation speed can be expressed as:
5) obtaining an observed position by performing an integral operation using an observed rotation speed
Similarly, the observation position can be obtained by performing integral operation on the observation rotating speed:
6) and repeating the steps 1) to 5) so as to continuously acquire the observed rotating speed.
In order to verify the feasibility and the effectiveness of the observer algorithm provided by the invention, a simulation model is specifically built based on MATLAB/SIMULINK. The simulation experiment is carried out by utilizing the parameters of the Huada motor (130ST-M10015 LFB). In SIMULINK, pairs are based on idA PMSM driving system controlled by a vector control method of 0 is modeled, and a speed loop controller and a current loop controller are both realized by adopting PI control. The specification of the simulation model is as follows:
TABLE 1
| Parameter(s) | Value of |
| Pole pair number of PMSM | 4 |
| D-axis and q-axis inductances of PMSM | 3.675mH |
| Coefficient of torque | 1.6667 |
| Stator phase resistor of PMSM | 0.801Ω |
| Inertia of PMSM | 0.002595kg.m2 |
| PI parameter of speed controller | 10,1300 |
| PI parameter of |
1,10 |
In this example, comparative simulations were performed using a conventional Luenberger speed observer and the observer provided by the present invention. First, to determine the parameters of the PID compensator in a conventional Luenberger speed observer, according to the literature (G.Ellis, Control System Design Guide,4th ed.Waltham, MA, USA: Butterworth-Heinemann,2012.) experimental procedures for determining PID compensator parameters to determine a suitable set of compensator parameters, which are identified as Kp＝200，Ki＝1000，Kd10. In addition, a suitable set of parameters is determined experimentally for the observer algorithm provided by the present invention: kp(0)＝500，Ki(0)＝100，Kd(0)＝3，ηp＝0.9，ηi＝0.6，ηd＝0.2。The speed command signal is given at 400 rpm. As can be seen from fig. 4, the experiment of the conventional Luenberger speed observer and the observer provided by the present invention with the determined parameters in the absence of noise can yield almost the same results, and as can be seen from fig. 4, the determined parameters ensure that a good estimated rotational speed can be obtained. Fig. 5 is a comparative experiment performed in the presence of noise, and it can be seen that the observer provided by the present invention significantly reduces the sensitivity of the conventional Luenberger speed observer to noise. Thus, the feasibility and effectiveness of the observer algorithm provided by the invention are verified.
The Luenberger speed observer improved by the invention can obtain a good speed estimation result in an application environment with noise. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The preferred embodiments of the present invention have been described in detail, however, the present invention is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present invention within the technical idea of the present invention, and these simple modifications are within the protective scope of the present invention.
It should be noted that the various technical features described in the above embodiments can be combined in any suitable manner without contradiction, and the invention is not described in any way for the possible combinations in order to avoid unnecessary repetition.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as the disclosure of the present invention as long as it does not depart from the spirit of the present invention.
Claims (7)
1. A method for improved Luenberger velocity observation using a single neuron network, the method comprising the steps of:
1) at the moment k, utilizing an encoder to detect in real time to obtain a feedback position and utilizing a Luenberger observer to observe the position and calculate an observation error;
2) acquiring observed total torque by using the single neuron network designed based on the artificial neural network;
3) calculating an observed angular acceleration using the total torque;
4) calculating an observation rotating speed through integral operation by utilizing the angular acceleration, wherein the observation rotating speed is the estimated speed;
5) carrying out integral operation by utilizing the observation rotating speed to obtain the observation position at the k +1 moment
6) Let time k be k +1, repeat steps 1) to 5), thereby continuously acquiring the observed rotation speed.
2. Observation method according to claim 1, characterized in that: in step 1), the observation error at time k may be calculated as:
wherein, thetamFor the purpose of position feedback, an
To observe the location.
3. Observation method according to claim 1, characterized in that: in the step 2), based on the single neuron network designed by the artificial neural network, error (k),
the proportionality coefficient is 1 as input signal; taking the observed total torque as the output of the single neuron network;
the output of the designed single neuron network can be expressed as:
where T is the sampling period, KpIs the proportionality coefficient, KiIs the integral coefficient, KdIs the differential coefficient, Kp、Ki、KdUpdated with the supervised Hebb rule,
for total torque observed, electromagnetic torque TeThe updating of (2) is realized by acquiring the electromagnetic torque in real time.
4. Observation method according to claim 1, characterized in that: the formula for calculating the observation angular acceleration in the step 3) is:
wherein J is the system inertia.
5. Observation method according to claim 1, characterized in that: in the step 4), the observed rotation speed may be calculated as:
6. observation method according to claim 1, characterized in that: in the step 5), the formula of the observation position is:
7. a Luenberger velocity observation system improved using a single neuron network, the system comprising: the system comprises an encoder, a Luenberger observer, a PMSM (permanent magnet synchronous Motor) driving system, a single neuron network designed based on an artificial neuron network and a model system;
detecting the PMSM driving system in real time by using the encoder to obtain a feedback position; acquiring the observation position of the PMSM driving system by using the Luenberger observer and calculating an observation error;
respectively taking the observation error, the integral of the observation error, the differential of the observation error and a proportionality coefficient as the input of the single neuron network to output the total torque;
and taking the total torque as an input of the model system, and outputting an observed rotating speed, namely the estimated speed.
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