CN112198888A - Self-adaptive PID control method considering autonomous take-off and landing of unmanned aerial vehicle on motor-driven platform - Google Patents
Self-adaptive PID control method considering autonomous take-off and landing of unmanned aerial vehicle on motor-driven platform Download PDFInfo
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Abstract
The invention discloses a self-adaptive PID control method and a self-adaptive PID control system considering autonomous take-off and landing of an unmanned aerial vehicle on a motor platform, wherein the method comprises the steps of obtaining ideal PID parameters of a multi-rotor unmanned aerial vehicle under different working environments; initializing a PID value according to the working environment of the multi-rotor unmanned aerial vehicle, and setting a PID controller to be in an automatic mode; and correcting the PID parameters according to a fuzzy self-adaptive control algorithm. According to the control method, the control deviation and the deviation change rate of the attitude of the multi-rotor unmanned aerial vehicle are observed, the compensation quantity of the PID in the current state is solved in real time by using a fuzzy self-adaptive control algorithm, the PID parameters are compensated, and the value is assigned again for flight control, so that the interference caused by the change of the flight environment and the change of the dynamic characteristics of the aircraft to a control system is adapted, the robustness of the control system is improved, and the performance of a PID controller and the safety of the whole multi-rotor unmanned aerial vehicle are ensured.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle control, in particular to a self-adaptive PID control method considering autonomous take-off and landing of an unmanned aerial vehicle on a motor platform.
Background
The PID control algorithm is a typical linear feedback control algorithm based on deviation, has a simple structure, shows a good control effect, and is widely used in the field of industrial control. Generally, a complete PID control algorithm includes three correction links of proportion, integral and differential, and effective adjustment of a controlled variable is realized by linear superposition, and parameters to be set include a proportional coefficient, an integral coefficient and a differential coefficient.
The multi-rotor unmanned aerial vehicle is a multi-channel input and six-degree-of-freedom underactuated system, a flight controller of the multi-rotor unmanned aerial vehicle mostly adopts a Pixhawk4 product, a primary firmware of the Pixhawk adopts a cascade PID algorithm to realize stable and accurate control on the attitude and the position of the multi-rotor unmanned aerial vehicle, and the steady state and the dynamic characteristics of a control system are improved by performing proportional integral differential regulation on deviation of attitude angular motion information (angle and angular velocity) or flight position motion information (position and velocity), so that the control system achieves good control effects, such as quick response, no overshoot, disturbance resistance and the like.
Although the PID algorithm is a deviation-based control algorithm, in practical engineering application, parameters of the PID algorithm are set by a manual experimental debugging method, so that a design result is not a globally optimal design, which also becomes a factor restricting the actual control efficiency and control accuracy of the algorithm. The PID parameters of the multi-rotor unmanned aerial vehicle are generally determined by a manual parameter adjusting method, extremely high response speed and zero static difference effect are difficult to achieve, and the method is slightly insufficient compared with time-varying parameters with self-adaptive capacity.
On the other hand, the conventional PID control algorithm is not changed once the controller parameters are set, and is reset only when the parameters are modified next time, and for the multi-rotor aircraft, the ground station cannot be changed in the flight process after the PID parameters of each channel are set.
When the multi-rotor unmanned aerial vehicle executes an actual flight task, the process and the environment are often nonlinear and time-varying, and especially when the autonomous take-off and landing task on a maneuvering platform is completed, the influence of maneuvering characteristics of a target platform, ground effect and aerodynamic interference on the ground is received, and single and unchangeable PID controller parameters are hardly suitable for various complex working conditions, so that the multi-rotor unmanned aerial vehicle has poor performances of oscillation or insufficient response and the like in the autonomous take-off and landing process on the maneuvering platform. Therefore, in this case, it is difficult for the conventional PID control algorithm to meet the requirements of engineering practice.
Disclosure of Invention
In order to overcome the problems, the inventor of the invention carries out intensive research and designs an adaptive PID control method and system considering autonomous taking-off and landing of the unmanned aerial vehicle on a maneuvering platform, wherein the method comprises the steps of obtaining ideal PID parameters of the multi-rotor unmanned aerial vehicle under different working environments; initializing a PID value according to the working environment of the multi-rotor unmanned aerial vehicle, and setting a PID controller to be in an automatic mode; and correcting the PID parameters according to a fuzzy self-adaptive control algorithm. The control method of the invention utilizes a fuzzy control algorithm to calculate the compensation quantity of PID in real time under the current state by observing the control deviation and the deviation change rate of the attitude of the multi-rotor unmanned aerial vehicle, compensates the PID parameters, and assigns values to flight control again so as to adapt to the interference brought to a control system by the change of the flight environment and the change of the dynamic characteristics of the aircraft, thereby improving the robustness of the control system, ensuring the performance of a PID controller and the safety of the whole multi-rotor unmanned aerial vehicle and further completing the invention.
The invention aims to provide a self-adaptive PID control method considering autonomous take-off and landing of an unmanned aerial vehicle on a maneuvering platform, which comprises the following steps:
and 3, compensating the PID parameters in real time according to a fuzzy self-adaptive control algorithm.
In the step 1, a dynamic model is established according to historical flight data of the multi-rotor unmanned aerial vehicle, and ideal PID parameters of the multi-rotor unmanned aerial vehicle in different working environments are calculated by adopting a frequency domain analysis method.
In step 2, according to the working environment of the multi-rotor unmanned aerial vehicle, calling the PID parameter result in the step 1, carrying out initialization assignment on the PID parameter before the multi-rotor unmanned aerial vehicle flies, and recording the initial values as KP0、KI0、KD0。
In step 3, the fuzzy self-adaptive control algorithm comprises the step of completing PID parameter K on line according to attitude deviation e and change rate ec of the multi-rotor unmanned aerial vehicle observed in real timeP、KIAnd KDThe PID parameters are compensated in real time and assigned to the PID controller.
The deviation e is the deviation of the attitude angle and the angular speed of the multi-rotor unmanned aerial vehicle observed in real time and the expected attitude angle and the angular speed thereof, and the deviation e and the change rate thereof are subjected to a fuzzy adaptive control algorithm to obtain a PID parameter compensation value delta KP、ΔKIAnd Δ KDWill Δ KP、ΔKIAnd Δ KDSuperimposed to the PID initial value KP0、KI0、KD0And finally, reassigning the PID parameters.
After the deviation e and the change rate ec thereof are subjected to the proportional scaling of ke and kec, the direction and the magnitude of PID parameter self-adaptive adjustment are obtained through a fuzzy control rule, and the PID parameter compensation value delta K is obtained after the proportional scaling of kuP、ΔKIAnd Δ KDPreferably, the fuzzy control rule is defined by a compensation value Δ KP、ΔKIAnd Δ KDAnd establishing a relation between the system deviation e and the deviation change rate ec.
The control method further comprises the following steps: and (5) repeating the step (3) and carrying out next round of judgment and correction on the PID parameters.
An adaptive PID control system, the system comprising:
an initial parameter setting module for setting an initial value K of the PID parameterP0，KI0And KD0；
The parameter acquisition module is used for observing the attitude of the multi-rotor unmanned aerial vehicle in real time;
the deviation calculation module is used for calculating the deviation e and the change rate ec thereof between the attitude of the multi-rotor unmanned aerial vehicle observed in real time and the expected attitude, and the deviation e and the change rate ec thereof form an input variable;
a fuzzy processing module for obtaining PID parameter compensation value delta K according to a fuzzy control algorithmP、ΔKIAnd Δ KD；
And the correction module is used for correcting the PID initial parameter by using the compensation value to obtain a PID setting parameter and updating the PID parameter.
In a module processing module, converting the input variable into a fuzzy set, inquiring a fuzzy rule according to the fuzzy set to obtain the direction and the size of PID parameter setting, and obtaining an output variable PID parameter compensation value delta K through a defuzzification processP、ΔKIAnd Δ KD。
The membership function of the input variable is a Gaussian membership function, and the membership function of the output variable is a triangular membership function.
The invention has the following beneficial effects:
(1) the self-adaptive PID control method provided by the invention is used for controlling the flight attitude of the unmanned aerial vehicle, so as to control the unmanned aerial vehicle to take off and land autonomously on a motor platform, solve the problem of instability of the multi-rotor aircraft when the traditional PID controller is subjected to huge environmental changes or ground effect and gust disturbance after the parameters are given, and improve the flight reliability and safety;
(2) the self-adaptive PID control method provided by the invention enables the response of a control system of the multi-rotor unmanned aerial vehicle to be more ideal, enables the response of an attitude angle under the excitation action to be smoother through the optimization compensation of the PID initial value, reduces the oscillation in the dynamic process, almost has no overshoot, and has higher control precision;
(3) in the method provided by the invention, when the sudden disturbance or complex environment is faced, the response speed of the self-adaptive PID control is faster than that of the traditional PID control, and the PID parameters can be redesigned to recover the attitude of the multi-rotor unmanned aerial vehicle, so that the stability and stability margin of the flight control system are improved to a certain degree;
(4) the method and the system provided by the invention can enable the multi-rotor unmanned aerial vehicle to adapt to the change of the flight environment and the interference brought to the flight control system by the change of the dynamic characteristics of the aircraft, thereby improving the robustness of the flight control system and ensuring the performance of a PID controller and the safety of the whole multi-rotor unmanned aerial vehicle.
Drawings
Fig. 1 shows a schematic flow chart of an adaptive PID control method for autonomous take-off and landing on a mobile platform considering a multi-rotor drone according to a preferred embodiment of the present invention;
FIG. 2 is a schematic diagram illustrating a fuzzy adaptive control algorithm in accordance with a preferred embodiment of the present invention;
FIG. 3 shows a schematic diagram of the internal structure of the fuzzy adaptive PID control of the preferred embodiment of the present invention;
FIG. 4 shows Δ K in the fuzzy adaptive PID control algorithm in the preferred embodiment of the present inventionPAn output surface on the domain of discourse;
FIG. 5 shows a preferred embodiment of the inventionDelta K in fuzzy self-adaptive PID control algorithm in mode of executionIAn output surface on the domain of discourse;
FIG. 6 shows Δ K in the fuzzy adaptive PID control algorithm in the preferred embodiment of the present inventionDAn output surface on the domain of discourse;
FIG. 7 shows a Simulink simulation block diagram constructed in embodiment 1 of the present invention;
fig. 8 shows a comparison diagram of pitch angle output before and after PID adaptation when the attitude control of the quad-rotor unmanned aerial vehicle obtained in embodiment 1 of the present invention has a static error and the response speed is too slow;
fig. 9 shows a comparison diagram of pitch angle output before and after PID adaptation when oscillation occurs in attitude control of a quad-rotor unmanned aerial vehicle obtained in embodiment 1 of the present invention.
Detailed Description
The invention is explained in more detail below with reference to the drawings and preferred embodiments. The features and advantages of the present invention will become more apparent from the description.
According to the invention, an adaptive PID control method considering autonomous take-off and landing of an unmanned aerial vehicle on a motor-driven platform comprises the following steps:
and 3, compensating the PID parameters in real time according to a fuzzy self-adaptive control algorithm.
In the invention, when the multi-rotor unmanned aerial vehicle executes an actual task, the process and the environment are usually nonlinear and time-varying, and particularly when an autonomous take-off and landing task on a maneuvering platform is completed, the multi-rotor unmanned aerial vehicle is influenced by maneuvering characteristics of a target platform, ground effects and aerodynamic interference on the ground, and a single and unchangeable PID parameter is not suitable for various complex working conditions, so that the multi-rotor unmanned aerial vehicle has poor performances such as oscillation or insufficient response in the autonomous take-off and landing process on the maneuvering platform, and therefore, the PID parameter needs to be properly adjusted according to the specific working environment of the multi-rotor unmanned aerial vehicle.
According to the method, in step 1, a dynamic model of the multi-rotor unmanned aerial vehicle is established according to historical flight data or flight logs of the multi-rotor unmanned aerial vehicle, ideal PID parameters of the multi-rotor unmanned aerial vehicle in different working environments are solved by adopting a frequency domain analysis method, and therefore initial assignment of the PID parameters of the multi-rotor unmanned aerial vehicle before flight is determined.
According to the invention, the PID parameters comprise a proportionality coefficient K of the attitude angle ring and a proportionality coefficient K of the attitude angle rate ringPIntegral coefficient KIAnd a differential coefficient KD。
In the invention, the idea of designing an ideal PID parameter by a frequency domain analysis method is mainly based on the stability criterion of a control system after the PID correction link and a controlled object act together under the frequency domain representation, and if the system meets the stability requirement, the amplitude-frequency characteristic | G of the PID correction link is neededc(jωc) L and phase frequency characteristic < Gc(jωc) Satisfying the following formula (1):
in the formula (1, | G0(jωc) I and angle G0(jωc) Respectively representing the passing frequency omega of the transfer function of the controlled object at designcThe amplitude-frequency characteristic and the phase-frequency characteristic are shown, gamma represents the phase margin designed by the whole control system, and is usually 30-60 degrees.
Due to the fact that a PID correction link is at the designed crossing frequency omegacCan be represented in the form of the following formula (2):
by combining the two formulas, the theoretical analytic expression of three parameters of the PID controller can be obtained by solving, and the formula is (3):
therefore, the design index omega can be obtained according to historical flight data in the inventioncAnd gamma, and then combining the dynamic model of the multiple rotors to obtain ideal PID parameters according to the design calculation process.
According to a preferred embodiment of the invention, in step 1, a dynamic model of a pitch channel of a multi-rotor unmanned aerial vehicle of a certain type is established as shown in a transfer function in formula (4), wherein
For the pitch angle rate output of a multi-rotor drone,
is the input variable to the controller, i.e., the desired pitch rate.
According to the method, in step 2, according to different working environments of the multi-rotor unmanned aerial vehicle, a setting result suitable for the flight environment of the multi-rotor unmanned aerial vehicle is selected, the PID parameter design result in step 1 is called, namely PID parameters are initialized, the PID parameters before the multi-rotor unmanned aerial vehicle flies are subjected to initialized assignment, and the initial values of the PID parameters are respectively marked as KP0、KI0、KD0. And meanwhile, the PID controller is placed in an automatic mode, so that the multi-rotor unmanned aerial vehicle can carry out self-adaptive control.
According to the method and the device, the initial assignment of the PID parameters is determined according to the flight environment of the multi-rotor unmanned aerial vehicle, so that the interference of the flight environment on a flight control system is reduced before the multi-rotor unmanned aerial vehicle flies.
According to the preferred embodiment of the invention, the initial assignment setting of the PID parameters in the attitude controller of a multi-rotor unmanned aerial vehicle of a certain type is shown in table 1, for the autonomous taking off and landing process of a maneuvering platform in a conventional pneumatic environment.
TABLE 1
| Control channel | Angle ring K | Angular rate ring KP | Angular rate ring KI | Angular rate ring KD |
| Rolling channel | 4 | 0.1 | 0.09 | 0.005 |
| Pitching channel | 4 | 0.1 | 0.089 | 0.005 |
| Yaw channel | 3 | 0.18 | 0.016 | 0 |
According to the invention, in step 3, the PID parameters are compensated in real time according to a fuzzy self-adaptive control algorithm.
According to the invention, in step 3, in the process of completing autonomous landing of the multi-rotor unmanned aerial vehicle, the attitude (including attitude angle and angular rate) control deviation e and the change rate ec of the multi-rotor unmanned aerial vehicle are observed in real time, the compensation quantity of the current PID parameter is obtained according to the fuzzy control rule, the PID parameter is re-set and then assigned to flight control, the flight control of the multi-rotor unmanned aerial vehicle is realized, the attitude of the multi-rotor unmanned aerial vehicle is recovered stably, and the flight reliability and safety of the multi-rotor unmanned aerial vehicle are improved.
According to the invention, the attitude of the multi-rotor drone comprises attitude angles and angular rates, in particular roll angles, pitch angles, yaw angles, roll angular rates, pitch angular rates and yaw angular rates, the deviation e is the difference between the attitude value of the multi-rotor drone observed in real time and the expected attitude value output by the guidance system, the rate of change ec of the deviation is the differential of the deviation, the derivative of the deviation e with respect to time t is used to obtain the derivative
According to the invention, in step 3, aiming at the pitching channel of the multi-rotor unmanned aerial vehicle, the roll, pitch and yaw angles and the angular rates of the multi-rotor unmanned aerial vehicle are observed in real time according to an inertial measurement unit (such as a gyroscope and an accelerometer) carried in flight control, and then the difference is made between the rolling angle, the pitch angle, the yaw angle and the angular rates of the multi-rotor unmanned aerial vehicle and the expected attitude output by a guidance system to obtain an attitude control deviation e and a deviation change rate ec, and a PID parameter K is completed on line according to a fuzzy selfP、KIAnd KDAnd automatically setting and automatically assigning values to flight control. According to the invention, the deviation e and the change rate ec thereof are processed by a fuzzy self-adaptive control algorithm to obtain a PID parameter compensation value delta KP、ΔKIAnd Δ KDWill Δ KP、ΔKIAnd Δ KDSuperimposed to the PID initial value KP0、KI0、KD0Above, the PID parameters are reassigned, preferably with the deviation e and its rate of change ec as input variables, to compensate the value Δ KP、ΔKIAnd Δ KDIs an output variable.
According to the invention, the fuzzy adaptive PID control algorithm comprises:
for the pitching channel of the multi-rotor unmanned aerial vehicle, fuzzy self-adaptive PID control calculation is carried outThe method adopts a two-input three-output form, takes the deviation e and the deviation change rate ec as input, and takes the compensation quantity delta K of three parameters of PIDP、ΔKIAnd Δ KDIs the output.
According to the invention, after the deviation e and the change rate ec thereof are subjected to the proportional scaling of ke and kec, the direction and the magnitude of PID parameter self-adaptive adjustment are obtained through a fuzzy control rule, and the PID parameter compensation value delta K is obtained after the proportional scaling of kuP、ΔKIAnd Δ KDPreferably, the fuzzy control rule is defined by a compensation value Δ KP、ΔKIAnd Δ KDAnd establishing a relation between the system deviation e and the deviation change rate ec.
According to the preferred embodiment of the invention, the input variable deviation e and the change rate ec thereof are converted into a fuzzy set, the input variable in the fuzzy set is used for inquiring the fuzzy rule to obtain the PID compensation value delta K in the fuzzy set theory domainP、ΔKIAnd Δ KDThe fuzzy value of the compensation value in the fuzzy set discourse domain is subjected to deblurring processing to obtain a compensation value delta KP、ΔKIAnd Δ KDThe obtained compensation value delta KP、ΔKIAnd Δ KDThe algebraic value is superposed on the PID parameter initial value to obtain a PID setting parameter, so that the purpose of updating the PID is achieved, and the PID controller is self-set according to the PID initial value, so that the PID controller can control the controlled object more accurately.
According to the invention, the fuzzy rule is a fuzzy control rule table comprising an input variable deviation e and its rate of change ec and an output variable compensation value Δ KP、ΔKIAnd Δ KDThe corresponding relation between them.
According to the invention, in step 3, as shown in fig. 2, u (t) represents the expected values of attitude angles or angular rates (roll, pitch, yaw), and y (t) represents the corresponding output variable, i.e. the true attitude angle or angular rate of the multi-rotor drone after response.
Obtaining a compensation value delta K of a PID parameter after fuzzy self-adaptive control of the attitude control deviation e and the deviation change rate ecP、ΔKIAnd Δ KDEstablishing a compensation value delta K of the PID parameterP、ΔKIAnd Δ KDRegarding the nonlinear functional relation between the system deviation e and the deviation change rate ec, the observation quantity is fuzzified and substituted into a fuzzy rule table to find the direction and the size of PID parameter setting given by corresponding expert experience, then a fuzzy value is converted into a algebraic value through a fuzzy solution process and is superposed into an initial PID value to obtain a new control parameter, therefore, the PID parameter can realize the table look-up and calculation of a fuzzy logic rule on line in the designed fuzzy control process to realize the on-line self-setting, a PID controller outputs the control quantity to a controlled object such as a multi-rotor unmanned aerial vehicle, the controlled object realizes the regulation and control of the flight attitude according to a flight control instruction, outputs the real attitude angle or angular rate after response, and the cycle is repeated to correct the PID parameter to realize the self-adaptation.
According to the invention, fuzzy sets of five variables of input and output are set to be { NB, NM, NS, ZO, PS, PM, PB }, fuzzy subsets are [ NB ], [ NM ], [ NS ], [ ZO ], [ PS ], [ PM ], [ PB ], each element in the set sequentially represents negative large, negative medium, negative small, zero, positive small, positive medium and positive large, the domain of a deviation variable e is defined as [ -6, 6], and the domains of the other 4 variables are defined as [0, 1 ].
According to a preferred embodiment of the invention, for membership functions with 5 variables in the algorithm, input and output, the membership functions for input variables e and ec are of gaussian type (gausssf) and the output variable Δ KP、ΔKIAnd Δ KDThe membership function of (c) selects a trigonometric form (trimf) such that each variable forms a continuous functional relationship over the corresponding domain of discourse.
In view of magnitude and improved controllability of the fuzzy adaptive controller, scale factors-ke, ke and ku are introduced at its input and output, respectively, according to the present invention, as shown in fig. 3, which is a more detailed schematic diagram of the internal structure of fig. 2 including the fuzzy adaptive controller and the input and output of the PID controller. After the deviation e and the deviation change rate ec of the control system are subjected to the scaling of ke and kec, the direction and the size of parameter self-adaption adjustment are found through a fuzzy control rule, and then the deviation e and the deviation change rate ec are subjected to kuAfter scaling, the compensation value delta K is superposed on the original PID parameterP、ΔKIAnd Δ KDSuperimposed to the PID initial value KP0，KI0，KD0Therefore, a new control parameter is obtained, and the purpose of updating the PID is achieved.
In the invention, the performance of the PID controller can be effectively improved by reasonably selecting the sizes of three factors of ke, kec and ku, the control action on deviation can be enhanced by properly increasing ke, the rise time is shortened, but overshoot is increased and the regulation time is prolonged; the kec is properly increased to strengthen the inhibition effect on deviation change and reduce the overshoot of the system, but the response speed is also slowed down; for ku, too little selection lengthens the dynamic response process, and too much selection causes oscillation of the system.
According to the invention, when the PID parameters are designed by combining scientific research and expert experience:
a. when the deviation e is large, a large K is required to increase the response speed of the system and to prevent the control action from exceeding the allowable range due to differential supersaturation which may be caused by instantaneous increase of the deviation in the initial stagePAnd a smaller KD. In addition, to prevent integral saturation, avoid large overshoot, KIThe value is small, usually 0.
b. When the deviation e and the change rate ec are of medium size, K is used to reduce the overshoot of the system response and ensure a certain response speedPShould be taken smaller. In this case KDThe value of (A) has a large influence on the system, should be smaller, KIThe value of (A) is appropriate.
c. When the deviation e is small, K should be increased for better steady-state performance of the systemP、KIThe value of K should be chosen appropriately to avoid oscillation of the output response around the set value, and to take into account the interference rejection of the systemDThe principle is as follows: when the rate of change of deviation is small, KDTaking the larger part; when the rate of change of deviation is large, KDTake smaller values, usually of medium size.
According to the invention, Δ K is establishedP、ΔKIAnd Δ KDThe fuzzy control rule tables are shown in tables 2-4 respectively, 49 fuzzy control rules can be obtained by combining logical judgment relations in the three tables, and the fuzzy control rule tables are suitable for fuzzy self-adaptive PID control systems including attitude and position control of multi-rotor unmanned aerial vehicles.
FIGS. 4-6 are the fuzzy adaptive PID control algorithm Δ K in the present invention, respectivelyP、ΔKIAnd Δ KDAnd (3) outputting a curved surface on the domain of discourse, wherein two independent variables in the horizontal plane are deviation e and deviation change rate ec respectively, and each point on the curved surface is a compensation quantity delta K of a PID parameter generated according to a fuzzy control ruleP、ΔKIAnd Δ KD。
When the multi-rotor unmanned aerial vehicle faces a complex environment, the deviation control signal can find a corresponding setting relation in the fuzzy rule table, so that the compensation quantity of three parameters of PID is solved and is output to a flight control system of the multi-rotor unmanned aerial vehicle, and corresponding adjustment is carried out on the current environment change or disturbance.
TABLE 2
TABLE 3
TABLE 4
According to the method, after one round of parameter updating is completed, the step 3 is repeated at intervals of time, for example, every 5-10 minutes, the flight attitude of the multi-rotor unmanned aerial vehicle is observed, and the next round of judgment and parameter correction are performed on the PID parameters.
The invention also provides a self-adaptive PID control system, preferably a self-adaptive PID control system considering the autonomous take-off and landing of the unmanned aerial vehicle on a motor platform, the system comprises:
an initial parameter setting module for setting an initial value K of the PID parameterP0，KI0And KD0；
The parameter acquisition module is used for observing the attitude of the multi-rotor unmanned aerial vehicle in real time;
the deviation calculation module is used for calculating the deviation e and the change rate ec thereof between the attitude of the multi-rotor unmanned aerial vehicle observed in real time and the expected attitude, and the deviation e and the change rate ec thereof form an input variable;
a fuzzy processing module for obtaining PID parameter compensation value delta K according to a fuzzy control algorithmP、ΔKIAnd Δ KD；
And the correction module is used for correcting the PID initial parameter by using the compensation value to obtain a PID setting parameter and updating the PID parameter.
According to the invention, in a module processing module, the input variable deviation e and the change rate ec thereof are converted into a fuzzy set, the fuzzy rule is inquired according to the fuzzy set to obtain the direction and the size of PID parameter setting, and then the output variable PID parameter compensation value delta K is obtained through the process of deblurringP、ΔKIAnd Δ KD。
According to the invention, the membership function of the input variable is a gaussian membership function and the membership function of the output variable is a triangular membership function.
The self-adaptive PID control method for the multi-rotor unmanned aerial vehicle solves the problem of instability of the multi-rotor unmanned aerial vehicle when the environment is greatly changed or gust disturbance is encountered after the parameters of the traditional PID controller are given, and improves the flight reliability and safety of the multi-rotor unmanned aerial vehicle; by optimizing and compensating the PID initial value, the response of the attitude angle under the excitation action is smoother, the oscillation in the dynamic process is reduced, almost no overshoot exists, and the control precision is higher; when the system is in a sudden disturbance or complex environment, the response speed of the adaptive PID control is higher, and the PID parameters can be redesigned, so that the stability and stability margin of the flight control system are improved to a certain extent; the change that can make many rotor unmanned aerial vehicle adapt to flight environment and the interference that aircraft self dynamic characteristic's change brought for flight control system improve flight control system's robustness, guarantee the performance of PID controller and the safety of whole many rotor machines.
Examples
Example 1
The transfer function of the yaw channel dynamic model of the quad-rotor unmanned aerial vehicle in the hovering mode obtained by the system identification method is
The specific structure of the simulation experiment system constructed in the MATLAB/Simulink environment is shown in FIG. 7.
The input of a control system in a simulation experiment is set as a unit step signal, the unit step signal is used as a pitch angle control instruction, a delay model (a first-order inertia link) of a fuselage pneumatic model and a motor is considered in the aspect of a dynamic model of the quad-rotor unmanned aerial vehicle, and pitch angle speed information in a feedback loop are considered to be obtained without errors, so that unit feedback is adopted, namely the influence of errors, delay and filtering measured by a sensor is ignored.
A group of PID parameters with relatively common effects are initially given, so that the problems of too low response speed, static difference not being 0, output oscillation and the like exist, the problem of system control performance caused by flight environment change or dynamic model change when the quad-rotor unmanned aerial vehicle autonomously takes off and lands on a maneuvering platform is simulated, and then the PID parameters are self-tuned through a fuzzy control algorithm, so that model output results under the control of the PID parameters before and after the self-tuning are observed.
Parameters in the fuzzy adaptive PID algorithm are set as follows: the two problems are simulated by MATLAB/Simulink software, and the simulation time is set to be 2s because the attitude control of the multi-rotor unmanned aerial vehicle requires a high response speed.
For the control system with the static error and the response speed too low, the obtained fuzzyA comparison graph of the output pitch angle results before and after the adaptive PID control method is set is shown in fig. 8, wherein the PID parameters after the adaptive control method are K10 and KP＝1.02、K I5 and KD＝0.02。
It can be seen from fig. 8 that the system output response time before setting is 0.71s, which tends to be stable around 1.8s, but a static error of 15% exists, and both dynamic and static performances are not ideal. The output response time of the control system after the PID parameter self-adaptation is 0.09s, the control system can fully respond within about 0.6s, the requirement on the rapidity of the attitude loop is met, the static error is avoided, the control signal can be well followed, and the stability is good.
For the oscillation phenomenon of the control system output, fig. 9 shows the comparison of the output pitch angle results before and after the fuzzy adaptive PID control algorithm tuning, where K is 9.2 and K isP＝1.2、KI4.1 and KD＝0.03。
It can be seen from fig. 9 that the original system oscillates without adjusting PID parameters, and not only has static errors, but also the whole process approaching to the steady state does not meet the requirement of attitude control of the multi-rotor unmanned aerial vehicle. The output response time of the control system after PID parameter self-adaptation is 0.111s, the control system can quickly reach a steady state in about 0.6s, the response speed is fast enough, system oscillation is successfully eliminated, static errors do not exist, and the follow-up and response of control signals are good.
By combining the simulation experiment results, the fuzzy self-adaptive PID-based multi-rotor unmanned aerial vehicle control method provided by the invention has the advantages of high response speed, no static error, capability of eliminating system oscillation, capability of meeting the task requirement of independent take-off and landing on a motor platform and capability of improving the flight reliability and safety of the multi-rotor unmanned aerial vehicle.
The invention has been described in detail with reference to the preferred embodiments and illustrative examples. It should be noted, however, that these specific embodiments are only illustrative of the present invention and do not limit the scope of the present invention in any way. Various modifications, equivalent substitutions and alterations can be made to the technical content and embodiments of the present invention without departing from the spirit and scope of the present invention, and these are within the scope of the present invention. The scope of the invention is defined by the appended claims.
Claims (10)
1. An adaptive PID control method considering autonomous take-off and landing of an unmanned aerial vehicle on a motor-driven platform is characterized by comprising the following steps:
step 1, obtaining ideal PID parameters of a multi-rotor unmanned aerial vehicle in different working environments;
step 2, initializing a PID value according to the working environment of the multi-rotor unmanned aerial vehicle, and setting a PID controller to be in an automatic mode;
and 3, compensating the PID parameters in real time according to a fuzzy self-adaptive control algorithm.
2. The control method according to claim 1, wherein in step 1, a dynamic model is established according to historical flight data of the multi-rotor unmanned aerial vehicle, and ideal PID parameters of the multi-rotor unmanned aerial vehicle under different working environments are solved preferably by a frequency domain analysis method.
3. The control method according to claim 1, wherein in step 2, the result of the PID parameters in step 1 is called according to the working environment of the multi-rotor unmanned aerial vehicle, and the PID parameters before flying of the multi-rotor unmanned aerial vehicle are subjected to initialization assignment, and initial values of the PID parameters are respectively recorded as KP0、KI0、KD0。
4. The control method according to claim 1, wherein in step 3, the fuzzy adaptive control algorithm comprises performing online PID parameter K according to attitude deviation e and change rate ec thereof of the multi-rotor unmanned aerial vehicle obtained in real timeP、KIAnd KDThe PID parameters are compensated in real time and assigned to the PID controller.
5. A control method according to claim 3, characterized in that said deviatione is the deviation of the attitude angle and the angular rate of the multi-rotor unmanned aerial vehicle observed in real time from the expected attitude angle and the angular rate thereof, and the deviation e and the change rate thereof are subjected to a fuzzy adaptive control algorithm to obtain a PID parameter compensation value delta KP、ΔKIAnd Δ KDWill Δ KP、ΔKIAnd Δ KDSuperimposed to the PID initial value KP0、KI0、KD0And finally, reassigning the PID parameters.
6. The control method according to claim 4, wherein the deviation e and the change rate ec thereof are scaled by ke and kec, the direction and the magnitude of PID parameter adaptive adjustment are obtained through a fuzzy control rule, and the PID parameter compensation value Δ K is obtained through the scaling of kuP、ΔKIAnd Δ KDPreferably, the fuzzy control rule is defined by a compensation value Δ KP、ΔKIAnd Δ KDAnd establishing a relation between the system deviation e and the deviation change rate ec.
7. The control method according to claim 3, characterized by further comprising: and (3) repeating the step (3), and carrying out next round of judgment and correction on the PID parameters, preferably, repeating the step (3) at intervals.
8. An adaptive PID control system, the system comprising:
an initial parameter setting module for setting an initial value K of the PID parameterP0，KI0And KD0；
The parameter acquisition module is used for observing the attitude of the multi-rotor unmanned aerial vehicle in real time;
the deviation calculation module is used for calculating the deviation e and the change rate ec thereof between the attitude of the multi-rotor unmanned aerial vehicle observed in real time and the expected attitude, and the deviation e and the change rate ec thereof form an input variable;
a fuzzy processing module for obtaining PID parameter compensation value delta K according to a fuzzy control algorithmP、ΔKIAnd Δ KD；
And the correction module is used for correcting the PID initial parameter by using the compensation value to obtain a PID setting parameter and updating the PID parameter.
9. The control system of claim 8, wherein the input variable is converted into a fuzzy set in a fuzzy processing module, the fuzzy rule is queried according to the fuzzy set to obtain the direction and magnitude of PID parameter tuning, and the output variable PID parameter compensation value Δ K is obtained through a defuzzification processP、ΔKIAnd Δ KD。
10. The control system of claim 8, wherein the membership function for the input variable is a gaussian membership function and the membership function for the output variable is a triangular membership function.
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