CN113759722B - Unmanned aerial vehicle active disturbance rejection controller parameter optimization method - Google Patents
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Abstract
The invention discloses an unmanned aerial vehicle Active Disturbance Rejection Controller (ADRC) parameter optimization method, which aims at the problems that in engineering practice, the ADRC has more controller parameters which need to be adjusted compared with a PID controller, and the parameters are mutually influenced, so that manual adjustment is difficult and optimal is difficult to achieve; the invention solves the problem of parameter adjustment of the active disturbance rejection controller, improves the stability and the robustness of the unmanned aerial vehicle control system, and greatly promotes the wide application of the active disturbance rejection controller in practice.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle nonlinear control, in particular to an unmanned aerial vehicle active disturbance rejection controller parameter optimization method.
Background
The unmanned aerial vehicle has the advantages of high degree of freedom, high flexibility, high adaptability to complex terrains, low cost and the like, is commonly used for executing the patrol and search and rescue tasks in dangerous areas or complex terrains, and has wide application in civil and military fields at present. Such as monitoring and reconnaissance tasks in the military domain; the method is applied to electric power inspection, energy system inspection, bridge inspection, forest fire rescue, agricultural plant protection and the like in the civil field. How to realize the stable flight of the unmanned aerial vehicle is a precondition that the unmanned aerial vehicle can finish the specified inspection task. However, unmanned aerial vehicles are a nonlinear, under-actuated, strongly coupled system, and it is difficult to build their exact mathematical model; how to design a controller with high stability and strong robustness for the system characteristics of unmanned aerial vehicles is a difficulty for engineering technicians to study.
The active disturbance rejection controller (ADRC, active Disturbance Rejection Control) is a novel control method which is not dependent on an accurate system model and is proposed by analyzing the advantages and disadvantages of the PID controller based on the principle of a traditional PID controller, has the advantages of high tracking speed, high control precision, strong disturbance resistance, capability of estimating and compensating various disturbances suffered by a system in real time and the like, and is applied to a plurality of practices at present. The unmanned aerial vehicle control system based on ADRC is designed, the problem that many unknown disturbance exists in the unmanned aerial vehicle in the flight process can be solved, and the unmanned aerial vehicle control system with high stability and strong robustness is realized.
However, in engineering practice, ADRC has more controller parameters to be adjusted compared with PID controllers, and the parameters are mutually influenced; the manual adjustment is difficult and is difficult to achieve the optimal, which brings great obstruction to the wide application of ADRC in the actual unmanned aerial vehicle control system.
In order to solve the problems, suiyuan Shen et al (Attitude Active Disturbance Rejection Control of the quadrotor and its parameter tuning [ J ], international Journal of Aerospace Engineering) designed an ADRC-based four-rotor unmanned aerial vehicle attitude controller, and adopted an adaptive genetic algorithm-particle swarm optimization (AGA-PSO) to optimize the controller parameters, so as to solve the problem that the controller parameters are difficult to adjust. Zhihao Cai et al (Quadrotor trajectory tracking and obstacle avoidance by chaotic grey wolf optimization-based active disturbance rejection control [ J ], mechanical Systems and Signal Processing) propose a chaotic gray wolf optimization algorithm (CGWO, chaotic grey wolf optimization) combining chaotic initialization and chaotic search to obtain optimal parameters for attitude and position controllers. Wu Lei, macro-protection, du Jingli et al (an ADRC parameter learning algorithm [ J ]. Automation chemistry report) propose an ADRC parameter self-learning algorithm CARLA-ADRC in combination with a continuous motion reinforcement learner (CARLA, continuous action reinforcement learning automata) for the problems of multiple ADRC parameters, strong coupling and difficult parameter determination.
However, the above-mentioned methods have drawbacks in that the optimization search range is not wide, the solution accuracy is not high, the solution time is too long, and local optimum values are easily trapped.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle ADRC parameter optimization method, which solves the problem that the parameters of a controller are difficult to adjust in an unmanned aerial vehicle control system based on ADRC.
The technical scheme adopted by the invention is that an unmanned aerial vehicle ADRC parameter optimization method is implemented by adding fuzzy logic based on a traditional PSO algorithm according to the following steps:
step 1: initializing a population; the method mainly comprises population dimension, population scale, maximum iteration times of the population, and the position and speed of initial particles. Wherein, the population dimension is 7 dimensions, corresponding to 7 parameters of ADRC respectively: beta 01 ，β 02 ，β 03 ，r，c，h 1 ，b 0 The method comprises the steps of carrying out a first treatment on the surface of the The population scale and the maximum iteration number of the population are adjusted according to the actual requirements of the project; the position and velocity of the initial particles are randomly generated based on the upper and lower limits of the position and velocity of the particles.
Step 2: judging whether the maximum iteration times are reached; if the optimal parameters reach the optimal parameters, the global optimal particles are recorded, and the position values of the particles are the optimal parameters of the optimized controller; if not, go to step 3.
Step 3: calculating the fitness value of each particle according to a fitness function, wherein the fitness function is defined as follows:
because the rising stage of ADRC is mainly affected by TD part, the invention is based on absolute error product criterion (IAE), the designed fitness function is defined as the cumulative sum of the system response value and the expected value error from the rising time to the sampling period end, the designed fitness function can lead the controller to have better performance than other fitness functions, and the mathematical expression of the fitness function is as follows:
wherein, the fitness value of J particles, t r For the rise time of the system step response, T is the sampling period of the system, and e (T) is the difference between the response of the controller at time T and the expected input of the controller.
Step 4: calculating the particle with the smallest fitness value according to the fitness of all the particles in the iteration, and recording the optimal value of the particle in the iteration; and meanwhile, comparing the global optimal value with the global optimal value, if the global optimal value is small, updating the global optimal value, otherwise, not updating.
Step 5: and calculating a population iteration process, population diversity and population errors.
The calculation method of the population iteration process comprises the following steps:
expressed in terms of percentages, the degree of PSO iteration is expressed in terms of normalized terms, and the number of normalized iterations is:
wherein, NI (k) is the iteration degree of the population in the kth iteration, k is the algebra of the current iteration, and M is the largest iteration algebra of the population.
The method for calculating the population diversity comprises the following steps:
the diversity of the population is expressed using the degree of dispersion between particles, and is obtained by measuring the average value of the euclidean distance between each particle and the optimal particle, and the population diversity D (k) is specifically as follows:
wherein,is the globally optimal particle before the kth iteration. Population diversity is expressed using a normalized form, defined for normalized population diversity as:
the error of the population is defined as the average of the sum of the differences between the fitness of each particle and the fitness of the best particle; the population error E (k) is defined as:
the population error is expressed in a normalized form, and the normalized population error is defined as:
where k is the kth iteration of PSO, M is the population size, fitness (x i ) Is the fitness value of particle i.
Step 6: solving c according to a fuzzy logic system 1 、c 2 。
Wherein c 1 、c 2 The learning factors of PSO represent the weights of social cognition and self cognition respectively; c defined as 1 、c 2 The value range is between 0.5 and 2.5.
The structure of the defined fuzzy logic system is a three-input two-output system; it should be noted that before performing fuzzy inference, the fuzzy rule needs to be defined according to the requirements of the actual project.
Wherein, for the fuzzy logic system, the iteration process, population diversity and population error obtained in the step 5 are adopted as the input of the fuzzy logic system, and the factor c is learned 1 、c 2 As an output of the blurring system.
Step 7: the velocity and position of the particles are updated.
The speed update formula of the particles is as follows:
wherein alpha is an adjustable parameter, and the value range is between 0.6 and 0.8.
Wherein the position of the particles is updated as follows:
x i (k+1)＝x i (k)+v i (k+1) (8)
wherein k is the iteration number; v i (k) For the velocity of particle i at the kth iteration, x i (k+1) is the position of particle i at the (k+1) th iteration, p i The historical optimum value of particle i, g is the historical optimum value of all particles.
Step 8: and returning to the step 2 for judgment.
The unmanned aerial vehicle active disturbance rejection controller parameter optimization method provided by the invention has the advantages that: on the basis of the traditional particle swarm optimization algorithm (PSO, particle Swarm optimization), fuzzy logic is added, and the method has the advantages of high convergence speed, high solving precision and difficult sinking into a local optimal value.
The unmanned aerial vehicle active disturbance rejection controller parameter optimization method provided by the invention solves the dilemma that ADRC has more controller parameters compared with a PID controller and needs to be adjusted, and the parameters are mutually influenced, so that manual adjustment is difficult and optimal adjustment is difficult to achieve, and the unmanned aerial vehicle active disturbance rejection controller parameter optimization method can be widely applied to parameter adjustment of the unmanned aerial vehicle ADRC, and compared with a controller which is not subjected to parameter optimization, the unmanned aerial vehicle control system with high stability and strong robustness can be realized after the controller parameters are optimized by the method.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a four rotor unmanned aerial vehicle attitude control system designed in the present invention;
FIG. 2 is a flow chart of an implementation of an ADRC parameter optimization method for an unmanned aerial vehicle according to the present invention;
fig. 3 is a frame of a fuzzy logic system according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present invention will be further described in detail with reference to the accompanying drawings and examples.
Taking a quadrotor unmanned aerial vehicle as an example, the invention analyzes a mathematical model of the quadrotor unmanned aerial vehicle aiming at the system characteristics of the quadrotor unmanned aerial vehicle; the mathematical model of the four-rotor unmanned aerial vehicle is as follows:
wherein J r Omega is the moment of inertia of the rotor i The rotation speed of the corresponding motor; phi, theta, phi are the angles (anticlockwise) by which the fuselage rotates about the x-axis, y-axis, z-axis, respectively;acceleration of the machine body along the x axis, the y axis and the z axis is respectively; j (J) x ,J y ,J z The moment of inertia of the machine body in three directions respectively; g is the acceleration (9.81 in value) and m is the mass of the fuselage.
Wherein U is 1 、U 2 、U 3 、U 4 Is defined as follows:
wherein d is the arm length from the motor to the center of the unmanned aerial vehicle; c T Is the tension coefficient of the propeller; c M Is the torque coefficient.
The attitude control system of the four-rotor unmanned aerial vehicle based on ADRC, which is designed for the four-rotor unmanned aerial vehicle, is shown in figure 1;
wherein translational movement of the quadrotor unmanned in 3-dimensional space can be achieved by changing attitude angle, so the desired input of the control system contains three control quantities, namely the desired roll angle phi d Desired pitch angle θ d And a desired yaw angle ψ d 。
The attitude control loop of the quadrotor unmanned aerial vehicle comprises an ADRC-based attitude controller and control quantity conversion.
The ADRC mainly comprises a Tracking Differentiator (TD), an Extended State Observer (ESO) and a nonlinear state error feedback control rate (NLSEF).
The TD schedules an excessive process for the input signal, solving the contradiction between system overshoot and rapidity. The tracking differentiator mathematical expression for a second order system is as follows:
wherein fhan (·) is the fastest synthesis function, T is the sampling period of the control system, v d 、v 1 、v 2 The desired input signal, the desired input tracking signal, and the differentiation of the tracking signal, respectively. r and h are two adjustable parameters of the tracking differentiator; wherein r is 0 As a speed factor, r is increased 0 The response speed is quickened, the excessive process is reduced, but the r is too large 0 The value will make the tracking signal closer to the desired input signal, losing the meaning of the transition process; h is a 0 To filter the factor, add h 0 The value of (2) can improve the filtering effect and also bring about a larger phase delay.
ESO is the core of ADRC, which is independent of the exact mathematical model of the control object, estimates and compensates the controller for the total disturbance of the system (uncertainty inside the system and disturbance outside the system) based on the input and output of the system, and the mathematical expression of ESO is as follows:
wherein fal (·) is a nonlinear function α 1 ，α 2 For variable parameters, determining the interval length of linear segment of nonlinear function, generally taking the value alpha 1 ＝0.5，α 2 ＝0.25。β 01 、β 02 、β 03 Is determined by the sampling step size of the system.
The NLSEF generates a system tracking error signal by using a TD phase and an ESO phase, and generates a control quantity by a series of nonlinear combinations, and the mathematical expression of the NLSEF is as follows:
by compensating for the state variable of the expansion observed by the ESO, the control quantity can be obtained:
wherein r is the gain of the control quantity, and generally takes a larger value, c is the damping factor, h 1 As a precision factor, b 0 Is a compensation factor.
In summary, the parameters that need to be adjusted for ADRC are: beta 01 ，β 02 ，β 03 ，r，c，h 1 ，b 0 。
The specific implementation flow of the unmanned aerial vehicle ADRC parameter optimization method is as follows, and the flow chart is shown in fig. 2:
step 1: initializing a population; the method mainly comprises population dimension, population scale, maximum iteration times of the population, and the position and speed of initial particles. Wherein, the population dimension is 7 dimensions, corresponding to 7 parameters of ADRC respectively: beta 01 ，β 02 ，β 03 ，r，c，h 1 ，b 0 The method comprises the steps of carrying out a first treatment on the surface of the The population scale and the maximum iteration number of the population are adjusted according to the actual requirements of the project; the position and velocity of the initial particles are randomly generated based on the upper and lower limits of the position and velocity of the particles.
TABLE 1 particle ranges
| Parameters (parameters) | Value range | Parameters (parameters) | Value range |
| r | [0，400] | β 01 | [0，500] |
| c | [0，400] | β 02 | [0，4000] |
| h 1 | [0，400] | β 03 | [0，8000] |
| b 0 | [0，100] |
Step 2: judging whether the maximum iteration times are reached; if the optimal parameters reach the optimal parameters, the global optimal particles are recorded, and the position values of the particles are the optimal parameters of the optimized controller; if not, go to step 3.
In this embodiment, the maximum number of iterations is set to 100.
Step 3: calculating the fitness value of each particle according to a fitness function, wherein the fitness function is defined as follows:
because the rising phase of ADRC is mainly influenced by the TD part of ADRC, the invention is based on absolute error product criterion (IAE), the designed fitness function is defined as the cumulative sum of the system response value and the expected value error from the rising time of the system step response to the end of the sampling period, and the designed fitness function can lead the controller to have better performance than other fitness functions, and the mathematical expression of the fitness function is as follows:
wherein, the fitness value of J particles, t r For the rise time of the system step response, T is the sampling period of the system, and e (T) is the difference between the response of the controller at time T and the expected input of the controller.
Step 4: calculating the particle with the smallest fitness value according to the fitness of all the particles in the iteration, and recording the optimal value of the particle in the iteration; and meanwhile, comparing the global optimal value with the global optimal value, if the global optimal value is small, updating the global optimal value, otherwise, not updating.
Step 5: and calculating a population iteration process, population diversity and population errors.
The calculation method of the population iteration process comprises the following steps:
expressed in terms of percentages, the degree of PSO iteration is expressed in terms of normalized terms, and the number of normalized iterations is:
wherein, NI (k) is the iteration degree of the population in the kth iteration, k is the algebra of the current iteration, and M is the largest iteration algebra of the population.
The method for calculating the population diversity comprises the following steps:
the diversity of the population is expressed using the degree of dispersion between particles, and is obtained by measuring the average value of the euclidean distance between each particle and the optimal particle, and the population diversity D (k) is specifically as follows:
wherein,is the globally optimal particle before the kth iteration. Population diversity is expressed using a normalized form, defined for normalized population diversity as:
the error of the population is defined as the average of the sum of the differences between the fitness of each particle and the fitness of the best particle; the population error E (k) is defined as:
the population error is expressed in a normalized form, and the normalized population error is defined as:
where k is the kth iteration of PSO, M is the population size, fitness (x i ) Is the fitness value of particle i.
Step 6: solving c according to a fuzzy logic system 1 、c 2 。
Wherein c 1 、c 2 The learning factors of PSO represent the weights of social cognition and self cognition respectively; c defined as 1 、c 2 The value range is between 0.5 and 2.5.
The structure of the defined fuzzy logic system is shown in fig. 3.
Wherein, for the fuzzy logic system, the iteration process, population diversity and population error obtained in the step 5 are adopted as the input of the fuzzy logic system, and the factor c is learned 1 、c 2 As an output of the blurring system.
Step 7: the velocity and position of the particles are updated.
The speed update formula of the particles is as follows:
wherein alpha is a parameter, and the value range is 0.6-0.8.
Wherein the position of the particles is updated as follows:
x i (k+1)＝x i (k)+v i (k+1) (22)
wherein k is the iteration number; v i (k) For the velocity of particle i at the kth iteration, x i (k+1) is the position of particle i at the (k+1) th iteration, p i The historical optimum value of particle i, g is the historical optimum value of all particles.
Step 8: and returning to the step 2 for judgment.
And (3) respectively corresponding to 7 controller parameters of the ADRC through the optimization result obtained in the step (2), namely, the optimal controller parameters.
The theory analysis part of the invention finally needs to carry out simulation analysis on Matlab and Simulink platforms, and carries out verification and comparison on experimental results.
Finally, the optimized optimal controller parameters are required to be deployed into an actual unmanned aerial vehicle hardware platform, and whether the actual flight effect of the unmanned aerial vehicle reaches the expectations is observed.
The foregoing disclosure is merely illustrative of one preferred embodiment of the present invention, and it is not intended to limit the scope of the claims herein, as it will be understood by those skilled in the art that all or part of the above embodiments may be implemented and equivalents thereof may be substituted and practiced within the scope of the invention as defined in the claims.
Claims (2)
1. The unmanned aerial vehicle active disturbance rejection controller parameter optimization method is characterized in that a particle swarm algorithm combined with fuzzy logic is adopted to optimize parameters of an active disturbance rejection controller, and the method is implemented according to the following steps:
step 1: initializing a population;
the population dimension in the step 1 is 7 dimensions, and the population dimension corresponds to 7 parameters of the active disturbance rejection controller respectively: beta 01 ，β 02 ，β 03 ，r，c，h 1 ，b 0 ；β 01 、β 02 、β 03 Is determined by the sampling step length of the system, r is the gain of the control quantity, c is the damping factor, h 1 As a precision factor, b 0 Is a compensation factor; the population scale and the maximum iteration number of the population are adjusted according to the actual requirements of the project; the position and the speed of the initial particles are randomly generated according to the upper limit and the lower limit of the position and the speed of the particles;
step 2: judging whether the maximum iteration times are reached; if the optimal parameters reach the optimal parameters, the global optimal particles are recorded, and the position values of the particles are the optimal parameters of the optimized controller; if not, turning to the step 3;
step 3: calculating the fitness value of each particle according to the fitness function;
step 4: calculating particles with minimum fitness values according to the fitness of all particles of the iteration, and recording the optimal values of the particles of the iteration; meanwhile, the value is compared with the global optimal value, if the global optimal value is small, the global optimal value is updated, otherwise, the value is not updated;
step 5: calculating a population iteration process, population diversity and population errors;
the method for calculating the population iteration process, the population diversity and the population error comprises the following steps:
the calculation method of the population iteration process comprises the following steps:
expressed in terms of percentage, the degree of iteration of the particle swarm algorithm is expressed, and expressed in terms of normalized form, and the number of normalized iterations is as follows:
wherein, NI (k) is the iteration degree of the population in the kth iteration, k is the algebra of the current iteration, and M is the largest iteration algebra of the population; the method for calculating the population diversity comprises the following steps:
the diversity of the population is expressed using the degree of dispersion between particles, and is obtained by measuring the average value of the euclidean distance between each particle and the optimal particle, and the population diversity D (k) is specifically as follows:
wherein,is the globally optimal particle before the kth iteration; population diversity is expressed using a normalized form, defined for normalized population diversity as:
the error of the population is defined as the average of the sum of the differences between the fitness of each particle and the fitness of the best particle; the population error E (k) is defined as:
the population error is expressed in a normalized form, and the normalized population error is defined as:
where k is the kth iteration of the particle swarm algorithm, M is the population size, fitness (x i ) The fitness value of the particle i;
step 6: solving c according to a fuzzy logic system 1 、c 2 ；
The structure of the fuzzy logic system is a three-input two-output system; for the fuzzy logic system, the iteration process and population diversity obtained in the step 5 are adoptedSex and population errors are input as a fuzzy logic system, and a learning factor c 1 、c 2 As an output of the fuzzy system;
step 7: updating the speed and position of the particles;
step 8: and returning to the step 2 for judgment.
2. The unmanned aerial vehicle active disturbance rejection controller parameter optimization method according to claim 1, wherein the fitness function mathematical expression is as follows:
wherein, the fitness value of J particles, t r For the rise time of the system step response, T is the sampling period of the system, and e (T) is the difference between the response of the controller at time T and the expected input of the controller.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101425726A (en) * | 2008-11-18 | 2009-05-06 | 天津大学 | Motor optimized design method based on fuzzy expert system multi-target particle team |
| CN106682682A (en) * | 2016-10-20 | 2017-05-17 | 北京工业大学 | Method for optimizing support vector machine based on Particle Swarm Optimization |
| CN109917800A (en) * | 2019-03-19 | 2019-06-21 | 中国科学院深圳先进技术研究院 | Unmanned aerial vehicle (UAV) control method, apparatus, computer equipment and storage medium |
| CN109947124A (en) * | 2019-04-25 | 2019-06-28 | 南京航空航天大学 | Improve particle swarm algorithm Optimization of Fuzzy PID unmanned helicopter attitude control method |
| CN110161995A (en) * | 2019-06-10 | 2019-08-23 | 北京工业大学 | Municipal sewage treatment procedure optimization control method based on dynamic multi-objective particle swarm algorithm |
| CN110376900A (en) * | 2019-08-16 | 2019-10-25 | 中国科学院深圳先进技术研究院 | Parameter optimization method, terminal device and computer storage medium |
| CN111142387A (en) * | 2020-01-09 | 2020-05-12 | 东南大学 | Limited generalized predictive control method based on improved particle swarm optimization |
| CN111459179A (en) * | 2020-05-18 | 2020-07-28 | 哈尔滨理工大学 | Four-rotor active disturbance rejection controller based on particle swarm optimization algorithm |
| CN113253603A (en) * | 2021-04-29 | 2021-08-13 | 桂林电子科技大学 | Design method of unmanned aerial vehicle active disturbance rejection controller based on FOPSO algorithm |
-
2021
- 2021-09-13 CN CN202111067168.0A patent/CN113759722B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101425726A (en) * | 2008-11-18 | 2009-05-06 | 天津大学 | Motor optimized design method based on fuzzy expert system multi-target particle team |
| CN106682682A (en) * | 2016-10-20 | 2017-05-17 | 北京工业大学 | Method for optimizing support vector machine based on Particle Swarm Optimization |
| CN109917800A (en) * | 2019-03-19 | 2019-06-21 | 中国科学院深圳先进技术研究院 | Unmanned aerial vehicle (UAV) control method, apparatus, computer equipment and storage medium |
| CN109947124A (en) * | 2019-04-25 | 2019-06-28 | 南京航空航天大学 | Improve particle swarm algorithm Optimization of Fuzzy PID unmanned helicopter attitude control method |
| CN110161995A (en) * | 2019-06-10 | 2019-08-23 | 北京工业大学 | Municipal sewage treatment procedure optimization control method based on dynamic multi-objective particle swarm algorithm |
| CN110376900A (en) * | 2019-08-16 | 2019-10-25 | 中国科学院深圳先进技术研究院 | Parameter optimization method, terminal device and computer storage medium |
| CN111142387A (en) * | 2020-01-09 | 2020-05-12 | 东南大学 | Limited generalized predictive control method based on improved particle swarm optimization |
| CN111459179A (en) * | 2020-05-18 | 2020-07-28 | 哈尔滨理工大学 | Four-rotor active disturbance rejection controller based on particle swarm optimization algorithm |
| CN113253603A (en) * | 2021-04-29 | 2021-08-13 | 桂林电子科技大学 | Design method of unmanned aerial vehicle active disturbance rejection controller based on FOPSO algorithm |
Non-Patent Citations (5)
| Title |
|---|
| 基于PSO算法的模糊控制方法研究;赵诗雨;扈宏杰;;自动化应用;20161125(第11期);全文 * |
| 基于改进粒子群算法的自抗扰控制器参数优化及仿真;楚东来;;信息通信(第09期);全文 * |
| 多无人机编队协同控制软件平台设计与仿真;李春;宋晓程;李芳芳;;现代防御技术(第06期);全文 * |
| 改进的粒子群算法及其在控制器参数整定中的应用;罗春松;硕士电子期刊;20100115;全文 * |
| 改进遗传算法整定自抗扰控制器参数及应用;孙雨萌;张旭秀;;自动化与仪表(第03期);全文 * |
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