Simulation and adaptive control of back propagation neural network proportional–integral–derivative for special launcher using new version of transfer matrix method for multibody systems

2019 ◽  
Vol 26 (9-10) ◽  
pp. 757-768 ◽  
Author(s):  
Yunfei Miao ◽  
Guoping Wang ◽  
Xiaoting Rui
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Transfer Matrix Method ◽  
Matrix Method ◽  
Multibody Systems ◽  
Proportional Integral Derivative ◽  
Backpropagation Neural Network ◽  
Proportional Integral ◽  
Proportional Integral Derivative Control

Rocket launcher system, as a special launcher placed on tactical vehicles, is a very complex mechanical system with characteristics of strong shock and vibration. In order to improve position accuracy, as well as reduce vibration, this paper creates a nonlinear dynamics model of the launcher system by using a new version of the transfer matrix method for multibody systems. The overall transfer equation of the nonlinear model is deduced. Combining with general kinematics equations of the rocket, the system launch dynamics are simulated and compared with experiments to verify the correctness of the model. On this basis, a backpropagation neural network proportional–integral–derivative adaptive control system is designed to improve servo control of the launcher. Then, the effectiveness of this method is verified by comparing with the traditional proportional–integral–derivative control method. Simulated results show that the backpropagation neural network proportional–integral–derivative control system makes the azimuth and elevation angles reach the target values smoothly and quickly, with higher accuracy. The results prove that the proposed method prominently reduces vibrations of the launcher, by adjusting the control parameters online according to the operation state of the system, presenting a better stability and robustness.

Fuzzy adaptive robust proportional–integral–derivative control optimized by the multi-objective grasshopper optimization algorithm for a nonlinear quadrotor

2020 ◽  
Vol 26 (17-18) ◽  
pp. 1574-1589
Author(s):  
Mohammad Javad Mahmoodabadi ◽  
Nima Rezaee Babak
Keyword(s):  
Control System ◽  
Sliding Surface ◽  
Proportional Integral Derivative ◽  
Adaptation Mechanism ◽  
Proportional Integral Derivative Controller ◽  
Proportional Integral ◽  
Grasshopper Optimization Algorithm ◽  
Proportional Integral Derivative Control ◽  
Grasshopper Optimization ◽  
Fuzzy Adaptive

Proportional–integral–derivative is one of the most applicable control methods in industry. Although it is simple and effective in most cases, it does not provide robustness against disturbances and may not perform well in cases with uncertainties and nonlinearities. In this study, a fuzzy adaptive robust proportional–integral–derivative controller is used to control a nonlinear 4 degree-of-freedom quadrotor. An adaptation mechanism is submitted to the proportional–integral–derivative controller for updating the proportional, derivative, and integral gains of proportional–integral–derivative control. Furthermore, a sliding surface is generated and submitted to the adaptation mechanism for better regulation of proportional–integral–derivative gains. Afterward, a fuzzy engine is applied to regulate the sliding surface for better performance of the adaptive proportional–integral–derivative when there are disturbance and uncertainties. The multi-objective grasshopper optimization algorithm is implemented on the control system for the regulation of the control system parameters to minimize the error and control effort of the proposed hybrid control system. Finally, the obtained results are presented for a nonlinear 4 degree-of-freedom multi-purpose (for marine, ground, and aerial maneuvers) quadrotor system designed and built in Sirjan University of Technology, Sirjan, Iran, to assure the effectiveness of this technique.

Performance-Adaptive Generalized Predictive Control-Based Proportional-Integral-Derivative Control System and Its Application

2014 ◽  
Vol 136 (6) ◽  
Author(s):  
Takao Sato ◽  
Toru Yamamoto ◽  
Nozomu Araki ◽  
Yasuo Konishi
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Pid Control ◽  
Design Method ◽  
Design Parameters ◽  
Generalized Predictive Control ◽  
Control Performance ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Proportional Integral Derivative Control

In the present paper, we discuss a new design method for a proportional-integral-derivative (PID) control system using a model predictive approach. The PID compensator is designed based on generalized predictive control (GPC). The PID parameters are adaptively updated such that the control performance is improved because the design parameters of GPC are selected automatically in order to attain a user-specified control performance. In the proposed scheme, the estimated plant parameters are updated only when the prediction error increases. Therefore, the control system is not updated frequently. The control system is updated only when the control performance is sufficiently improved. The effectiveness of the proposed method is demonstrated numerically. Finally, the proposed method is applied to a weigh feeder, and experimental results are presented.

Research on adaptive control strategy of hydraulic mechanical continuously variable transmission of a cotton picker

2020 ◽  
Vol 234 (17) ◽  
pp. 3335-3345
Author(s):  
Xin Zhao ◽  
Xiangdong Ni ◽  
Qi Wang ◽  
Mingxi Bao ◽  
Sheng Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Strategy ◽  
Basis Function ◽  
Continuously Variable Transmission ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Cotton Picker ◽  
Variable Transmission ◽  
Adaptive Control Strategy

Hydraulic mechanical continuously variable transmission has the advantages of good hydraulic stepless speed regulation performance and high efficiency of mechanical transmission, which can achieve the optimal matching of transmission system load and power source. In this article, an adaptive control strategy based on radial basis function neural network and proportional–integral–derivative control was proposed. The speed compound control method was used to solve the problems that the output speed of the hydraulic mechanical continuously variable transmission system was poor at the variable speed input and was difficult to control. The throttle opening and the engine speed were used as controller inputs. The pump–motor's displacement ratio and the output speed were used as controller outputs. Finally, the output speed of the cotton picker was stably controlled. Simulation and experimental results show that the transmission can quickly respond to the target speed and had little fluctuation based on different initial input speeds. The control strategy had good control precision and robustness. Compared with the traditional proportional–integral–derivative algorithm, the average steady-state error of the system output speed was controlled between ±0.0125%. The proposed algorithm based on radial basis function neural network proportional–integral–derivative adaptive control strategy had obvious control effect, and the stability of the speed output of the system was improved under the nonlinear input complex conditions. It provided research for the speed ratio adjustment and control of the hydraulic mechanical continuously variable transmission of the cotton picker.

Study on the visual tracking control technology of six-crawler machine

2019 ◽  
Vol 233 (17) ◽  
pp. 6051-6075
Author(s):  
Shuai Wang ◽  
Haoran Ge ◽  
Ruoding Ma ◽  
Da Cui ◽  
Xinhui Liu ◽  
...  
Keyword(s):  
Control System ◽  
Visual Tracking ◽  
Tracking Control ◽  
Control Algorithm ◽  
Least Square ◽  
Control Parameters ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Proportional Integral Derivative Control ◽  
Derivative Control

In this paper, the autonomous navigation of six-crawler machine is studied, and a visual tracking control method based on machine vision for fuzzy proportional–integral–derivative control of six-crawler machine is proposed. The steering principle of the six-crawler machine and the matching relationship between the steering angle and the speed of each crawler are introduced, and the control system is described in detail. Besides, the mathematical model for the unsteady steering is introduced to analyze the influence of deflection angle on the steering trajectory of the six-crawler machine. The image processing algorithm is programmed by LabVIEW software. After the image is fitted by graying, binary, filtering, edge detection, and least square method, the navigation line-fitting curve is obtained. The fuzzy proportional–integral–derivative control algorithm is programmed in the control system to control the six-crawler machine to drive along the navigation line. In order to obtain reasonable control parameters, a virtual prototype model of a six-crawler machine is established. In the CoLink module, the control algorithm of a six-crawler machine is established, and the co-simulation is carried out. By analyzing the simulation results, the control parameters of the fuzzy proportional–integral–derivative controller of the six-crawler machine are established. In order to verify the control effect of the visual tracking control system of the six-crawler machine, a physical prototype of the six-crawler machine is constructed and tested. The results show that the visual tracking control system of the six-crawler machine can complete the preset functions.

PIDNN control for Vernier-gimballing magnetically suspended flywheel under nonlinear change of stiffness and disturbance

2020 ◽  
pp. 095965182097757
Author(s):  
Jiqiang Tang ◽  
Mengyue Ning ◽  
Xu Cui ◽  
Tongkun Wei ◽  
Xiaofeng Zhao
Keyword(s):  
Neural Network ◽  
Attitude Control ◽  
Magnetic Force ◽  
Magnetic Bearing ◽  
Network Control ◽  
Gradient Descent Method ◽  
Neural Network Control ◽  
Proportional Integral Derivative ◽  
Tracking Accuracy ◽  
Proportional Integral

Vernier-gimballing magnetically suspended flywheel is often used for attitude control and interference suppression of spacecrafts. Due to the special structure of the conical magnetic bearing, the radial component generated by the axial magnetic force and the change of the magnetic air gap will cause the nonlinearity of stiffness and disturbance. That will lead to not only poor stability of the suspension control system but also unsatisfactory tracking accuracy of the rotor position. To solve the nonlinear problem of the system, this article proposes a proportional–integral–derivative neural network control scheme. First, the rotor model considering the nonlinear variation of disturbance and stiffness parameters is established. Then, the weight of neural network is adjusted by the gradient descent method online to ensure the accurate output of magnetic force. Finally, the convergence analysis is carried out based on the Lyapunov stability theory. Compared with the general proportional–integral–derivative control and the radial basis function neural network control, the simulation results demonstrate that the proposed method has the highest tracking accuracy and excellent performance in improving stability. The experimental results prove the correctness of the theoretical analysis and the validity of the proposed method.

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