scholarly journals Parametric Neural Network-Based Model Free Adaptive Tracking Control Method and Its Application to AFS/DYC System

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Zhijun Fu ◽  
Yan Lu ◽  
Fang Zhou ◽  
Yaohua Guo ◽  
Pengyan Guo ◽  
...  
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Nonlinear Identification ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking ◽  
Model Free ◽  
Model Control ◽  
Tracking Controller ◽  
Good Identification ◽  
Unknown System

This paper deals with adaptive nonlinear identification and trajectory tracking problem for model free nonlinear systems via parametric neural network (PNN). Firstly, a more effective PNN identifier is developed to obtain the unknown system dynamics, where a parameter error driven updating law is synthesized to ensure good identification performance in terms of accuracy and rapidity. Then, an adaptive tracking controller consisting of a feedback control term to compensate the identified nonlinearity and a sliding model control term to deal with the modeling error is established. The Lyapunov approach is synthesized to ensure the convergence characteristics of the overall closed-loop system composed of the PNN identifier and the adaptive tracking controller. Simulation results for an AFS/DYC system are presented to confirm the validity of the proposed approach.

Energy Comparisons of the Adaptive Tracking Control for a Linear Drive System

2015 ◽  
Vol 764-765 ◽  
pp. 602-606 ◽  
Author(s):  
Kun Yung Chen ◽  
Rong Fong Fung
Keyword(s):  
Tracking Control ◽  
External Disturbance ◽  
Hamiltonian Function ◽  
Electrical Energy ◽  
Drive System ◽  
Nonlinear Friction ◽  
Adaptive Tracking Control ◽  
Adaptive Tracking ◽  
Tracking Controller ◽  
Linear Drive System

In this paper, a mechatronic motor-table system is realized to plan the minimum input electrical energy trajectory (MIEET) based on Hamiltonian function. In this system, the adaptive tracking controller is designed to track the MIEET to overcome the nonlinear friction and external disturbance. Moreover, trapezoidal trajectory (TT) and regulator control are compared with the MIEET by the adaptive tracking controller. Finally, it is concluded that the MIEET based on the adaptive tracking controller can obtain the minimum input electrical energy and robustness performance for the mechatronic motor table system.

scholarly journals New Smith Internal Model Control of Two-Motor Drive System Based on Neural Network Generalized Inverse

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Guohai Liu ◽  
Jun Yuan ◽  
Wenxiang Zhao ◽  
Yaojie Mi
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Internal Model ◽  
Generalized Inverse ◽  
Control Structure ◽  
Control Method ◽  
Internal Model Control ◽  
Drive System ◽  
Motor Drive ◽  
Model Control

Multimotor drive system is widely applied in industrial control system. Considering the characteristics of multi-input multioutput, nonlinear, strong-coupling, and time-varying delay in two-motor drive systems, this paper proposes a new Smith internal model (SIM) control method, which is based on neural network generalized inverse (NNGI). This control strategy adopts the NNGI system to settle the decoupling issue and utilizes the SIM control structure to solve the delay problem. The NNGI method can decouple the original system into several composite pseudolinear subsystems and also complete the pole-zero allocation of subsystems. Furthermore, based on the precise model of pseudolinear system, the proposed SIM control structure is used to compensate the network delay and enhance the interference resisting the ability of the whole system. Both simulation and experimental results are given, verifying that the proposed control strategy can effectively solve the decoupling problem and exhibits the strong robustness to load impact disturbance at various operations.

scholarly journals Model Free Adaptive Control of Switched Reluctance Motor for Electric Vehicle

2021 ◽  
pp. 104-114
Author(s):  
Xifeng Mi , Yuanyuan Fan
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Control Method ◽  
Rbf Neural Network ◽  
Switched Reluctance Motor ◽  
Torque Ripple ◽  
Torque Distribution ◽  
Switched Reluctance ◽  
Model Free ◽  
Reluctance Motor

In this paper, the model free adaptive control method of switched reluctance motor for electric vehicle is studied. Based on the torque distribution control of SRM, a SRM control strategy based on torque current hybrid model based on RBF neural network is proposed in this paper. Based on the deviation between the dynamic average value and instantaneous value of SRM output torque, the online learning of RBF neural network is realized. At the same time, this paper constructs a torque current hybrid model, obtains the current variation law of SRM under low torque ripple operation, and reduces the torque ripple of SRM. The SRM torque distribution control is realized on the SRM experimental platform. Compared with the voltage chopper control method, the experimental results show that the torque ripple of SRM can be reduced by adopting the torque distribution control strategy.

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