Parameter Estimation-Based Time-Varying Sliding Mode Control for Multimotor Driving Servo Systems

被引:36
|
作者
Zhao, Wei [1 ,2 ]
Ren, Xuemei [1 ]
Wang, Shubo [3 ]
机构
[1] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China
[2] China Elect Technol Grp Corp, Inst 32, Shanghai 201808, Peoples R China
[3] Qingdao Univ, Coll Automat & Elect Engn, Qingdao 266071, Peoples R China
基金
中国国家自然科学基金;
关键词
Adaptive parameter estimation (APE); fuzzy neural network; multimotor driving servo systems; synchronization control; time-varying sliding mode; SPEED SYNCHRONIZATION CONTROL; PRESCRIBED PERFORMANCE; INDUCTION-MOTORS; MOTION CONTROL; NETWORKS;
D O I
10.1109/TMECH.2017.2725344
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper presents a parameter estimation-based time-varying sliding mode controller for the multimotor driving servo systems with the unknown parameters and actuator saturation, such that the finite-time coupling control of load output tracking and motors synchronization is achieved. First, the parameter estimation scheme employs the filtered representation of the load system dynamics to obtain the parameter estimation error, which is utilized to design the adaptive parameter law with dynamic gain to attain the finite-time parameter estimation with the prescribed transient and steady-state performances. Then, the generalized coupling error (GCE) is designed based on the estimation results to transform the complicated coupling control problem into the GCE convergence, which would extremely simplify the controller design. By incorporating the fuzzy echo state network (FESN), a time-varying sliding mode controller is presented to successfully eliminate the reaching phase and guarantee the finite-time GCE convergence with L-2 norm performance. For the FESN compensation, a novel learning law is derived based on the norm of FESN weight vector to reduce the computational cost where only a scalar weight needs to be updated online. Finally, the comparative experiments illustrate the benefits and reliability of the proposed algorithms.
引用
收藏
页码:2330 / 2341
页数:12
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