Observer-based dynamic surface control for flexible-joint manipulator system with input saturation and unknown disturbance using type-2 fuzzy neural network

被引：39
作者：
Hu, Yi [1 ]
Dian, Songyi [1 ]
Guo, Rui [2 ]
Li, Shengchuan [3 ]
Zhao, Tao [1 ]
机构：
[1] Sichuan Univ, Coll Elect Engn, Chengdu 610065, Peoples R China
[2] State Grid Shandong Elect Power Co, Jinan 250001, Peoples R China
[3] State Grid Liaoning Elect Power Co Ltd, Elect Power Res Inst, Shenyang 110006, Peoples R China
来源：
NEUROCOMPUTING | 2021年 / 436卷
关键词：
Flexible-joint manipulator; Dynamic surface control; Interval type-2 fuzzy system; Neural network; Nonlinear disturbance observer; NONLINEAR-SYSTEMS; TRACKING CONTROL; ROBUST-CONTROL; ROBOT;
D O I：
10.1016/j.neucom.2020.12.121
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the nonlinear disturbance observer (NDO) based dynamic surface control (DSC) with interval type-2 fuzzy neural network (IT2FNN) approximator is proposed for flexible-joint manipulator with the input saturation and unknown nonlinear disturbance. The DSC technique has tremendous advantages in eliminating the 'explosion of complexity' problem. The IT2FNN approximator is used to deal with parameter uncertainties. The NDO is applied to estimate the unknown external disturbance and compensate the saturation constrain. From Lyapunov stability analysis, it is proved that with the proposed control scheme, all signals of the closed-loop system are semiglobally uniformly ultimately bounded. Simulation results are carried out to demonstrate the effectiveness of the proposed scheme. Compared with the adaptive DSC with neural network (NN) approximator and type-1 fuzzy (T1F) approximator, the tracking error of the proposed control scheme converges to a sufficiently small value. (c) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页码：162 / 173
页数：12
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