Neural adaptive compensation control for a class of MIMO affine uncertain nonlinear systems with actuator failures
- Resource Type
- article
- Authors
- Shao-jie Zhang; Xiang-wei Qiu
- Source
- Systems Science & Control Engineering, Vol 6, Iss 1, Pp 37-47 (2018)
- Subject
- MIMO nonlinear systems
actuator failure
neural networks
MMST
backstepping control
PPB
adaptive compensation control
Control engineering systems. Automatic machinery (General)
TJ212-225
Systems engineering
TA168
- Language
- English
- ISSN
- 2164-2583
21642583
A new neural adaptive compensation control approach for a class of multi-input multi-output (MIMO) uncertain nonlinear systems with actuator failures is proposed in this paper. In order to enlarge the set of compensable actuator failures, an actuator grouping scheme based on multiple model switching and tuning (MMST) is proposed for the nonlinear MIMO minimum phase systems with multiple actuator failures, and RBF neural networks are used to approximate the error of plant model. Then an adaptive compensation scheme based on prescribed performance bound (PPB) which characterizes the convergence rate and maximum overshoot of the tracking error is designed for the system to ensure closed-loop signal boundedness and asymptotic output tracking despite unknown actuator failures. Simulation results are given to show the effectiveness of the control design.