Paper Info
Title
Optimization-based adaptive neural sliding mode control for nonlinear systems with fast and accurate response under state and input constraints
Abstract
The high-performance control requires the system to be stable, fast and accurate simultaneously. However, various systems (e.g., motors, industrial robots) generally face technical challenges such as nonlinearities, uncertainties, external disturbances and physical constraints, which make it difficult to reach the hardware potential of the systems to track the desired trajectories when satisfying the high-performance control requirements. Therefore, take a two-order nonlinear system for example, an optimization-based adaptive neural sliding mode control based on a two-loop control structure is proposed in this paper, where the outer and inner loops are designed separately to achieve different control requirements. Namely, the outer loop is designed as a model predictive control (MPC)-based optimization problem, which can optimize the desired trajectories to meet the state and input constraints, and maximize the converging speed of transient response as fast as possible, and the inner loop is designed with a recurrent neural network (RNN)-based adaptive neural sliding mode controller, which can guarantee the tracking of the replanned desired trajectories from outer loop as accurate as possible. The stability of the system is guaranteed by Lyapunov theorem, the optimal tracking performance is achieved under nonlinearities, uncertainties, external disturbances and physical constraints, and comparative simulation with a motor system is carried out to verify the effectiveness and superiority of the proposed approach.
Year
DOI
Venue
2022
10.1016/j.jfranklin.2022.07.010
Journal of the Franklin Institute
DocType
Volume
Issue
Journal
359
13
ISSN
Citations 
PageRank 
0016-0032
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Jinna Fu100.34
Fanghao Huang200.34
Zheng Chen3398.99