Robust sliding model control-based adaptive tracker for a class of nonlinear systems with input nonlinearities and uncertainties. (April 2021)

• Record Type: Journal Article
• Authors: Fang, Jiunn-Shiou; Tsai, Jason Sheng-Hong; Yan, Jun-Juh; Guo, Shu-Mei
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 43:Number 7(2021)
• Page Start: 1629
• Page End: 1639
• Publication Date: 2021-04
• Subjects: Robust tracker -- sliding mode control -- radial basis function neural network -- adaptive control -- input nonlinearity; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331220976114
• Languages: English
• ISSNs: 0142-3312

Abstract

A robust adaptive tracker is newly proposed for a class of nonlinear systems with input nonlinearities and uncertainties. Because the upper bounds of input nonlinearities and uncertainties are difficult to be acquired, the adaptive control integrated with sliding mode control (SMC) and radial basis function neural network (RBFNN) are utilized to cope with these undesired problems and effectively complete the robust tracker design. The main contributions are concluded as follows: (1) new sufficient conditions are obtained such that the proposed adaptive control laws can avoid overestimation; (2) A smoothtanh function is introduced to eliminate the undesired chattering phenomenon in the traditional SMC systems; (3) A robust tracker is proposed such that the controlled system outputs can robustly track the pre-specified trajectories directly, even when subjected to unknown input nonlinearities and uncertainties. Finally, the numerical simulation results are illustrated to verify the proposed approach.