Adaptive sliding mode control design for a class of uncertain singularly perturbed nonlinear systems
Kuo-Jung Lin · International Journal of Control · 2013
This paper addresses adaptive sliding mode control (ASMC) of uncertain singularly perturbed nonlinear (USPN) systems with guaranteed H∞ control performance. First, we use Takagi–Sugeno (T–S) fuzzy model to construct the USPN systems. Then, the sliding surface can be determined via linear matrix inequality (LMI) design procedure. Second, we propose neural network (NN)-based ASMC design to stabilise the USPN systems. The proposed methods are based on the Lyapunov stability theorem. The adaptive law can reduce the effect of uncertainty. The proposed NN-based ASMC will stabilise the USPN systems for all ϵ ∈ (0, ϵ*]. Simulation result reveals that the proposed NN-based ASMC scheme has better convergence time compared with the fuzzy control scheme (Li, T.-H.S., & Lin, K.J. (2004). Stabilization of singularly perturbed fuzzy systems, IEEE Transactions on Fuzzy Systems, 12, 579–595.).