Neuro-fuzzy sliding mode controller: design and stability analysis

Abstract

In this paper, design and stability analysis of neuro-fuzzy sliding mode controller is discussed. The controller has two parts: fuzzy logic system and neural network. They are used concurrently but each part is responsible for one phase of sliding mode controller. The fuzzy logic system is utilised to control reaching phase dynamics and the feed-forward neural network is employed to keep the system states on the sliding surface. The neural network is trained online using modified back-propagation algorithm. Initially, fuzzy logic system is dominant and as the system moves from reaching phase to sliding phase, neural network becomes more active and hence, a hybrid computing paradigm is achieved. The stability of the system is analysed using Lyapunov's direct method. The proposed controller is implemented to regulate a second-order nonlinear uncertain system and simulation results confirm that the proposed system reduces chattering and improves transient response.