Design of a fuzzy controller with fuzzy sliding surface
Fuzzy Sets and Systems - Special issue on fuzzy neural control
Optimal design of fuzzy sliding-mode control: a comparative study
Fuzzy Sets and Systems
Decoupled adaptive neuro-fuzzy (DANF) sliding mode control system for a Lorenz chaotic problem
Expert Systems with Applications: An International Journal
Decoupled fuzzy sliding-mode control
IEEE Transactions on Fuzzy Systems
ROCOM'11/MUSP'11 Proceedings of the 11th WSEAS international conference on robotics, control and manufacturing technology, and 11th WSEAS international conference on Multimedia systems & signal processing
International Journal of Intelligent Mechatronics and Robotics
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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.