A course in fuzzy systems and control
A course in fuzzy systems and control
Robust self-learning fuzzy controller design for a class of nonlinear MIMO systems
Fuzzy Sets and Systems
Indirect adaptive fuzzy sliding mode control: Part I: fuzzy switching
Fuzzy Sets and Systems
Stable adaptive control of fuzzy dynamic systems
Fuzzy Sets and Systems - Modeling and control
Observer-based adaptive fuzzy-neural control for unknown nonlineardynamical systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive fuzzy sliding mode control of nonlinear system
IEEE Transactions on Fuzzy Systems
Fuzzy control design for the trajectory tracking on uncertain nonlinear systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems
IEEE Transactions on Fuzzy Systems
Fuzzy adaptive observer backstepping control for MIMO nonlinear systems
Fuzzy Sets and Systems
An adaptive fuzzy sliding mode controller for remotely operated underwater vehicles
Robotics and Autonomous Systems
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This paper proposes an observer-based indirect adaptive fuzzy sliding mode controller with state variable filters for a certain class of unknown nonlinear dynamic systems in which not all the states are available for measurement. To design the proposed controller, we first construct the fuzzy models to describe the input/output behavior of the nonlinear dynamic system. Then, an observer is employed to estimate the tracking error vector. Based on the observer, a fuzzy sliding model controller is developed to achieve the tracking performance. Then, a filtered observation error vector is obtained by passing the observation error vector to a set of state variable filters. Finally, on the basis of the filtered observation error vector, the adaptive laws are proposed to adjust the free parameters of the fuzzy models. The stability of the overall control system is analyzed based on the Lyapunov method. Simulation results illustrate the design procedures and demonstrate the tracking performance of the proposed controller.