Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Fuzzy-neural control: principles, algorithms and applications
Fuzzy-neural control: principles, algorithms and applications
Large-scale systems: modeling, control, and fuzzy logic
Large-scale systems: modeling, control, and fuzzy logic
Neural Fuzzy Control Systems with Structure and Parameter Learning
Neural Fuzzy Control Systems with Structure and Parameter Learning
Conventional fuzzy control and its enhancement
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A method for design of a hybrid neuro-fuzzy control system based on behavior modeling
IEEE Transactions on Fuzzy Systems
Parameter Tuning of Stable Fuzzy Controllers
Journal of Intelligent and Robotic Systems
Design and Implementation of Fuzzy Sliding-Mode Controller for a Wedge Balancing System
Journal of Intelligent and Robotic Systems
Design of interval type-2 fuzzy sliding-mode controller
Information Sciences: an International Journal
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Neural Processing Letters
International Journal of Intelligent Systems Technologies and Applications
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This paper derives and analyzes a new robust fuzzy-logic sliding-mode controller of the diagonal type, which does not need the prior design of the rule base. The basic objective of the controller is to keep the system on the sliding surface so as to ensure the asympotic stability of the closed-loop system. The control law consists of two rules: (i) IF sign(e(t)ė(t)) 0 THEN change the control action, where e(t) = x(t) − x^d(t) is the system state error, and the control action can be either an increase or decrease of the control signal, which is realized through the use of fuzzy rules. The proposed controller, which does not need the prior knowledge of the system model and the prior design of the membership functions" shape, was tested, by simulation, on linear and nonlinear systems. The performance was in all cases excellent (very fast trajectory tracking, no chattering) . Of course, as in traditional control, there was a trade-off between the rise-time and the overshoot of the system response.