Genetic algorithms for fuzzy controllers
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Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
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
Stable adaptive fuzzy controller with time-varying dead-zone
Fuzzy Sets and Systems - Special issue on formal methods for fuzzy modeling and control
Indirect adaptive fuzzy sliding mode control: Part I: fuzzy switching
Fuzzy Sets and Systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Stable adaptive control of fuzzy dynamic systems
Fuzzy Sets and Systems - Modeling and control
Decoupled control using neural network-based sliding-mode controller for nonlinear systems
Expert Systems with Applications: An International Journal
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
Fuzzy adaptive sliding-mode control for MIMO nonlinear systems
IEEE Transactions on Fuzzy Systems
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
Stability analysis and robustness design of nonlinear systems: An NN-based approach
Applied Soft Computing
Adaptive fuzzy sliding mode control for electro-hydraulic servo mechanism
Expert Systems with Applications: An International Journal
Evolutionary optimization-based tuning of low-cost fuzzy controllers for servo systems
Knowledge-Based Systems
Computers and Industrial Engineering
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In this paper, the stability analysis of the GA-based adaptive fuzzy sliding model controller for a nonlinear system is presented. First, an uncertain and nonlinear plant for the tracking of a reference trajectory is well approximated and described via the reference model and the fuzzy model involving fuzzy logic control rules. Next, the difficulty in designing a fuzzy sliding mode controller (FSMC) capable of rapidly and efficiently controlling complex and nonlinear systems is how to select the most appropriate initial values for the parameter vector. The initial values of the consequent parameter vector are decided via the genetic algorithm. After this, a modified adaptive law can be adopted to find the best high-performance parameters for the fuzzy sliding model controller. The adaptive fuzzy sliding model controller is derived to simultaneously stabilize and control the system. The stability of the nonlinear system is ensured by the derivation of the stability criterion based upon Lyapunov's direct method. Finally, a numerical simulation is provided as an example to demonstrate the control methodology.