A stability approach to fuzzy control design for nonlinear systems
Fuzzy Sets and Systems
Robust control by fuzzy sliding mode
Automatica (Journal of IFAC)
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
Quasi time—optimal PID control of multivariable systems: a seesaw example
Journal of the Chinese Institute of Engineers - Chinese speech and language processing
Design of a fuzzy gain scheduler using sliding mode control principles
Fuzzy Sets and Systems - Special issue on formal methods for fuzzy modeling and control
A Simple Robust Sliding-Mode Fuzzy-Logic Controller of the Diagonal Type
Journal of Intelligent and Robotic Systems
Genetic Tuning of PID Controllers Using a Neural Network Model: A Seesaw Example
Journal of Intelligent and Robotic Systems
Parameter Tuning of Stable Fuzzy Controllers
Journal of Intelligent and Robotic Systems
Stable adaptive fuzzy controllers with application to inverted pendulum tracking
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Designing fuzzy controllers from a variable structures standpoint
IEEE Transactions on Fuzzy Systems
MIMO adaptive fuzzy terminal sliding-mode controller for robotic manipulators
Information Sciences: an International Journal
Fuzzy sliding mode autopilot design for nonminimum phase and nonlinear UAV
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Recent Advances in Soft Computing: Theories and Applications
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In this paper, we address the design and implementation of fuzzy sliding-mode controller for balancing a wedge system. At first, we examine the mathematical model of the wedge balancing system. The dynamic system is complex and ill defined; hence we propose the fuzzy sliding-mode control (FSMC) method to achieve the control objective. The proposed control method enhances the ability of fuzzy logic control so that the minimal number of fuzzy inference rules is systematically obtained even the plant parameters are unknown. Both computer simulations and real-time experiments are exploited to demonstrate the validity and feasibility of the developed control scheme.