System identification: theory for the user
System identification: theory for the user
Fuzzy self-organizing controller and its application for dynamic processes
Fuzzy Sets and Systems - Fuzzy Control
An introduction to fuzzy control
An introduction to fuzzy control
Digital control system analysis and design (3rd ed.)
Digital control system analysis and design (3rd ed.)
Fuzzy logic techniques for navigation of several mobile robots
Applied Soft Computing
Fuzzy Sliding Mode Control for Robot Based on Passivity Theory
ISCID '08 Proceedings of the 2008 International Symposium on Computational Intelligence and Design - Volume 01
Fractional Fuzzy Adaptive Sliding-Mode Control of a 2-DOF Direct-Drive Robot Arm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Paper: A linguistic self-organizing process controller
Automatica (Journal of IFAC)
Adaptive fuzzy controller with sliding surface for vehicle suspension control
IEEE Transactions on Fuzzy Systems
Robust control of a spatial robot using fuzzy sliding modes
Mathematical and Computer Modelling: An International Journal
Adaptive fuzzy sliding mode control for electro-hydraulic servo mechanism
Expert Systems with Applications: An International Journal
Novel Adaptive Charged System Search algorithm for optimal tuning of fuzzy controllers
Expert Systems with Applications: An International Journal
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It is difficult to determine the stability of a self-organizing fuzzy controller (SOFC). Therefore, a system controlled using the SOFC cannot guarantee the stability of the system during the control process. To eliminate the problem caused by the SOFC, this study developed an enhanced self-organizing fuzzy sliding-mode controller (EASFSC) for robotic systems. Instead of using the system's output error and its error change, the EASFSC uses a sliding surface and its differentiation as the input variables of a fuzzy logic controller (FLC) in the SOFC. Using the fuzzy operation, these variables generate a control input that ensures the stability of the system. The proposed method also employs an adaptive law to modify the fuzzy consequent parameter of the FLC in the SOFC to improve the control performance of the system. The stability of the EASFSC has been proven using the Lyapunov stability theorem. Simulation results of a two-link robotic manipulator application verified that the EASFSC provides superior control performance as compared with the SOFC.