Trajectory stabilization of a model car via fuzzy control
Fuzzy Sets and Systems - Special issue on modern fuzzy control
Automatica (Journal of IFAC)
Analysis and design for a class of complex control systems part II: fuzzy controller design
Automatica (Journal of IFAC)
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Fuzzy Control Systems Design and Analysis: A Linear Matrix Inequality Approach
Switching control of an R/C hovercraft: stabilization and smoothswitching
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An approach to fuzzy control of nonlinear systems: stability and design issues
IEEE Transactions on Fuzzy Systems
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Analysis and design of fuzzy controller and fuzzy observer
IEEE Transactions on Fuzzy Systems
Optimal fuzzy controller design: local concept approach
IEEE Transactions on Fuzzy Systems
Optimal fuzzy controller design in continuous fuzzy system: global concept approach
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Discrete-time optimal fuzzy controller design: global concept approach
IEEE Transactions on Fuzzy Systems
Affine TS-model-based fuzzy regulating/servo control design
Fuzzy Sets and Systems
Fuzzy hyperbolic neural network with time-varying delays
Fuzzy Sets and Systems
Neural, Parallel & Scientific Computations
Differences between t-norms in fuzzy control
International Journal of Intelligent Systems
Hi-index | 0.20 |
A neural-learning fuzzy technique is proposed for T-S fuzzy-model identification of model-free physical systems. Further, an algorithm with a defined modelling index is proposed to integrate and to guarantee that the proposed neural-based optimal fuzzy controller can stabilize physical systems; the modelling index is defined to denote the modelling-error evolution, and to ensure that the training data for neural learning can describe the physical system behavior very well; the algorithm, which integrates the neural-based fuzzy modelling and optimal fuzzy controlling process, can implement off-line modelling and on-line optimal control for model-free physical systems. The neural-fuzzy inference network is a self-organizing inference system to learn fuzzy membership functions and fuzzy-subsystems' parameters as data feeding in. Based on the generated T-S fuzzy models for the continuous mass-spring-damper system and Chua's chaotic circuit, discrete-time model car system and articulated vehicle, their corresponding fuzzy controllers are formulated from both local-concept and global-concept fuzzy approach, respectively. The simulation results demonstrate the performance of the proposed neural-based fuzzy modelling technique and of the integrated algorithm of neural-based optimal fuzzy control structure.