Fuzzy Rule Based Control System Research Articles

Robust synchronization of chaotic systems with novel fuzzy rule-based controllers

This paper proposes a set of novel fuzzy controllers with adjustable control forces for the robust synchronization of different chaotic systems that include uncertain terms or unexpected disturbances. The proposed fuzzy rule-based control system is developed through Lyapunov stability theory and the fuzzy logic concept to form a multi-input multi-output (MIMO) system. This system provides flexible control inputs based on the error dynamics of the system, and the inputs satisfy the synchronization condition of being asymptotically stable. In this case, the error derivatives always have signs opposite those of the error states, which yields negative definite first-order Lyapunov function derivatives. Furthermore, the proposed system appropriately adjusts the control inputs through the constructed fuzzy if-then rules. This method is robust to both unmodeled terms and unexpected interference signals during the synchronization process. The simulation results demonstrate the feasibility and effectiveness of the proposed fuzzy rule-based control system.

Adaptive Fuzzy Control System for a Squirrel Cage Induction Motor

A fuzzy adaptive method is designed and implemented in order to improve the response of a scalar fuzzy control system for the speed of an induction motor. The adaptive method determines the set of output membership functions used in the difuzzification process of a typical fuzzy rule based control system. The universe, domain and distribution of the controller's output membership functions are defined at the beginning of every control cycle, thus having a dynamic generation of the set of output membership functions. In this paper, this method is applied to an induction motor in order to control its speed. The experimental results are compared to those obtained with a non-adaptive fuzzy controller, showing an improvement in torque, stator currents and voltages as well as some dynamic parameters such as steady state ripple.

A Hierarchical Fuzzy-Neural Multi-Model Applied in Nonlinear Systems Identification and Control

The paper brings forward a hierarchical fuzzy-neural multi-model with recurrent neural procedural consequent par for systems identification, states estimation and adaptive control of complex nonlinear plants. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive trajectory tracking control systems design. The designed local control laws are coordinated by a fuzzy rule-based control system. The upper level defuzzyfication is performed by a recurrent neural network. The applicability of the proposed intelligent control system is confirmed by simulation examples and by a DC-motor identification and control experimental results. Two main cases of a reference and plant output fuzzyfication are considereda two membership functions without overlapping and a three membership functions with overlapping. In both cases a good convergent results are obtained.

A fuzzy-neural multi-model for nonlinear systems identification and control

The paper proposed to apply a hierarchical fuzzy-neural multi-model and Takagi–Sugeno (T–S) rules with recurrent neural procedural consequent part for systems identification, states estimation and adaptive control of complex nonlinear plants. The parameters and states of the local recurrent neural network models are used for a local direct and indirect adaptive trajectory tracking control systems design. The designed local control laws are coordinated by a fuzzy rule-based control system. The upper level defuzzyfication is performed by a recurrent neural network. The applicability of the proposed intelligent control system is confirmed by simulation examples and by a DC-motor identification and control experimental results. Two main cases of a reference and plant output fuzzyfication are considered—a two membership functions without overlapping and a three membership functions with overlapping. In both cases a good convergent results are obtained.