Multivariable structure of fuzzy control systems
IEEE Transactions on Systems, Man and Cybernetics
Probabilistic fuzzy controller as a generalization of the concept of fuzzy controller
Fuzzy Sets and Systems - Fuzzy Control
Structure of multivariable fuzzy control systems with a coordinator
Fuzzy Sets and Systems - Special issue on fuzzy control
Design of a multi-layer fuzzy logic controller for multi-input multi-output systems
Fuzzy Sets and Systems
A mixed fuzzy controller for MIMO systems
Fuzzy Sets and Systems
Fuzzy Control: Synthesis and Analysis
Fuzzy Control: Synthesis and Analysis
Stability Issues in Fuzzy Control
Stability Issues in Fuzzy Control
Construction of fuzzy systems using least-squares method and genetic algorithm
Fuzzy Sets and Systems - Theme: Modeling and control
Some remarks on the stability of Mamdani fuzzy control systems
IEEE Transactions on Fuzzy Systems
Design of a multilevel fuzzy controller for nonlinear systems and stability analysis
IEEE Transactions on Fuzzy Systems
An index of applicability for the decomposition method of multivariable fuzzy systems
IEEE Transactions on Fuzzy Systems
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This paper presents a systematic design procedure of a multivariable fuzzy controller for a general Multi-Input Multi-Output (MIMO) nonlinear system with an input-output monotonic relationship or a piecewise monotonic relationship for each input-output pair. Firstly, the system is modeled as a Fuzzy Basis Function Network (FBFN) and its Relative Gain Array (RGA) is calculated based on the obtained fuzzy model. The proposed multivariable fuzzy controller is constructed with two orthogonal fuzzy control engines. The horizontal fuzzy control engine for each system input-output pair has a hierarchical structure to update the control parameters online and compensate for unknown system variations. The perpendicular fuzzy control engine is designed based on the system RGA to eliminate the multivariable interaction effect. The resultant closed-loop fuzzy control system is proved to be passive stable as long as the augmented open-loop system is input-output passive. Two sets of simulation examples demonstrate that the proposed fuzzy control strategy can be a promising way in controlling multivariable nonlinear systems with unknown system uncertainties and time-varying parameters.