Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Discrete-time control systems (2nd ed.)
Discrete-time control systems (2nd ed.)
A course in fuzzy systems and control
A course in fuzzy systems and control
Direct adaptive fuzzy control with a self-structuring algorithm
Fuzzy Sets and Systems
Stable indirect adaptive control based on discrete-time T--S fuzzy model
Fuzzy Sets and Systems
An approach to online identification of Takagi-Sugeno fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
T-S model based indirect adaptive fuzzy control using online parameter estimation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
General SISO Takagi-Sugeno fuzzy systems with linear rule consequent are universal approximators
IEEE Transactions on Fuzzy Systems
Online adaptive fuzzy neural identification and control of a class of MIMO nonlinear systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
| Hi-index | 0.00 |
This paper presents a nonlinear identification and indirect control algorithm based on a self-structuring fuzzy system (SFS) with guaranteed stability. The overall controller consists of two parts: the indirect adaptive controller based on the self-structuring fuzzy system (IACSFS) is the dominant controller which maintains the closed-loop stability when the fuzzy system is a good approximation of the nonlinear plant. A supervisory controller is an auxiliary controller which isactivated when the tracking error reaches the boundary of a predefined constraint set. The supervisory controller helps generate useful data and allows enough time for the fuzzy system to learn and improve through online adding new rules, replacing or deleting old rules and tune the parameters of rules according the latest on-line data. When the fuzzy system regains good approximation through learning and the model based main controller is capable of maintain system stability, the supervisory controller is idle. It is proven that the overall adaptive control scheme with the IACSFS and the supervisory controller guarantees the global stability in the sense that all the closed-loop signals are bounded. The effectiveness of the proposed control scheme is demonstrated through simulation.