Stable indirect fuzzy adaptive control
Fuzzy Sets and Systems - Theme: Modeling and control
Information Sciences—Informatics and Computer Science: An International Journal
Adaptive fuzzy control of a class of SISO nonaffine nonlinear systems
Fuzzy Sets and Systems
Direct adaptive fuzzy control with a self-structuring algorithm
Fuzzy Sets and Systems
Brief paper: An adaptive neuro-fuzzy tracking control for multi-input nonlinear dynamic systems
Automatica (Journal of IFAC)
Direct adaptive fuzzy control for a class of MIMO nonlinear systems
International Journal of Systems Science
Robust and Adaptive Fuzzy Feedback Linearization Regulator Design
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Observer-based fuzzy adaptive control for strict-feedback nonlinear systems
Fuzzy Sets and Systems
Dynamic structure adaptive neural fuzzy control for MIMO uncertain nonlinear systems
Information Sciences: an International Journal
Fuzzy adaptive observer backstepping control for MIMO nonlinear systems
Fuzzy Sets and Systems
Perspectives of fuzzy systems and control
Fuzzy Sets and Systems
A combined backstepping and small-gain approach to robust adaptive fuzzy output feedback control
IEEE Transactions on Fuzzy Systems
Information Sciences: an International Journal
GFHM model and control for uncertain chaotic system
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Stable adaptive fuzzy control for MIMO nonlinear systems
Computers & Mathematics with Applications
International Journal of Fuzzy System Applications
Adaptive type-2 fuzzy sliding mode controller for SISO nonlinear systems subject to actuator faults
International Journal of Automation and Computing
Hi-index | 0.00 |
In this letter, stable direct and indirect adaptive controllers are presented that use Takagi-Sugeno (T-S) fuzzy systems (1985), conventional fuzzy systems, or a class of neural networks to provide asymptotic tracking of a reference signal vector for a class of continuous time multi-input multi-output (MIMO) square nonlinear plants with poorly understood dynamics. The direct adaptive scheme allows for the inclusion of a priori knowledge about the control input in terms of exact mathematical equations or linguistics, while the indirect adaptive controller permits the explicit use of equations to represent portions of the plant dynamics. We prove that with or without such knowledge the adaptive schemes can “learn” how to control the plant, provide for bounded internal signals, and achieve asymptotically stable tracking of the reference inputs. We do not impose any initialization conditions on the controllers and guarantee convergence of the tracking error to zero