TY - GEN
T1 - An observer-based adaptive type-2 fuzzy-neural controller for a class of MIMO systems with uncertainties
AU - Li, I. Hsum
AU - Lee, Lian Wang
AU - Wang, Wei Yen
AU - Hsu, Kai Wei
PY - 2011
Y1 - 2011
N2 - An observer-based adaptive controller based on a type-2 fuzzy neural network (type-2 FNN) is developed for a class of multi-input multi-output (MIMO) nonaffine nonlinear system. The interval type-2 fuzzy system is proposed in this paper as an alternative solution when a MIMO system has a large amount of uncertainties or the training data is corrupted by noise. By using implicit function theorem and Lyapunov theorem, the observer-based control law and the weight update law of the adaptive type-2 FNN controller are derived. Based on the design of the type-2 fuzzy neural network, the observer-based adaptive controller can improve its robustness to noise. In this paper, we prove that the proposed observer-based adaptive controller can guarantee that all signals involved are bounded and the outputs of the closed-loop system asymptotically track the desired output trajectories. Simulations results are reported to show the performance of the proposed control system mode and algorithms.
AB - An observer-based adaptive controller based on a type-2 fuzzy neural network (type-2 FNN) is developed for a class of multi-input multi-output (MIMO) nonaffine nonlinear system. The interval type-2 fuzzy system is proposed in this paper as an alternative solution when a MIMO system has a large amount of uncertainties or the training data is corrupted by noise. By using implicit function theorem and Lyapunov theorem, the observer-based control law and the weight update law of the adaptive type-2 FNN controller are derived. Based on the design of the type-2 fuzzy neural network, the observer-based adaptive controller can improve its robustness to noise. In this paper, we prove that the proposed observer-based adaptive controller can guarantee that all signals involved are bounded and the outputs of the closed-loop system asymptotically track the desired output trajectories. Simulations results are reported to show the performance of the proposed control system mode and algorithms.
KW - Fuzzy Neural control
KW - Interval type-2 Fuzzy Systems
KW - MIMO nonaffine systems
UR - http://www.scopus.com/inward/record.url?scp=80053582947&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:80053582947
SN - 9780955529375
T3 - 2011 International Conference on Advanced Mechatronic Systems, ICAMechS 2011 - Final Program
SP - 540
EP - 545
BT - 2011 International Conference on Advanced Mechatronic Systems, ICAMechS 2011 - Final Program
T2 - 2011 International Conference on Advanced Mechatronic Systems, ICAMechS 2011
Y2 - 11 August 2011 through 13 August 2011
ER -