Evolution of Voronoi based fuzzy recurrent controllers
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
International Journal of Computer Applications in Technology
Design of robust fuzzy neural network controller with reduced rule base
International Journal of Hybrid Intelligent Systems
Optimal adaptive fuzzy control for a class of unknown nonlinear systems
WSEAS Transactions on Systems and Control
Adaptive Backstepping Fuzzy Control for a Class of Nonlinear Systems
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
DSP-based PMSM drive design for electric injection moulding machines
International Journal of Computer Applications in Technology
Aspects regarding the fuzzy logic in the process control from the sintering plants
ICS'09 Proceedings of the 13th WSEAS international conference on Systems
IEEE Transactions on Neural Networks
Adaptive fuzzy dead-zone control for unknown nonlinear systems
ACC'09 Proceedings of the 2009 conference on American Control Conference
Direct adaptive fuzzy control for a class of nonlinear systems based on integral Lyapunov functions
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 6
Fuzzy-approximation-based adaptive control of strict- feedback nonlinear systems with time delays
IEEE Transactions on Fuzzy Systems
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Stable adaptive fuzzy control for MIMO nonlinear systems
Computers & Mathematics with Applications
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We present a combined direct and indirect adaptive control scheme for adjusting an adaptive fuzzy controller, and adaptive fuzzy identification model parameters. First, using adaptive fuzzy building blocks, with a common set of parameters, we design and study an adaptive controller and an adaptive identification model that have been proposed for a general class of uncertain structure nonlinear dynamic systems. We then propose a hybrid adaptive (HA) law for adjusting the parameters. The HA law utilizes two types of errors in the adaptive system, the tracking error and the modeling error. Performance analysis using a Lyapunov synthesis approach proves the superiority of the HA law over the direct adaptive (DA) method in terms of faster and improved tracking and parameter convergence. Furthermore, this is achieved at negligible increased implementation cost or computational complexity. We prove a theorem that shows the properties of this hybrid adaptive fuzzy control system, i.e., bounds for the integral of the squared errors, and the conditions under which these errors converge asymptotically to zero are obtained. Finally, we apply the hybrid adaptive fuzzy controller to control a chaotic system, and the inverted pendulum system