Neural based adaptive control of a class of dynamical nonlinear processes
ICAI'06 Proceedings of the 7th WSEAS International Conference on Automation & Information
Adaptive fuzzy tracking control of nonlinear systems
WSEAS Transactions on Systems and Control
Direct adaptive control of an anaerobic depollution bioprocess using radial basis neural networks
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
Engineering Applications of Artificial Intelligence
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A direct adaptive control strategy for a class of single-input/single-output nonlinear systems is presented. The major advantage of the proposed method is that a detailed dynamic nonlinear model is not required for controller design. The only information required about the plant is measurements of the state variables, the relative degree, and the sign of a Lie derivative which appears in the associated input-output linearizing control law. Unknown controller functions are approximated using locally supported radial basis functions that are introduced only in regions of the state space where the closed-loop system actually evolves. Lyapunov stability analysis is used to derive parameter update laws which ensure (under certain assumptions) the state vector remains bounded and the plant output asymptotically tracks the output of a linear reference model. The technique is successfully applied to a nonlinear biochemical reactor model