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Decentralized adaptive fuzzy neural iterative learning control for nonaffine nonlinear interconnected systems. (English) Zbl 1248.93104

Summary: We study the design of iterative learning controllers for nonaffine nonlinear interconnected systems with repeatable control tasks. The interaction between each subsystem can be of general type of unknown nonlinear functions if a bounding condition is satisfied. An error model is derived such that only local subsystem information is required for the controller design. An adaptive iterative learning controller for each subsystem is constructed based on a fuzzy neural learning component and a robust learning component. The fuzzy neural learning component designed by an output recurrent fuzzy neural network is utilized to approximate the system nonaffine nonlinearities and interconnections. The approximation error due to the fuzzy neural learning component will be then compensated by a robust learning component. Stable adaptive laws are derived to update the control parameters in order to guarantee the stability and convergence. We show that the internal signals are bounded during the learning process and the state tracking errors of each subsystem converge asymptotically along the iteration axis to a tunable residual set.

MSC:

93C42 Fuzzy control/observation systems
93C40 Adaptive control/observation systems
93B51 Design techniques (robust design, computer-aided design, etc.)
68T05 Learning and adaptive systems in artificial intelligence
93A14 Decentralized systems
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References:

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