S. Kamalasadan (USA)
Multimodal and Uncertain systems, Composite Parallel Intelligent Controller, Radial Basis Function Neural Network, Fuzzy Multiple Model Generator.
This paper presents theoretical formulation of a new intelligent controller approach for parametric, functionally uncertain and multi-modal systems based on a supervisory loop approach. The scheme consists of three software agents that work in an autonomous manner for the precision control of such nonlinear dynamic systems. The first agent is a neuro-controller that monitors the system closed loop error for functional uncertainty. The second agent is an adaptive controller which controls the system dynamics when parametric change occur and helps to keep it inbound. The third one is a fuzzy reference model generator that works online to generate stable desired reference models when the system shows drastic modal changes. The theoretical formulations and subsequently the stability of the overall scheme based on a Lyapunov function candidate are analyzed in this part, thus illustrating the strengths of the proposed concept.
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