Model-based learning control with non-repetitive initial conditions
by Orest V. Iftime, Michel Verhaegen
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 2, No. 2/3, 2007

Abstract: In this paper we will focus on model-based learning control for discrete, time-varying, linear systems with non-repetitive initial state. We assume that the disturbances are repetitive in trial-domain. A particular form for the learning input update law is considered and the stability of a general learning input law is analysed. The refinement of the inputs design based on the learning concepts with non-repetitive initial state has the potential to enhance the performances of tracking control. Mechanical systems, chemical and other manufacturing processes such as batch distillation, heat treatment processes for metallic or ceramic products can benefit from the analysis performed in this paper.

Online publication date: Mon, 19-Feb-2007

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