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arXiv · 2308.10851

Learning in Dynamic Systems and Its Application to Adaptive PID Control

Omar Makke·Feng Lin
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Abstract

Deep learning using neural networks has revolutionized machine learning and put artificial intelligence into everyday life. In order to introduce self-learning to dynamic systems other than neural networks, we extend the Brandt-Lin learning algorithm of neural networks to a large class of dynamic systems. This extension is possible because the Brandt-Lin algorithm does not require a dedicated step to back-propagate the errors in neural networks. To this end, we first generalize signal-flow graphs so that they can be used to model nonlinear systems as well as linear systems. We then derive the extended Brandt-Lin algorithm that can be used to adapt the weights of branches in generalized signal-flow graphs. We show the applications of the new algorithm by applying it to adaptive PID control. In particular, we derive a new adaptation law for PID controllers. We verify the effectiveness of the method using simulations for linear and nonlinear plants, stable as well as unstable plants.

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Omar Makke, Feng Lin. 2023-08-21. Learning in Dynamic Systems and Its Application to Adaptive PID Control. https://arxiv.org/abs/2308.10851

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