Data-Driven Optimized Tracking Control Heuristic for MIMO Structures: A Balance System Case Study

• URL: http://arxiv.org/abs/2104.00199v1
• Date: Thu, 1 Apr 2021 02:00:20 GMT
• Title: Data-Driven Optimized Tracking Control Heuristic for MIMO Structures: A Balance System Case Study
• Authors: Ning Wang, Mohammed Abouheaf, Wail Gueaieb
• Abstract summary: The PID is illustrated on a two-input two-output balance system. It integrates a self-adjusting nonlinear threshold with a neural network to compromise between the desired transient and steady state characteristics. The neural network is trained upon optimizing a weighted-derivative like objective cost function.
• Score: 8.035375408614776
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A data-driven computational heuristic is proposed to control MIMO systems without prior knowledge of their dynamics. The heuristic is illustrated on a two-input two-output balance system. It integrates a self-adjusting nonlinear threshold accepting heuristic with a neural network to compromise between the desired transient and steady state characteristics of the system while optimizing a dynamic cost function. The heuristic decides on the control gains of multiple interacting PID control loops. The neural network is trained upon optimizing a weighted-derivative like objective cost function. The performance of the developed mechanism is compared with another controller that employs a combined PID-Riccati approach. One of the salient features of the proposed control schemes is that they do not require prior knowledge of the system dynamics. However, they depend on a known region of stability for the control gains to be used as a search space by the optimization algorithm. The control mechanism is validated using different optimization criteria which address different design requirements.

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