Data-Driven Optimized Tracking Control Heuristic for MIMO Structures: A Balance System Case Study
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.
Cite
@article{arxiv.2104.00199,
title = {Data-Driven Optimized Tracking Control Heuristic for MIMO Structures: A Balance System Case Study},
author = {Ning Wang and Mohammed Abouheaf and Wail Gueaieb},
journal= {arXiv preprint arXiv:2104.00199},
year = {2021}
}