Affiliation:
1. Faculty of Mechanical Engineering and Robotics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059 Kraków, Poland
Abstract
This article presents a neural algorithm based on Reinforcement Learning for optimising Linear Quadratic Regulator (LQR) creation. The proposed method allows designing such a target function that automatically leads to changes in the quality and resource matrix so that the target LQR regulator achieves the desired performance. The solution’s stability and optimality are the target controller’s responsibility. However, the neural mechanism allows obtaining, without expert knowledge, the appropriate Q and R matrices, which will lead to such a gain matrix that will realise the control that will lead to the desired quality. The presented algorithm was tested for the derived quadrant model of the suspension system. Its application improved user comfort by 67% compared to the passive solution and 14% compared to non-optimised LQR.
Funder
AGH University of Science and Technology
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
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