Control Synthesis as Machine Learning Control by Symbolic Regression Methods

Abstract

The problem of control synthesis is considered as machine learning control. The paper proposes a mathematical formulation of machine learning control, discusses approaches of supervised and unsupervised learning by symbolic regression methods. The principle of small variation of the basic solution is presented to set up the neighbourhood of the search and to increase search efficiency of symbolic regression methods. Different symbolic regression methods such as genetic programming, network operator, Cartesian and binary genetic programming are presented in details. It is shown on the computational example the possibilities of symbolic regression methods as unsupervised machine learning control technique to the solution of MLC problem of control synthesis for obtaining the stabilization system for a mobile robot.

Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives

“…to reach or preserve a certain status [ 183 ]. In this regard, several studies have equipped SR to generate analytic functions towards a control system design [ 128 , 184 ].…”

Model-free tracking control of complex dynamical trajectories with machine learning

“…For regression-based control design of the second kind, machine learning is exploited to identify arbitrary nonlinear control laws that minimize the cost function of the system. In this case, it is not necessary to know the model, control law structure, or the optimizing actuation command, and optimization is solely based on the measured control performance (cost function), for which genetic programming represents an effective regression technique [23,24]. For reinforcement learning, the control law can be continually updated over measured performance changes based on rewards [25][26][27][28][29][30][31][32].…”