Unsupervised Machine Learning Control Techniques for Solving the General Synthesis of Control System Problem

The focus of this study is a relatively new development in the field of control: machine learning control. This study offers a mathematical framework for machine learning control and explores three symbolic regression-based techniques for supervised and unsupervised learning. One of the challenges associated with machine learning control pertains to the control general synthesis. This entails figuring out a control function contingent upon the object’s state, guaranteeing the attainment of the control objective while optimizing the quality criterion value across all possible initial states within a permissible zone where finding a satisfactory solution occurs within the space of codes. The implementation of the small variations principle within the basic solution is suggested as a viable technique for developing the algorithms of search. This paper extensively discusses three symbolic regression techniques, including Cartesian genetic programming (CGP), synthesized genetic programming (SGP) and parse-matrix evolution (PME). Notably, synthesized genetic programming, being a novel technique, and PME get utilized for the first time to address the general synthesis of control problems. The mathematical expression’s SGP code is a six-row integer matrix; the first row of the matrix represents the functions that take two arguments, while the second and fourth rows represent the functions that take one argument. The third and fifth rows represent the arguments of the mathematical expression, and the sixth row represents the priority. The computational example demonstrates the potential of symbolic regression approaches as unsupervised machine learning control techniques for addressing the machine learning control challenge of general synthesis of control in order to achieve the stability of a mobile robot system. Likewise, practical experience shows that synthesized genetic programming has faster efficiency than Cartesian genetic programming and parse-matrix evolution in discovering solutions, about 2.33 and 2.11 times on average, respectively. © (2024), (Intelligent Network and Systems Society). All Rights Reserved.