1 Introduction
1.1 About modern control systems
1.2 About machine learning control
1.3 About symbolic regression methods
References
2 Mathematical Statements of MLC Problems
2.1 Machine Learning Problem
2.2 Optimal Control Problem
2.3 Control Synthesis Problem
2.4 Synthesized Optimal Control Problem
2.5 Model Identification Problem
References
3 Numerical Solution of Machine Learning Control Problems
3.1 Artificial Neural Networks
3.2 General Approach of Symbolic Regression
3.3 The principle of small variations of the basic solution
3.4 Genetic Algorithm for Multicriterial Structural-Parametric Search of Functions
3.5 Space of Machine-Made Functions
Appendix
References
4 Symbolic Regression Methods
4.1 Genetic Programming
4.2 Grammatical Evolution
4.3 Cartesian Genetic Programming
4.4 Inductive Genetic Programming
4.5 Analytic Programming
4.6 Parse-Matrix Evolution
4.7 Binary Complete Genetic Programming
4.8 Network Operator Method
4.9 Variational Symbolic Regression Methods
4.9.1 Variational Genetic Programming
4.9.2 Variational Analytic Programming
4.9.3 Variational Binary Complete Genetic Programming
4.9.4 Variational Cartesian Genetic Programming
4.10 Multilayer Symbolic Regression Methods
References
5 Examples of MLC Problem Solutions
5.1 Control Synthesis as Unsupervised MLC
5.1.1 Ponryagin's Example
5.1.2 Mobile Robot
5.1.3 Quadcopter
5.2 Control Synthesis as Supervised MLC
5.3 Identification and Control Synthesis for Multi-link Robot
5.4 Synthesized Optimal Control Example
5.4.1 Synthesized optimal control
5.4.2 Direct solution of the optimal control problem
5.4.3 Experimental analysis of sensitivity to perturbations
5.5 Machine learning in Synergetic control
References
