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#machine learning #optimal control #robotic group #symbolic regression

Synthesized Optimal Control for a Robotic Group by Complete Binary Genetic Programming The paper continues the study of symbolic regression methods for control learning. The optimal control problem with phase constraints for a group of robots is considered. To solve the problem, the method of synthesized optimal control is used. At the first stage the stabilization problem is solved for each robot. Using a new hybrid evolutionary algorithm, built on the basis of the genetic algorithm, the particle swarm optimization and the gray wolf optimizer, stable equilibrium points are found. Next, the original optimization problem by piece-wise linear approximation of the equilibrium points is solved. In contrast to the known methods for solving the synthesis problem, the control learning by the complete binary genetic programming is used. The advantage of this approach is that the resulting control is realizable on board of mobile robots. Simulation is given for a group of two mobile robots.

The new method of symbolic regression is presented. The method is used for solution of the optimal control problem by synthesized control method. This approach includes two stages, solution of control synthesis problem and then optimal problem.