Multicriteria Optimization of Stochastic Robust Control of the Tracking System

Abstract

A multicriteria optimization of stochastic robust control with two degrees of freedom of a tracking system with anisotropic regulators has been developed to increase accuracy and reduce sensitivity to uncertain object parameters. Such objects are located on a moving base, on which sensors for angles, angular velocities and angular accelerations are installed. Improvements in the accuracy of control with two degrees of freedom include closed-loop feedback control and open-loop feedback control through the use of reference and perturbation effects. The multicriteria optimization of the stochastic robust control tracking system with two degrees of freedom with anisotropic controllers is reduced to the iterative solution of a system of four coupled Riccati equations, the Lyapunov equation, and the determination of the anisotropy norm of the system by an expression of a special form, which is numerically solved using the homotopy method, which includes vectorization matrices and iterations according to Newton's method. The objective vector of robust control is calculated in the form of a solution of a vector game, the vector gains of which are direct indicators of the quality that the system should achieve in different modes of its operation. The calculation of the vector gains of this game is related to the simulation of a synthesized system with anisotropic regulators for different modes of operation with different input signals and object parameter values. The solutions of this vector game are calculated on the basis of a set of Pareto-optimal solutions taking into account the binary relations of preferences on the basis of the metaheuristic algorithm of multi-swarm Archimedes optimization. Based on the results of the synthesis of stochastic robust control of a tracking system with two degrees of freedom with anisotropic controllers, it is shown that the use of synthesized controllers made it possible to increase the accuracy of system control, reduce the time of transient processes by 3–5 times, reduce the variance of errors by 2.7 times, and reduce the sensitivity of the system to the change of object parameters compared to typical regulators.