Long-time Average Cost Control of Polynomial Systems: A Sum-of-squares-based Small-feedback Approach

TL;DR

This paper introduces a sum-of-squares-based small-feedback control method for polynomial systems, providing a proof of concept and overcoming non-convexity in long-time average cost optimization.

Contribution

It presents a novel small-feedback approach that simplifies the non-convex optimization problem in long-time average cost control of polynomial systems.

Findings

Effective control design for a vortex shedding model.

Sum-of-squares relaxation yields good bounds on long-time average cost.

Sequential SOS problems avoid non-convexity issues.

Abstract

The two main contributions of this paper are a proof of concept of the recent novel idea in the area of long-time average cost control, and a new method of overcoming the well-known difficulty of non-convexity of simultaneous optimization of a control law and an additional tunable function. A recently-proposed method of obtaining rigorous bounds of long-time average cost is first outlined for the uncontrolled system with polynomials of system state on the right-hand side. In this method the polynomial constraints are relaxed to be sum-of-squares and formulated as semi-definite programs. It was proposed to use the upper bound of long-time average cost as the objective function instead of the time-average cost itself in controller design. In the present paper this suggestion is implemented for a particular system and is shown to give good results. Designing the optimal controller by this method requires optimising simultaneously both the control law and a tunable function similar to the Lyapunov function. The new approach proposed and implemented in this paper for overcoming the inherent non-convexity of this optimisation is based on a formal assumption that the amplitude of control is small. By expanding the tunable function and the bound in the small parameter, the long-time average cost is reduced by minimizing the respective bound in each term of the series. The derivation of all the polynomial coefficients in controller is given in terms of the solvability conditions of state-dependent linear and bilinear inequalities. The resultant sum-of-squares problems are solved in sequence, thus avoiding the non-convexity in optimization. The proposed approach is implemented for a simple model of oscillatory vortex shedding behind a cylinder.