Adaptive Control Design under Structured Model Information Limitation: A Cost-Biased Maximum-Likelihood Approach

TL;DR

This paper introduces an adaptive control strategy that, despite limited plant model information, asymptotically matches the performance of the optimal centralized controller for most plants, improving control design under information constraints.

Contribution

It demonstrates that an adaptive controller can asymptotically achieve optimal performance with limited model information, reducing the competitive ratio to one.

Findings

Adaptive controller asymptotically matches full-information optimal control

Achieves competitive ratio of one for most plants

Numerical validation on vehicle platooning problem

Abstract

Networked control strategies based on limited information about the plant model usually results in worse closed-loop performance than optimal centralized control with full plant model information. Recently, this fact has been established by utilizing the concept of competitive ratio, which is defined as the worst case ratio of the cost of a control design with limited model information to the cost of the optimal control design with full model information. We show that an adaptive controller, inspired by a controller proposed by Campi and Kumar, with limited plant model information, asymptotically achieves the closed-loop performance of the optimal centralized controller with full model information for almost any plant. Therefore, there exists, at least, one adaptive control design strategy with limited plant model information that can achieve a competitive ratio equal to one. The plant model considered in the paper belongs to a compact set of stochastic linear time-invariant systems and the closed loop performance measure is the ergodic mean of a quadratic function of the state and control input. We illustrate the applicability of the results numerically on a vehicle platooning problem.