Adaptive Stochastic Predictive Control from Noisy Data: A Sampling-based Approach

TL;DR

This paper introduces an adaptive stochastic predictive control method for linear systems with unknown parameters and noise, using online sampling and set membership to handle uncertainties and ensure constraint satisfaction.

Contribution

It presents a novel sampling-based adaptive control scheme that manages uncertainties stochastically with guaranteed recursive feasibility and constraint satisfaction.

Findings

Effective handling of stochastic uncertainties in control.

Guarantees recursive feasibility and constraint satisfaction.

Demonstrated efficacy through a numerical example.

Abstract

In this work, an adaptive predictive control scheme for linear systems with unknown parameters and bounded additive disturbances is proposed. In contrast to related adaptive control approaches that robustly consider the parametric uncertainty, the proposed method handles all uncertainties stochastically by employing an online adaptive sampling-based approximation of chance constraints. The approach requires initial data in the form of a short input-output trajectory and distributional knowledge of the disturbances. This prior knowledge is used to construct an initial set of data-consistent system parameters and a distribution that allows for sample generation. As new data stream in online, the set of consistent system parameters is adapted by exploiting set membership identification. Consequently, chance constraints are deterministically approximated using a probabilistic scaling approach by sampling from the set of system parameters. In combination with a robust constraint on the first predicted step, recursive feasibility of the proposed predictive controller and closed-loop constraint satisfaction are guaranteed. A numerical example demonstrates the efficacy of the proposed method.