A dual-control effect preserving formulation for nonlinear output-feedback stochastic model predictive control with constraints

TL;DR

This paper introduces a tractable approximation for nonlinear output-feedback stochastic model predictive control that preserves the dual control effect, enabling better decision-making under uncertainty with constraints.

Contribution

It presents a novel linearization-based formulation that maintains the dual control effect in approximate stochastic MPC for nonlinear systems with output feedback.

Findings

Exact computation of expected outer functions in the approximation

Preservation of dual control effect in the control strategy

Applicability to constrained nonlinear stochastic systems

Abstract

We propose a formulation for approximate constrained nonlinear output-feedback stochastic model predictive control. Starting from the ideal but intractable stochastic optimal control problem (OCP), which involves the optimization over output-dependent policies, we use linearization with respect to the uncertainty to derive a tractable approximation which includes knowledge of the output model. This allows us to compute the expected value for the outer functions of the OCP exactly. Crucially, the dual control effect is preserved by this approximation. In consequence, the resulting controller is aware of how the choice of inputs affects the information available in the future which in turn influences subsequent controls.