A Dual-Control Effect Preserving Formulation for Nonlinear Output-Feedback Stochastic Model Predictive Control With Constraints

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