Stochastic receding horizon control with output feedback and bounded control inputs

2010/01/18 by Peter Hokayem, Hokayem, Peter, Eugenio Cinquemani +7
Engineering · Mathematics · #Advanced Control Systems Optimization #Advanced Optimization Algorithms Research #Control Systems and Identification #FOS: Mathematics #Optimization and Control (math.OC) #math.OC

paper · pdf · doi:10.48550/arxiv.1001.3015

25 pages, 4 figures

openalex publication_date 2010/01/18 · arxiv created 2010/04/14 · arxiv updated 2010/04/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We provide a solution to the problem of receding horizon control for stochastic discrete-time systems with bounded control inputs and imperfect state measurements. For a suitable choice of control policies, we show that the finite-horizon optimization problem to be solved on-line is convex and successively feasible. Due to the inherent nonlinearity of the feedback loop, a slight extension of the Kalman filter is exploited to estimate the state optimally in mean-square sense. We show that the receding horizon implementation of the resulting control policies renders the state of the overall system mean-square bounded under mild assumptions. Finally, we discuss how some of the quantities required by the finite-horizon optimization problem can be computed off-line, reducing the on-line computation, and present some numerical examples.

Citations

• Constrained model predictive control: Stability and optimality

Related

• Stochastic receding horizon control of nonlinear stochastic systems with probabilistic state constraints
• On control of discrete-time state-dependent jump linear systems with\n probabilistic constraints: A receding horizon approach
• Linear Receding Horizon Control with Probabilistic System Parameters
• A relaxation technique to ensure feasibility in stochastic control with input and state constraints
• Robust Data-Driven Receding Horizon Control