Stochastic Receding Horizon Control of Constrained Linear Systems With State and Control Multiplicative Noise

Abstract

We develop a receding horizon control approach to stochastic linear systems with control and state multiplicative noise that also contain constraints. Our receding horizon formulation is based upon an on-line optimization that utilizes open-loop plus linear feedback and is solved as a semi-definite programming problem. We also provide a characterization of stability, performance, and constraint satisfaction properties of the receding horizon controlled system under a specific choice of terminal weight and terminal constraint. A simple numerical example is used to illustrate the approach.

Keywords

This publication has 27 references indexed in Scilit:

Solving ALM problems via sequential stochastic programming
Quantitative Finance, 2007
STOCHASTIC MODEL PREDICTIVE CONTROL AND PORTFOLIO OPTIMIZATION
International Journal of Theoretical and Applied Finance, 2007
Sequential Importance Sampling Algorithms for Dynamic Stochastic Programming
Journal of Mathematical Sciences, 2006
MPC as a Tool for Sustainable Development Integrated Policy Assessment
IEEE Transactions on Automatic Control, 2006
A two-layered optimisation-based control strategy for multi-echelon supply chain networks
Computers & Chemical Engineering, 2004
Scenario reduction in stochastic programming
Mathematical Programming, 2003
Stochastic Linear-Quadratic Control via Semidefinite Programming
SIAM Journal on Control and Optimization, 2001
Linear matrix inequalities, Riccati equations, and indefinite stochastic linear quadratic controls
IEEE Transactions on Automatic Control, 2000
State-feedback control of systems with multiplicative noise via linear matrix inequalities
Systems & Control Letters, 1995
Feedback stabilizability for stochastic systems with state and control dependent noise
Automatica, 1976

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