Stochastic model predictive control for discrete time-varying uncertain systems with chance constraint
ZHANG Yi-gang
HUANG Dao-ping
LIU Yi-qi
Abstract：The model uncertainty in the model predictive control(MPC)method mainly arises from factors such as external disturbances,input noise,and time-varying parameters,all of which may lead to prediction errors and thus reduce control performance.This paper proposes a novel stochastic model predictive control(SMPC)algorithm for a class of discrete time-varying systems with additive disturbances.The algorithm can effectively predict the system's future behavior and optimize the control input under specific constraints to achieve predetermined performance goals.To implement the control method proposed in this paper,the Lyapunov stability theory is employed to ensure the closed-loop stability of the system,convex combination techniques are used to address the time-varying nature of the system parameters,and convex optimization methods are utilized to calculate the control gain of the nominal system.Additionally,the inverse cumulative distribution function is used to transform probabilistic constraints into deterministic constraints.Finally,the paper verifies the proposed method through numerical simulation experiments.The simulation results show that the SMPC strategy can better adapt to the randomly changing environment,maintain high control accuracy when facing random disturbances,and has relatively low conservatism.
Keywords：stochastic model predictive controltime-varying systemsprobabilistic constraints:random additive dis-turbancesconvex combination
Publication Date：2025-12-30
Online Publishing Date：2026-03-05(First online date of this platform, not the publication date of the document)
Pages：9( 2459-2467 )