Control Lyapunov–Barrier function based model predictive control for stochastic nonlinear affine systems

A stochastic model predictive control (MPC) framework is presented in this paper for nonlinear affine systems with stability and feasibility guarantee. We first introduce the concept of stochastic control Lyapunov–Barrier function (CLBF) and provide a method to construct CLBF by combining an unconst...

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Bibliographic Details
Published inInternational journal of robust and nonlinear control Vol. 34; no. 1; pp. 91 - 113
Main Authors Zheng, Weijiang, Zhu, Bing
Format Journal Article
LanguageEnglish
Published Bognor Regis Wiley Subscription Services, Inc 10.01.2024
Subjects
Control systems
Feedback linearization
Liapunov functions
Nonlinear control
Nonlinear systems
Obstacle avoidance
Optimal control
Predictive control
Stability analysis
Stochastic models
Stochastic processes
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Summary:A stochastic model predictive control (MPC) framework is presented in this paper for nonlinear affine systems with stability and feasibility guarantee. We first introduce the concept of stochastic control Lyapunov–Barrier function (CLBF) and provide a method to construct CLBF by combining an unconstrained control Lyapunov function (CLF) and control barrier functions. The unconstrained CLF is obtained from its corresponding semi‐linear system through dynamic feedback linearization. Based on the constructed CLBF, we utilize sampled‐data MPC framework to deal with states and inputs constraints, and to analyze stability of closed‐loop systems. Moreover, event‐triggering mechanisms are integrated into MPC framework to improve performance during sampling intervals. The proposed CLBF based stochastic MPC is validated via an obstacle avoidance example.
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.6962