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

被引：8

作者：

Zheng, Weijiang ^{[1
]}

Zhu, Bing ^{[1
,2
]}

机构：

[1] Beihang Univ, Res Div 7, Beijing, Peoples R China

[2] Beihang Univ, Res Div 7, Beijing 100191, Peoples R China

来源：

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL | 2023年 / 34卷 / 01期

基金：

中国国家自然科学基金;

关键词：

control Lyapunov-Barrier function; dynamic feedback linearization; event-triggering mechanisms; sampled-data systems; stochastic model predictive control; CONSTRAINT-TIGHTENING APPROACH; FUNCTION DESIGN; STABILIZATION;

D O I：

10.1002/rnc.6962

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：91 / 113

页数：23

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