Control Lyapunov-Barrier Function Based Model Predictive Control for Stochastic Nonlinear Affine Systems
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866916107377442816 |
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| author | Zheng, Weijiang Zhu, Bing |
| author_facet | Zheng, Weijiang Zhu, Bing |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_06175 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Control Lyapunov-Barrier Function Based Model Predictive Control for Stochastic Nonlinear Affine Systems Zheng, Weijiang Zhu, Bing Systems and Control 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. |
| title | Control Lyapunov-Barrier Function Based Model Predictive Control for Stochastic Nonlinear Affine Systems |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2211.06175 |