论文标题
基于控制Lyapunov - 鲍尔函数的基于随机非线性仿射系统的模型预测控制
Control Lyapunov-Barrier Function Based Model Predictive Control for Stochastic Nonlinear Affine Systems
论文作者
论文摘要
本文为具有稳定性和可行性保证的非线性仿射系统提供了随机模型预测控制（MPC）框架。我们首先介绍了随机控制Lyapunov - 鲍尔函数（CLBF）的概念，并通过结合不受约束的控制Lyapunov函数（CLF）和控制屏障功能来提供构建CLBF的方法。通过动态反馈线性化，从其相应的半线性系统中获得了无约束的CLF。基于构建的CLBF，我们利用采样的数据MPC框架来处理状态和输入约束，并分析闭环系统的稳定性。此外，将事件触发机制集成到MPC框架中，以在抽样间隔内提高性能。提出的基于CLBF的随机MPC通过避免障碍示例验证。
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.
