论文标题
LPV框架中具有安全性和稳定性的非线性系统的基于学习和方案的MPC设计
A Learning- and Scenario-based MPC Design for Nonlinear Systems in LPV Framework with Safety and Stability Guarantees
论文作者
论文摘要
本文介绍了基于学习和方案的模型预测控制（MPC）设计方法，用于在线性参数变化（LPV）框架中建模的系统。使用从系统收集的输入输出数据，首先通过变异贝叶斯推理神经网络（BNN）方法来学习具有不确定性定量的状态空间LPV模型。假定学习的概率模型包含具有很高概率的系统的真实动力学，并用于生成场景，以确保基于方案的MPC安全。此外，为确保稳定性并提高闭环系统的性能，设计了参数依赖的终端成本和控制器以及终端可靠的正不变式集合。数值示例将用于证明拟议的控制设计方法可以确保安全并实现所需的控制性能。
This paper presents a learning- and scenario-based model predictive control (MPC) design approach for systems modeled in linear parameter-varying (LPV) framework. Using input-output data collected from the system, a state-space LPV model with uncertainty quantification is first learned through the variational Bayesian inference Neural Network (BNN) approach. The learned probabilistic model is assumed to contain the true dynamics of the system with a high probability and used to generate scenarios which ensure safety for a scenario-based MPC. Moreover, to guarantee stability and enhance performance of the closed-loop system, a parameter-dependent terminal cost and controller, as well as a terminal robust positive invariant set are designed. Numerical examples will be used to demonstrate that the proposed control design approach can ensure safety and achieve desired control performance.
