论文标题
使用因果状态反馈参数化对不确定系统的计算有效的鲁棒模型预测控制
Computationally Efficient Robust Model Predictive Control for Uncertain System Using Causal State-Feedback Parameterization
论文作者
论文摘要
本文调查了线性时间不变（LTI）离散时间系统的鲁棒模型预测控制（RMPC）的问题。通常，具有状态回馈参数化的约束RMPC问题是非线性（和非convex），其在线实施中具有过高的计算负担。为了解决这一问题，提出了一种新颖的方法，通过使用半决赛放松技术，将状态反馈的RMPC问题线性化，而最小的保守主义。提出的算法通过求解线性矩阵不平等（LMI）优化来计算状态反馈的增益和扰动，与文献中的其他方案相比，该优化的计算负担大大减轻，而不会对控制器的跟踪性能产生不利影响。此外，还提出了一种在RMPC问题上提供初始可行性的离线策略。通过文献的数值示例证明了所提出的方案的有效性。
This paper investigates the problem of robust model predictive control (RMPC) of linear-time-invariant (LTI) discrete-time systems subject to structured uncertainty and bounded disturbances. Typically, the constrained RMPC problem with state-feedback parameterizations is nonlinear (and nonconvex) with a prohibitively high computational burden for online implementation. To remedy this, a novel approach is proposed to linearize the state-feedback RMPC problem, with minimal conservatism, through the use of semidefinite relaxation techniques. The proposed algorithm computes the state-feedback gain and perturbation online by solving a linear matrix inequality (LMI) optimization that, in comparison to other schemes in the literature is shown to have a substantially reduced computational burden without adversely affecting the tracking performance of the controller. Additionally, an offline strategy that provides initial feasibility on the RMPC problem is presented. The effectiveness of the proposed scheme is demonstrated through numerical examples from the literature.
