论文标题
基于管子的鲁棒模型预测控制，用于模拟的分布式参数系统（扩展版本）
Tube-based Robust Model Predictive Control for a Distributed Parameter System Modeled as a Polytopic LPV (extended version)
论文作者
论文摘要
分布式参数系统（DPS）作为部分微分方程（PDE）配制。特别是，在随着时间变化的边界条件下，PDE引入力耦合。在柔性堆叠起重机（STC）的情况下，引入了非线性耦合。因此，可以使用非线性模型预测控制（NMPC）来解决在线轨迹计划和跟踪。然而，由于NMPC的计算需求很高，本文讨论了将非线性嵌入线性参数变化（LPV）系统中的可能性，因此可以使用数值低的线性MPC。在强大的管MPC（TMPC）的背景下，所得的不匹配被视为参数和添加剂不确定性。对于建议的方法，大多数计算都是离线进行的。仅在线进行简单的凸二次程序（QP）。另外，简要提出了软约束的扩展。模拟结果用于说明尽管不确定性，但提出的方法的良好性能，闭环稳定性和递归可行性。
Distributed parameter systems (DPS) are formulated as partial differential equations (PDE). Especially, under time-varying boundary conditions, PDE introduce force coupling. In the case of the flexible stacker crane (STC), nonlinear coupling is introduced. Accordingly, online trajectory planning and tracking can be addressed using a nonlinear model predictive control (NMPC). However, due to the high computational demands of a NMPC, this paper discusses a possibility of embedding nonlinearities inside a linear parameter varying (LPV) system and thus make a use of a numerically low-demanding linear MPC. The resulting mismatches are treated as parametric and additive uncertainties in the context of robust tube-based MPC (TMPC). For the proposed approach, most of the computations are carried out offline. Only a simple convex quadratic program (QP) is conducted online. Additionally a soft-constrained extension was briefly proposed. Simulation results are used to illustrate the good performance, closed-loop stability and recursive feasibility of the proposed approach despite uncertainties.
