论文标题
具有可变离散化的稳定准时间 - 最佳非线性模型预测控制
Stabilizing Quasi-Time-Optimal Nonlinear Model Predictive Control with Variable Discretization
论文作者
论文摘要
本文介绍了非线性系统的新型时间到点模型预测控制概念的开发和分析。但是，文献中的最新方法采用了时间转换，但是不能保持递归可行性用于分段恒定控制参数化。本文中的关键思想是引入具有可变离散化的统一网格。收缩的马网适应方案可确保与目标状态周围的特定区域和递归可行性的收敛。该区域的大小可通过设计参数配置。这促进了准时 - 最佳控制的系统双模式设计，以恢复渐近稳定性并建立平稳的稳定性。引入了两个具有不同稀疏模式的非线性程序公式，以实现和实施潜在的最佳控制问题。对于一类数值集成方案，即使在没有双模式的情况下，也可以实现名义渐近稳定性和真实的时间优化。比较分析以及实验结果证明了所提出的技术的有效性。
This paper deals with the development and analysis of novel time-optimal point-to-point model predictive control concepts for nonlinear systems. Recent approaches in the literature apply a time transformation, however, which do not maintain recursive feasibility for piecewise constant control parameterization. The key idea in this paper is to introduce uniform grids with variable discretization. A shrinking-horizon grid adaptation scheme ensures convergence to a specific region around the target state and recursive feasibility. The size of the region is configurable by design parameters. This facilitates the systematic dual-mode design for quasi-time-optimal control to restore asymptotic stability and establish a smooth stabilization. Two nonlinear program formulations with different sparsity patterns are introduced to realize and implement the underlying optimal control problem. For a class of numerical integration schemes, even nominal asymptotic stability and true time-optimality are achieved without dual-mode. A comparative analysis as well as experimental results demonstrate the effectiveness of the proposed techniques.
