论文标题
准时间最佳的非线性模型预测控制具有软约束的框架
A Framework for Quasi Time-Optimal Nonlinear Model Predictive Control with Soft Constraints
论文作者
论文摘要
在许多机电应用应用程序中，控制器输入成本可以忽略不计，时间最佳性对于通过执行快速定位操作来最大化生产率至关重要。结果，获得的控制输入主要具有爆炸性，这激发了不希望的机械振动，尤其是在具有柔性结构的系统中。本文解决了时间优势控制问题，并提出了一种新颖的方法，该方法明确地解决了在退化的地平线技术的背景下解决振动行为。这种技术是一个关键特征，尤其是对于具有时变振动行为的系统。在模型预测控制（MPC）的背景下，预测和应对振动行为，以软约束的配方进行应对，这会惩罚任何违反不希望的振动的行为。与硬约束配方相比，该公式扩大了广泛的工作范围内的可行性。这种方法的闭环性能在具有高度柔韧性的堆叠式起重机的数值示例中得到了证明。
In many mechatronic applications, controller input costs are negligible and time optimality is of great importance to maximize the productivity by executing fast positioning maneuvers. As a result, the obtained control input has mostly a bang-bang nature, which excite undesired mechanical vibrations, especially in systems with flexible structures. This paper tackles the time-optimal control problem and proposes a novel approach, which explicitly addresses the vibrational behavior in the context of the receding horizon technique. Such technique is a key feature, especially for systems with a time-varying vibrational behavior. In the context of model predictive control (MPC), vibrational behavior is predicted and coped in a soft-constrained formulation, which penalize any violation of undesired vibrations. This formulation enlarges the feasibility on a wide operating range in comparison with a hard-constrained formulation. The closed-loop performance of this approach is demonstrated on a numerical example of stacker crane with high degree of flexibility.
