基于非线性模型预测控制的多目标运动阻塞策略
An Efficient Move Blocking Strategy for Multiple Shooting based Nonlinear Model Predictive Control
摘要：
移动阻塞（MB）是一种广泛使用的策略，用于减少滚动时域控制中出现的最优控制问题（OCP）的自由度。通过强制输入变量在多个离散化步骤中保持恒定，减小了OCP的大小。在本文中，我们专注于开发计算效率的MB计划多拍摄的非线性模型预测控制（NMPC）。通过在拍摄步骤中引入MB来减少OCP的自由度，从而导致更小但稀疏的OCP。因此，保持了离散化精度和稀疏性水平。提出了一种利用OCP的稀疏结构的凝聚算法，该算法允许在离散化节点的数量上降低从二次凝聚到线性凝聚的计算复杂度。因此，可以有效地采用具有热启动策略的活动集方法，从而允许使用更长的预测范围。给出了该方案与非均匀网格NMPC的详细比较。通过两个不同的问题的仿真，该算法在减少计算负担，同时保持优化精度和约束履行的有效性。
英文摘要：
Move blocking (MB) is a widely used strategy to reduce the degrees of freedom of the Optimal Control Problem (OCP) arising in receding horizon control. The size of the OCP is reduced by forcing the input variables to be constant over multiple discretization steps. In this paper, we focus on developing computationally efficient MB schemes for multiple shooting based nonlinear model predictive control (NMPC). The degrees of freedom of the OCP is reduced by introducing MB in the shooting step, resulting in a smaller but sparse OCP. Therefore, the discretization accuracy and level of sparsity is maintained. A condensing algorithm that exploits the sparsity structure of the OCP is proposed, that allows to reduce the computation complexity of condensing from quadratic to linear in the number of discretization nodes. As a result, active-set methods with warm-start strategy can be efficiently employed, thus allowing the use of a longer prediction horizon. A detailed comparison between the proposed scheme and the nonuniform grid NMPC is given. Effectiveness of the algorithm in reducing computational burden while maintaining optimization accuracy and constraints fulfillment is shown by means of simulations with two different problems.
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