论文标题
区域预测控制具有次优扩展的有效性区域
Regional predictive control with suboptimally extended regions of validity
论文作者
论文摘要
模型预测控制（MPC）基于永久解决优化问题。优化的解决方案通常被解释为当前状态的最佳输入。但是，优化的解决方案不仅提供了最佳输入，还提供了整个最佳仿射反馈定律和本法律最佳的多元化。我们最近建议只要系统保留在多层室中，就可以使用本反馈定律。这可以解释为基于事件的方法，其中离开当前多层是触发下一个优化的事件。这种方法尤其适合于网络控制设置，因为可以通过对精益本地节点进行低计算工作来评估反馈定律及其多人。在本文中，延长了反馈法的有效性区域。更确切地说，最佳多面体扩展到由可行性和稳定区域的交集导致的非线性有效性区域。结果，与最佳方法相比，需要更少的二次程序。新的有效区域仍然适合在精益局部节点上进行评估。此外，可以调整区域以获得所需的闭环性能。
Model predictive control (MPC) is based on perpetually solving optimization problems. The solution of the optimization is usually interpreted as the optimal input for the current state. However, the solution of the optimization does not just provide an optimal input, but an entire optimal affine feedback law and a polytope on which this law is optimal. We recently proposed to use this feedback law as long as the system remains in its polytope. This can be interpreted as an event-based approach, where leaving the current polytope is the event that triggers the next optimization. This approach is especially appropriate for a networked control setting since the feedback laws and their polytopes can be evaluated with a low computational effort on lean local nodes. In this article the region of validity for a feedback law is extended. More precisely, the optimal polytopes are extended to nonlinearly bounded regions of validity resulting from the intersection of a feasibility and stability region. As a result, fewer quadratic programs need to be solved compared to the optimal approach. The new validity regions are still suitable for the evaluation on a lean local node. Moreover, the regions can be adjusted for a desired closed-loop performance.
