Improved Model Predictive Control of Discrete-Time Hybrid Systems with Mixed Inputs

Mehdi Gholami, Karim Salahshoor, M. Tabatabaei-Pour, H. Shaker, Tohid Alizadeh
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引用次数: 1

Abstract

This paper suggests an improved method for predictive control of hybrid systems with mixed inputs. The algorithm takes into account the real nonlinear system as a model of a hybrid system, which is based on building a tree of evolution. Where the branch & bound (B&B) technique is applied for discrete controls in which an embedded nonlinear programming approach (Pattern search) is associated with each node of the tree in order to provide the continuous controls and explore the tree. Once the whole nodes of the tree are explored, the corresponding input is exploited to the system and the procedure is repeated. The performance of the resulting predictive control system is demonstrated on a motorboat simulation case study.
具有混合输入的离散混合系统的改进模型预测控制
提出了一种改进的混合输入系统预测控制方法。该算法将实际的非线性系统作为混合系统的模型,以建立进化树为基础。其中分支定界(B&B)技术应用于离散控制,其中嵌入式非线性规划方法(模式搜索)与树的每个节点相关联,以提供连续控制并探索树。一旦树的整个节点被探索,相应的输入将被利用到系统中,这个过程将被重复。最后通过一个汽艇仿真实例验证了该预测控制系统的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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