This paper presents a new design of model predictive control which combines non-minimal state space (NMSS) and Laguerre functions. The utilization of NMSS omits the observer design process and brings convenience to multi-input, multi-output systems with time delay. But the dimensions of NMSS are high, which is harmful to online computation. The use of Laguerre functions helps reduce the data required in the optimization algorithm and makes up for the deficiency of NMSS. A modified solution for the optimal tracking problem using NMSS and Laguerre functions is proposed. Although the choice of the parameters in Laguerre functions needs experiments and experience, the method presented in this paper brings convenience to MPC design. Simulation results show that this approach not only reduces the computing effort without sacrificing the control performance significantly, but also has a good robustness to interference when tracking.

中文翻译：

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使用拉盖尔函数进行参考跟踪的非最小状态空间模型预测控制

本文提出了一种新的模型预测控制设计，它结合了非最小状态空间（NMSS）和拉盖尔函数。NMSS的利用省略了观察者的设计过程，为具有时延的多输入多输出系统带来了便利。但 NMSS 的维数较高，不利于在线计算。拉盖尔函数的使用有助于减少优化算法所需的数据，弥补了NMSS的不足。提出了一种使用 NMSS 和 Laguerre 函数的最优跟踪问题的改进解决方案。虽然拉盖尔函数中参数的选择需要实验和经验，但本文提出的方法为MPC设计带来了便利。