In this paper, a fast constrained model predictive control algorithm was designed for the active suspension of a half-car model to increase the controller bandwidth so that high frequency displacement disturbance coming from the road can be rejected. To this end, a quasi-LTI model of a semi-active suspension model was controlled by a model predictive controller with orthogonal Laguerre polynomials. With the use of Laguerre polynomials, it has been shown that the optimization parameter set could be made minimal, and thereby it has been shown that on-line optimization takes less time. With numerical simulations, it has been shown that the time complexity of a model predictive control having Laguerre polynomials is linear in the length of prediction horizon, whereas time complexity of a regular model predictive control is quadratic in the length of prediction horizon. Since it has been shown that time complexity of the constrained model predictive controller with orthogonal Laguerre polynomial is reduced, it is possible to extend the prediction horizon to large values. Further, constraints on the input signal and the state vector were also discussed within this context.

中文翻译：

---

使用拉盖尔多项式对车辆悬架进行约束模型预测控制

本文针对半车模型的主动悬架设计了一种快速约束模型预测控制算法，以增加控制器带宽，从而抑制来自道路的高频位移扰动。为此，半主动悬架模型的准 LTI 模型由具有正交拉盖尔多项式的模型预测控制器控制。通过使用拉盖尔多项式，已经表明可以使优化参数集最小化，从而表明在线优化花费的时间更少。数值模拟表明，具有拉盖尔多项式的模型预测控制的时间复杂度在预测范围的长度上是线性的，而常规模型预测控制的时间复杂度在预测范围的长度上是二次的。由于已经表明具有正交拉盖尔多项式的约束模型预测控制器的时间复杂度降低，因此可以将预测范围扩展到大值。此外，在此上下文中还讨论了对输入信号和状态向量的约束。