Warsaw University of Technology, Faculty of Electronics and Information Technology, Institute of Control and Computation Engineering, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland.
ISA Trans. 2023 Mar;134:336-356. doi: 10.1016/j.isatra.2022.09.010. Epub 2022 Sep 12.
A new approach to nonlinear Model Predictive Control (MPC) is discussed in this work. A custom user-defined cost function is used in place of the typically considered quadratic norm. An approximator of the cost function is applied to obtain a computationally simple procedure and linearization of two trajectories is carried out online. The predicted output trajectory of the approximator and the predicted trajectory of the manipulated variable, both over the prediction horizon, are repeatedly linearized online. It yields a simple quadratic programming task. The algorithm is implemented for a simulated neutralization benchmark modeled by a neural Wiener model. The resulting control quality is excellent, identical to that observed in the MPC scheme with nonlinear optimization. Validity of the described MPC algorithms is demonstrated when only simple box constraints are considered on the process input variable and in a more demanding case when additional soft limitations are put on the predicted output. Two structures of the approximator are compared: polynomial and neural; the advantages of the latter one are shown and stressed.
本文讨论了一种新的非线性模型预测控制（MPC）方法。使用自定义用户定义的成本函数代替通常考虑的二次范数。应用成本函数的逼近器来获得计算简单的过程，并在线进行两条轨迹的线性化。逼近器的预测输出轨迹和操纵变量的预测轨迹，都在预测范围内，在线重复线性化。它产生了一个简单的二次规划任务。该算法已针对通过神经 Wiener 模型建模的模拟中和基准进行了实施。所得的控制质量非常出色，与使用非线性优化的 MPC 方案观察到的质量相同。当仅对过程输入变量考虑简单的框约束并且在更苛刻的情况下对预测输出施加附加软限制时，证明了所描述的 MPC 算法的有效性。比较了两种逼近器结构：多项式和神经网络；展示并强调了后者的优势。
