In this paper, a novel strategy for finding the optimal operation profiles for nonlinear dynamic processes is developed. Based on the direct sequential stochastic framework for dynamic optimization, this work proposes a technique based on Fourier series for the control vector parameterization, as an alternative to the traditional methods. This approach has the advantage of choosing a high degree of smoothness to avoid sharp changes for the input variables, which is preferred in most chemical and biological processes. On the other hand, when several arcs are present in the qualitative optimal profile, the number of parameters can be increased for a better approximation. The proposed strategy was applied to four well-studied nonlinear processes, covering batch and fed-batch reactors, and multi-input systems. The algorithm was tested through simulations. Good performances were obtained in comparison to some previous results available in the literature.

本文提出了一种寻找非线性动态过程最优运行曲线的新策略。在动态优化的直接顺序随机框架的基础上，本文提出了一种基于傅立叶级数的控制矢量参数化技术，以替代传统方法。这种方法的优点是选择高度的平滑度，以避免输入变量的急剧变化，这在大多数化学和生物过程中都是优选的。另一方面，当定性最佳轮廓中存在多个弧时，可以增加参数的数量以实现更好的近似。拟议的策略被应用于四个经过充分研究的非线性过程，涵盖间歇式和补料分批反应器，以及多输入系统。通过仿真测试了该算法。与文献中现有的一些结果相比，获得了良好的性能。