This article deals with the optimal control issue of nonlinear batch process. First, in order to derive high efficiency and accuracy process model, a novel hierarchical searching mechanism local dynamic nonlinear model is constructed which is composed of just-in-time learning and extreme learning machine (JITL-ELM). Then, based on the local dynamic JITL-ELM model, an optimal quadratic-criterion-based iterative learning control (Q-ILC) algorithm is presented, where the control input trajectory can be obtained by solving a quadratic programming problem. Moreover, on the basis of inverse model system, the initial batch control input trajectory of the Q-ILC algorithm can be obtained by the use of JITL method. As a result, not only the issue of model-plant mismatch and real-time disturbance can be solved, but also obtain faster system convergence rate and smaller tracking error. Besides, the convergence properties of control input and tracking error are analyzed. Finally, a typical batch process is presented to demonstrate the feasibility and superiority.

中文翻译：

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基于局部动态极限学习机的非线性批处理迭代学习控制

本文讨论非线性批处理的最优控制问题。首先，为了推导高效、准确的过程模型，构建了一种由即时学习和极限学习机（JITL-ELM）组成的分层搜索机制局部动态非线性模型。然后，基于局部动态JITL-ELM模型，提出了一种基于二次准则的最优迭代学习控制（Q-ILC）算法，该算法可以通过求解二次规划问题获得控制输入轨迹。此外，在逆模型系统的基础上，利用JITL方法可以得到Q-ILC算法的初始批量控制输入轨迹。这样一来，不仅可以解决模型-工厂不匹配和实时扰动的问题，还能获得更快的系统收敛速度和更小的跟踪误差。此外，分析了控制输入和跟踪误差的收敛特性。最后，提出了一个典型的批处理过程来证明其可行性和优越性。