A local dynamic extreme learning machine based iterative learning control of nonlinear batch process
A local dynamic extreme learning machine based iterative learning control of nonlinear batch process
复制标题
基于局部动态极限学习机的非线性批处理迭代学习控制
DOI：
10.1002/oca.2788
复制
发表时间：
2022
影响因子：
1.8
通讯作者：
Li Jia
中科院分区：
文献类型：
--
作者：
Chengyu Zhou;Li Jia
本文研究了非线性间歇过程的最优控制问题。首先，为了得到高效、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)组成的新型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，其中控制输入轨迹可通过求解二次规划问题来获得。此外，在逆模型系统的基础上，利用JITL方法可以得到Q-ILC算法的初始批控制输入轨迹。不仅可以解决模型-对象失配和实时扰动问题，而且可以获得较快的系统收敛速度和较小的跟踪误差。此外，还分析了控制输入和跟踪误差的收敛特性。最后，给出了一个典型的间歇过程实例，验证了该方法的可行性和优越性。
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.