基于无约束迭代学习的间歇生产过程优化控制

针对基于迭代学习控制的间歇过程优化控制算法难以进行收敛性分析的难题,本文基于数据驱动的神经模糊模型提出一种新颖的间歇过程无约束迭代学习控制方法,通过调节因子的变化去除了约束条件,使控制轨迹在批次轴上收敛,并创新性地对优化问题的收敛性给出了严格的数学证明。在理论研究的基础上,将本文提出的算法用于间歇连续反应釜的终点质量控制研究,仿真结果验证了本文算法的有效性和实用价值,为间歇过程的优化控制提供了一条新途径。

Nonrestraint-iterative learning-based optimal control for batch processes

Considering that it is difficult to analyze the convergence of iterative learning optimal control for quality control of batch processes, a novel iterative learning control based on data-driven neural fuzzy model for product quality control in batch process is proposed in this paper, which results in the convergence of the product quality and control trajectory in batch axes.Moreover, the rigorous proof is given.Lastly, to verify the efficiency of the proposed algorithm, it was applied to a benchmark batch process.The simulation results show that the proposed method is better and can be applied to practical processes, thus it provides a new way for the control of batch processes.