论文标题
具有完全未知动态的连续时间工业过程的数据驱动的预测控制
Data-Driven Predictive Control for Continuous-Time Industrial Processes with Completely Unknown Dynamics
论文作者
论文摘要
本文研究了具有完全未知动态的一类连续时间工业过程的数据驱动的预测控制问题。拟议的方法采用数据驱动的技术，使用输入输出测量值将系统矩阵在线。然后，设计了一种无模型的预测控制方法，以实现后退的优化并实现参考跟踪。分别分析了提出的算法和闭环控制系统的稳定性的可行性。最后，提供了一个模拟示例来证明所提出的方法的有效性。
This paper investigates the data-driven predictive control problems for a class of continuous-time industrial processes with completely unknown dynamics. The proposed approach employs the data-driven technique to get the system matrices online, using input-output measurements. Then, a model-free predictive control approach is designed to implement the receding-horizon optimization and realize the reference tracking. Feasibility of the proposed algorithm and stability of the closed-loop control systems are analyzed, respectively. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed approach.
