数据驱动的未知复杂系统在线滚动辨识与优化控制OA
Data-driven online rolling horizon identification and optimization control for unknown complex system
文章重点研究了仅利用有限输入-状态数据对未知复杂系统,进行辨识并设计预测控制的方法.所提出方法关键创新在于,在时间序列上随系统运行对非线性系统建立线性模型并滚动辨识模型参数,进而设计模型预测控制器.首先,考虑有限的输入-状态数据,通过结合集合运算和求解优化问题反推拟合该组数据的线性状态空间模型;之后,利用模型预测算法获得下一时刻系统控制输入,更新输入-状态数据集,进行下一轮的参数辨识和控制量计算.线性系统和智能供水系统实验结果表明,所提出的方法能够实现对未知复杂系统的在线滚动辨识和优化控制.
This paper focuses on the method of identifying unknown complex systems and designing predictive control using only finite input-state data.The proposed method's core innovation lies in establishing a linear model for the nonlinear system based on the time series as the system operates,and continuously identifying the model parameters through rolling horizon techniques,thereby designing a model predictive controller.Firstly,the proposed identification method considers a finite number of noisy input-state data of the unknown system with disturbance,by combining the set operation and solving a series of optimization problems to obtain a linear state space model that can describe the data.Based on this model,a model predictive control(MPC)algorithm is used to compute the control input.By measuring the system state at the next moment,the input-state data set is updated and then the next round of system identification and control input computations starts.The experimental results of linear system and intelligent water supply system show that the proposed method can realize online rolling identification and optimal control of unknown complex systems.
杨少布道;傅安琪;乔俊飞
北京工业大学信息学部||智慧环保实验室||计算智能与智能系统北京市重点实验室||智能感知与自主控制教育部工程研究中心,北京 100124北京工业大学信息学部||智慧环保实验室||计算智能与智能系统北京市重点实验室||智能感知与自主控制教育部工程研究中心,北京 100124北京工业大学信息学部||智慧环保实验室||计算智能与智能系统北京市重点实验室||智能感知与自主控制教育部工程研究中心,北京 100124
在线滚动辨识模型预测控制奇诺多面体智能供水系统
rolling horizon identificationMPCzonotopeintelligent water supply system
《控制理论与应用》 2026 (4)
905-914,10
科技创新2030-"新一代人工智能国家科技重大专项"重大项目(2021ZD0112301),北京市自然科学基金项目(L221005),国家自然科学基金项目(62 003009,62021003,61890930-5)资助. Supported by the National Key Research and Development Program of China(2021ZD0112301),the Beijing Natural Science Foundation(L221005)and the National Natural Science Foundation of China(NSFC)(62003009,62021003,61890930-5).
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