基于迭代神经动态规划的数据驱动非线性近似最优调节
Data-driven Nonlinear Near-optimal Regulation Based on Iterative Neural Dynamic Programming
摘要
Abstract
An iterative neural dynamic programming approach is established to design the near optimal regulator of discrete-time nonlinear systems using the data-driven control formulation. An iterative adaptive dynamic programming algorithm for discrete-time general nonlinear systems is developed and proved to guarantee the property of convergence and optimality. Then, a globalized dual heuristic programming technique is developed with detailed implementation by constructing three neural networks, where the action network is trained under the framework of neural dynamic programming. This novel architecture can approximate the cost function with its derivative, and simultaneously, adaptively learn the near-optimal control law without depending on the system dynamics. It is significant to observe that it greatly improves the existing results of iterative adaptive dynamic programming algorithm, in terms of reducing the requirement of control matrix or its neural network expression, which promotes the development of data-based optimization and control design for complex nonlinear systems. Two simulation experiments are described to illustrate the effectiveness of the data-driven optimal regulation method.关键词
自适应动态规划/数据驱动控制/迭代神经动态规划/神经网络/非线性近似最优调节Key words
Adaptive dynamic programming/data-driven control/iterative neural dynamic programming/neural net-works/nonlinear near-optimal regulation引用本文复制引用
王鼎,穆朝絮,刘德荣..基于迭代神经动态规划的数据驱动非线性近似最优调节[J].自动化学报,2017,43(3):366-375,10.基金项目
国家自然科学基金(61233001, 61273140, 61304018, 61304086, 615 33017, U1501251, 61411130160), 北京市自然科学基金(4162065),天津市自然科学基金(14JCQNJC05400), 中国科学院自动化研究所复杂系统管理与控制国家重点实验室优秀人才基金, 天津市过程检测与控制重点实验室开放课题基金(TKLPMC-201612) 资助Supported by National Natural Science Foundation of China (61233001, 61273140, 61304018, 61304086, 61533017, U1501251, 61411130160), Beijing Natural Science Foundation (4162065), Tianjin Natural Science Foundation (14JCQNJC05400), the Early Career Development Award of the State Key Laboratory of Management and Control for Complex Systems (SKL-MCCS) of the Institute of Automation, Chinese Academy of Sciences (CA-SIA), and Research Fund of Tianjin Key Laboratory of Process Measurement and Control (TKLPMC-201612) （61233001, 61273140, 61304018, 61304086, 615 33017, U1501251, 61411130160）
