An identifier-actor-optimizer policy learning architecture for optimal control of continuous-time nonlinear systems

• 作者: FangHua Jiang JunFeng Li Lin Cheng ZhenBo Wang
• 来源期刊: 中国科学:物理学 力学 天文学（英文版）
• 年，卷（期）: 2020,（6）
• 页码范围: 42-53
• 页数: 12页
• 语种: 英文

摘要

An intelligent solution method is proposed to achieve real-time optimal control for continuous-time nonlinear systems using a novel identifier-actor-optimizer (IAO) policy learning architecture.In this IAO-based policy learning approach,a dynamical identifier is developed to approximate the unknown part of system dynamics using deep neural networks (DNNs).Then,an indirect-method-based optimizer is proposed to generate high-quality optimal actions for system control considering both the constraints and performance index.Furthermore,a DNN-based actor is developed to approximate the obtained optimal actions and return good initial guesses to the optimizer.In this way,the traditional optimal control methods and state-of-the-art DNN techniques are combined in the IAO-based optimal policy learning method.Compared to the reinforcement learning algorithms with actor-critic architectures that suffer hard reward design and low computational efficiency,the IAO-based optimal policy learning algorithm enjoys fewer user-defined parameters,higher learning speeds,and steadier convergence properties in solving complex continuous-time optimal control problems (OCPs).Simulation results of three space flight control missions are given to substantiate the effectiveness of this IAO-based policy learning strategy and to illustrate the performance of the developed DNN-based optimal control method for continuous-time OCPs.

作者信息

序号	姓名	单位	发文数	被引次数	H指数	G指数
1	Lin Cheng		1	0	0.0	0.0
2	ZhenBo Wang		1	0	0.0	0.0
3	FangHua Jiang		1	0	0.0	0.0
4	JunFeng Li		1	0	0.0	0.0