Title :
A novel self-learning optimal control approach for decentralized guaranteed cost control of a class of complex nonlinear systems
Author :
Ding Wang;Hongwen Ma;Pengfei Yan;Derong Liu
Author_Institution :
State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
Abstract :
In this paper, a novel self-learning optimal control approach is established to design the decentralized guaranteed cost control of a class of complex nonlinear systems under uncertain environment. By expressing the interconnected sub-systems as a whole system, establishing an appropriate bounded function, and defining a modified cost function, the decentralized guaranteed cost control problem is transformed into an optimal control problem. Then, the online policy iteration algorithm is employed to solve iteratively the modified Hamilton-Jacobi-Bellman equation corresponding to the nominal system. A critic neural network is constructed to obtain the optimal control approximately. At last, a simulation example is provided to verify the effectiveness of the present control approach.
Keywords :
"Cost function","Optimal control","Uncertainty","Nonlinear systems","Feedback control","Mathematical model"
Conference_Titel :
Intelligent Control and Information Processing (ICICIP), 2015 Sixth International Conference on
Print_ISBN :
978-1-4799-1715-0
DOI :
10.1109/ICICIP.2015.7388202