Decentralized Stabilization for Nonlinear Systems with Unknown Mismatched Interconnections
This paper establishes a neural network and policy iteration based decentralized control scheme to stabilize large-scale nonlinear systems with unknown mismatched interconnections. For relaxing the common assumption of upper boundedness on interconnections when designing the decentralized optimal control, interconnections are approximated by neural networks with local signals of isolated subsystem and replaced reference signals of coupled subsystems. By using the adaptive estimation term, the performance index function is constructed to reflect the replacement error. Hereafter, it is proven that the developed decentralized optimal control policies can guarantee the closed-loop large-scale nonlinear system to be uniformly ultimately bounded. The effectiveness of the developed scheme is verified by a simulation example.
KeywordsAdaptive Dynamic Programming Decentralized control Unknown mismatched interconnections Policy iteration
This work was supported in part by the National Natural Science Foundation of China under Grants 61233001, 61273140, 61304086, 61374105, 61374051, 61533017, 61603387 and U1501251, in part by the Scientific and Technological Development Plan Project in Jilin Province of China under Grants 20150520112JH and 20160414033GH, and in part by Beijing Natural Science Foundation under Grant 4162065.
- 3.Jiang, Y., Jiang, Z.: Robust adaptive dynamic programming for large-scale systems with an application to multimachine power systems. IEEE Trans. Circ. II 59(10), 693–697 (2012)Google Scholar