Decentralized control of large scale interconnected systems using adaptive neural network-based dynamic surface control
A novel decentralized controller using the dynamic surface control (DSC) is proposed for a class of uncertain large scale interconnected nonlinear systems in strictfeedback form while relaxing the "explosion of complexity" problem which is observed in the typical backstepping approach. The matching condition is not assumed when dealing with the interconnection terms. Neural networks (NNs) are utilized to approximate the uncertainties in both subsystem and interconnected terms. By using novel NN weight update laws, it is demonstrated using Lyapunov stability that the closed-loop signals are asymptotically stable in the presence of NN approximation errors in contrast with the uniform ultimate boundedness result that is common in the literature with NN- based DSC and backstepping schemes. Simulation results of the controller performance for a nonlinear decentralized system justify theoretical conclusions. © 2009 IEEE.
Publication Source (Journal or Book title)
Proceedings of the International Joint Conference on Neural Networks
Mehraeen, S., Jagannathan, S., & Crow, M. (2009). Decentralized control of large scale interconnected systems using adaptive neural network-based dynamic surface control. Proceedings of the International Joint Conference on Neural Networks, 2058-2064. https://doi.org/10.1109/IJCNN.2009.5178736