Adaptive Decentralized Neural Network Tracking Control for Uncertain Interconnected Nonlinear Systems With Input Quantization and Time Delay

This study investigates the problem of adaptive decentralized tracking control for a class of interconnected nonlinear systems with input quantization, unknown function, and time-delay, where the time-delay and interconnection terms are supposed to be bounded by some completely unknown functions. An adaptive decentralized tracking controller is constructed via the backstepping method and neural network technique, where a sliding-mode differentiator is presented to estimate the derivative of the virtual control law and reduce the complexity of the control scheme. On the basis of Lyapunov analysis scheme and graph theory, all the signals of the closed-loop system are uniformly ultimately bounded. Finally, an application example of an inverted pendulum system is given to demonstrate the effectiveness of the developed methods.