Neuroadptive quantization tracking control with accelerate convergence rate for self-restructuring systems

In this article, we investigate the tracking control problem for a class of self-restructuring systems with quantized input. The underlying system model is different from the one with fixed structure, and is able to reflect the impact arising from subsystem failure, system switching, and subsystem self-expansion and so forth. Furthermore, the system is driven with quantized input. For such systems we develop a neural network-based adaptive quantization control method with several attractive features including: (1) it is a less model-dependent based control approach with which little information on the system model is required; (2) the quantized input does not require exact knowledge of quantization parameters; (3) the tracking error is ensured to be ultimately uniformly bounded and convergence rate of the tracking error is adjustable via the introduced rate function in the control algorithm, and the tracking error converges into a specific compact set. The benefits and feasibility of the proposed control method are also validated and confirmed by numerical simulations.