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In this article, a novel robust adaptive neural network tracking control scheme is presented for a class of uncertain nonlinear systems with quantized inputs and full state constraints. A tan-type barrier Lyaponov function is proposed to constrain all states, and the unknown nonlinear function term in virtual control is approximated by radial basis function neural network(RBFNN). The uncertainty term and disturbance term in the system are dealt with by robust scheme. Under the proposed quantized tracking control scheme, the communication load of the system is reduced, the boundedness of all signals in the closed-loop system is verified, the full state constraints are satisfied. Simulation results are presented to illustrate the effectiveness of the proposed adaptive control scheme.

In this article, a practical fixed-time adaptive neural network (NN) trajectory tracking control scheme for underactuated autonomous underwater vehicles (AUVs) subject to uncertain dynamics, unknown time-varying disturbances, an unknown dead zone, actuator faults and input quantization is developed for the first time. Here, a hysteresis quantizer is introduced to decrease the oscillation in the signal quantization process. Then, the radial basis function NN is employed to compensate the uncertainty term in the AUVs trajectory tracking control system. By incorporating the bounded estimate, smoothing functions and parameter adaptive technique, the problem of unknown dead zone, actuator fault and input quantization are addressed. The restrictive conditions of boundedness for the disturbance-like item in conventional sector bounded quantizer is resolved. Subsequently, a practical fixed-time adaptive NN trajectory tracking control law is designed does not require any parameter information of the quantizer under the backstepping design framework. The theoretical analysis further confirms that all signals in the AUV trajectory tracking closed-loop control system remain bounded, and the developed control scheme is shown to be effective through simulation results.

This paper concentrates on the study of adaptive control for the synchronization of fractional-order neural networks. Instead of classical adaptive control updating method, an intermittent-active updating strategy is proposed to adaptively tune the control gain in a fractional-order fashion. Moreover, quantization is brought into the control design to take into account the restricted bandwidth in signal transmission. Note that the suggested controller is basic yet effective in terms of the fractional-order system. The main theorem is established with the method of reduction to absurdity as well as Lyapunov stability theorem. Finally, simulation calculation is conducted to validate the effectiveness of our proposed method.

In this paper, an adaptive neural control issue is addressed for a class of switched unknown strict-feedback nonlinear system under constraint output, in which the input signal is quantized. The control goal is to design a quantized controller to ensure that the system's output signal follows a given reference signal, meanwhile, the system output signal meets the asymmetric constraint requirement. To this end, the radial basis function neural networks (RBFNNs) are employed to approximate the unknown nonlinear functions. Adaptive backstepping technique and barrier Lyapunov function method are utilized to design the tracking controller and analyze the closed-loop stability. The proposed control strategy is shown to deal with the presented problem well. Finally, two simulation examples are presented to illustrate the efficacy of the design scheme.

This paper mainly concentrates on the distributed tracking control problem for a class of uncertain nonlinear multi-agent systems with quantized input signal. Unlike the previous results on quantized control for multi-agent systems, the nonlinearities in this research can be completely unknown. To cope with these unknown nonlinearities, adaptive neural networks technique is employed to approximate the unknown nonlinear functions. Meanwhile, backstepping approach is utilized to handle the control design issue. Based on an important property of nonlinear decomposition for quantizer, a distributed quantized feedback tracking control scheme is successfully proposed to ensure the stability of the whole systems and the implementation of synchronous tracking. Finally, a simulation example is used to illustrate the efficacy of the proposed control scheme.