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This paper investigates the observer-based adaptive stabilization control problem for a class of time-delay nonlinear systems with unknown control gain using an echo state network (ESN). In order to handle unknown functions, a new recurrent neural network (RNN) approximation method called ESN is utilized. It improves accuracy, reduces computing cost, and is simple to train. To address the issue of unknown control gain, the Nussbaum function is used, and the Lyapunov–Krasovskii functionals are used to address the delay term. The backstepping strategy and command filtering methodology are then used to create an adaptive stabilization controller. All of the closed-loop system's signals are predicted to be confined by the Lyapunov stability theory. Finally, a simulation example is used to demonstrate the effectiveness of the suggested control mechanism. [ABSTRACT FROM AUTHOR]

This paper presents a distributed adaptive quantized bipartite containment neural network control strategy for nonlinear multi-agent systems with sensor faults. The radial basis function neural network is introduced to compensate the uncertainties. The saturation function is employed to achieve the asymmetric hysteresis quantization control, which can effectively eliminate the assumption that the quantizer parameters are bounded. In addition, an adaptive compensation technique is employed to handle the difficulty of sensor faults. Moreover, the second-order tracking differentiator is used to eliminate the repeated differential problem existed in the backstepping technique. Under the proposed control scheme, all signals of the closed-loop multi-agent systems are cooperatively semi-globally uniformly ultimately bounded, and the containment control performance is gained. Finally, two simulation examples are listed to demonstrate the validity of the presented control strategy. [ABSTRACT FROM AUTHOR]