No abstract

In this article, the model-based event-triggered neural learning control problem is investigated for a class of discrete-time strict-feedback systems. First, with the case of sufficient network resources, an adaptive neural controller is designed with a new neural weight update law, which avoids the n-step delay of traditional adaptive neural control methods. The proposed controller performs the tracking control task while ensuring that the radial basis function neural network can accurately identify the unknown system nonlinear dynamics and the estimated neural weights can converge to their ideal values. Second, with the case of limited network resources, the convergent neural weights can be reused to construct the neural network model, event-trigger conditions, and the neural learning controller, which can improve the tracking control performance and save the communication resources. Especially, compared with the traditional event-triggered adaptive neural controller, the neural learning controller can reduce the burden of online calculations since the constant neural weights are used to construct the neural network model and trigger conditions. For the proposed control scheme, simulation results are given to verify its effectiveness.

This article focuses on the dynamic learning and control problem for a class of nonlinear sampled-data systems in strict-feedback form. To achieve learning of unknown system dynamics in closed-loop control, two obstacles need to be overcome: the inherent noncausal problem in the backstepping design of discrete-time nonlinear systems, and the exponential stability of the sampling closed-loop error system. First, a novel command filtered adaptive neural control is developed to achieve closed-loop stability and the convergence of output tracking error based on Lyapunov theory, the noncausal problem is overcome by utilizing the command filter technique, and the n-step delay is avoided in the recursive process. Second, by combining a state transformation method,