AI Article Synopsis
- The paper focuses on adaptive event-triggered control for nonlinear continuous-time systems using a method that includes neural networks for function approximation.
- The authors design an adaptive model and controller using backstepping and set conditions to update feedback signals and neural network weights only when necessary, which helps avoid unnecessary updates.
- The approach improves efficiency by reducing the frequency of data transmissions while ensuring system stability, supported by rigorous analysis, and is validated through simulations.
Article Abstract
This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN weights are aperiodically updated only when the event-triggered condition is violated. A positive lower bound on the minimum intersample time is guaranteed to avoid accumulation point. The closed-loop stability of the resulting nonlinear impulsive dynamical system is rigorously proved via Lyapunov analysis under an adaptive event sampling condition. In comparing with the traditional adaptive backstepping design with a fixed sample period, the event-triggered method samples the state and updates the NN weights only when it is necessary. Therefore, the number of transmissions can be significantly reduced. Finally, two simulation examples are presented to show the effectiveness of the proposed control method.
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| http://dx.doi.org/10.1109/TNNLS.2017.2650238 | DOI Listing |
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