Finite-time deterministic learning control considering quantization and communication constraints with its application
This paper proposes a finite-time deterministic learning control considering quantization and communication constraints for a class of strictly feedback nonlinear systems with quantized input and external disturbances.The method includes two stages:offline learning training and online triggered control.First,the neural network technique is used to approximate the unknown nonlinear function during the offline learning training stage.The command filter backstepping technique is introduced to overcome the problem of"computational explosion".As a result,the unknown dynamic knowl-edge of the system is acquired and stored in the control process.Then,an online triggered controller based on deterministic learning mechanism is designed using the obtained empirical knowledge.The Lyapunov stability theory is employed to prove that the closed-loop system is practically finite-time stable,and the tracking error converges to the neighborhood of the origin in finite time.Finally,the effectiveness of the proposed scheme is verified by aircraft simulation.
万方数据
维普
