基于事件触发机制和最小学习参数的FAHV指定时间收敛自适应控制

针对当前吸气式高超声速飞行器自适应控制结果仅能实现误差渐近收敛于预设包络、神经权值在线更新存在计算爆炸、对机载资源过度占用的难题,提出了基于事件触发机制和最小学习参数的FAHV 指定时间收敛自适应控制方法。首先,阐述了一种不依赖精确误差初值同时又能确保误差指定时间收敛的改进预设性能控制机制;其次,构建了用于FAHV 干扰辨识的相对阈值事件触发神经网络;最后,设计了相对阈值事件触发控制算法,有效降低了闭环控制器对通信资源的消耗,在非等周期信号传输的基础上实现了良好的控制精度。仿真结果表明,所提方法能够在低计算与传输资源消耗下对高度/速度参考信号实施指定时间跟踪。

An Adaptive Control with FAHV Being at Convergence on Specified Time Based on Event Triggering Mechanism and Minimum Learning Parameter

Aimed at the problems that the results of current adaptive control for Flexible Air-breathing Hypersonic Vehicle (FAHV) are capable of only achieving asymptotic error convergence to the preset envelope, and the problems of computational explosion and excessive occupancy of airborne resources remain due to online updating of neural weights, an adaptive control method is proposed for FAHV specified time convergence based on event triggering mechanism and minimum learning parameters. Firstly, an improved preset performance control mechanism is proposed which does not depend on the exact initial error value and can ensure the convergence of the specified error time. Secondly, a relative threshold event-triggered neural network for FAHV interference identification is constructed. Finally, a control algorithm of relative threshold event triggering is designed, which is capable of effectively reducing the consumption of communication resources of the closed-loop controller, and achieving good control accuracy on the basis of nonisoperiodic signal transmission. The simulation results show that the proposed method can track the height/speed reference signal on a specified time under conditions of low computing and transmission resource consumption.