针对模型未知的直升机机动飞行过程中存在的输入时滞及饱和问题, 提出了一种无模型增量自适应最优控制方案。首先, 利用增量非线性技术得到了系统的近似时变模型, 并通过递推最小二乘估计(RLS)对相关矩阵参数进行辨识; 其次, 采用泛函性能指标处理输入时滞及饱和问题, 利用增量自适应动态规划(IADP)设计近似最优跟踪控制律; 最后, 通过神经网络近似基于实时状态和延时输入的时间差分误差(TDE)函数, 并运用其瞬时积分得到评价网络权值更新率。通过Lyapunov函数分析证明了闭环系统的稳定性。直升机机动飞行速度跟踪控制仿真验证了该方法的有效性。

To solve the problems of input delay and saturation during helicopter maneuvering flight with uncertain model, a model-free incremental adaptive optimal control scheme is proposed.Firstly, the approximate time-varying model of the system is obtained by using incremental nonlinear technique, and the parameters of the relevant matrix are identified by Recursive Least Squares (RLS) estimation.Secondly, the functional performance indexes are used to deal with input delay and saturation, and Incremental Adaptive Dynamic Programming (IADP) is used to design the approximate optimal tracking control law.The Time Difference Error (TDE) function based on real-time state and delayed input is approximated by neural network, and the weight updating rate of the evaluation network is obtained by its instantaneous integral.Finally, the stability of the closed-loop system is proved by the Lyapunov function analysis.Simulations of helicopter maneuvering flight velocity tracking control are given to verify the effectiveness of the proposed control scheme.