弹群分布式一致误差约束自适应最优协同拦截方法

To deal with the problem of poor synchronization for partial unknown nonlinear cooperative guidance system subject to synchronization error constraints caused by unknown maneuvering targets, a feedforward compensation + feedback optimization cooperative guidance framework is constructed. Then, a distributed adaptive optimal cooperative interception guidance method is proposed. In the feedforward compensation part, a nonlinear mapping mechanism is provided by designing a novel barrier Lyapunov function (BLF) for compensating the system effects of synchronization error constraints. To estimate the unknown nonlinear term caused by the maneuvering target, a neural network (NN) observer is built, in which the weight values are updated adaptively for estimating the target’s unknown maneuvering disturbance. In the feedback optimization part, the virtual and actual control inputs are designed recursively by using the ADP technique, in which the control input in every backstepping process is transformed into the issue of solving the nonlinear coupled HJB equation. An adaptive critic network is built to solve the optimal cost function of the HJB equation online, in which a residual error based adaptive updating law of critic weight is derived. The stability of the nonlinear cooperative guidance closed-loop system is analyzed and the convergence of cooperative synchronization error is guaranteed theoretically based on the Lyapunov theory. The simulation results show that the synchronization error can be reduced to 0. 01 s while ensuring the interception precision with the proposed method.