Data-Driven Optimal Formation Tracking Control for Multiple Interceptors Under Switching Topologies and Input Constraints

In response to the challenges of high energy consumption, difficulty in detecting maneuvering targets, and limited interceptor maneuver overload in the large airspace and long-time domain, a data-driven optimal coverage formation and maintenance strategy is proposed in three-dimensional space. First, the nonlinear dynamic model of heterogeneous interceptors is linearized through feedback linearization. Then, considering the time-varying characteristics of the communication topology among heterogeneous interceptors, a distributed observer is proposed to estimate the expected positions of multiple interceptors. Second, based on integral reinforcement learning, an offline policy iteration control algorithm is proposed to overcome the constraints of overload and dependence on target models. It can learn the optimal control strategy online and prove the optimality of the strategy as well as the convergence and stability of the algorithm. Finally, the effectiveness of the strategy is verified through numerical simulation experiments, demonstrating its ability to achieve energy-optimal coverage formation tracking of high-speed maneuvering targets in the large airspace and long-time domain.