导弹集群技术在军事领域的应用是广泛的。深入研究导弹集群技术是改变未来作战模式单一、提升系统整体作战效能的有效手段。本文针对导弹集群打击机动目标的场景，考虑实际制导过程中集群系统可能面临的网络攻击和外源干扰等外部非理想因素及建模误差、领弹战损、弹间碰撞等内部限制性因素，结合自适应动态规划(Adaptive Dynamic Programming, ADP)、最优控制、滑模控制等控制方法，开展了基于微分博弈(Differential Game, DG)的导弹集群分布式协同制导技术研究。具体内容如下：
针对导弹多对一追逃系统目标可识别来袭进行躲避、集群计算量大和弹间易发生碰撞的问题，开展基于DG的集散式避撞协同制导律研究。首先，将集群中的智能体分为领弹和从弹，针对领弹和目标，构建双方博弈对抗性能指标函数，结合ADP技术在线求解追逃双方最优控制策略。然后，基于多智能体一致性理论和代数图论，结合滑模控制法，实现从弹间的分布式协同，并利用Lyapunov法证明稳定性。最后，采用人工势场法(Artificial Potential Field, APF) 实现整个集群系统导弹间的避撞。仿真验证了当目标采取最优机动时，本文所提制导律的有效性。
针对因领弹故障导致的集群系统崩溃和APF方法的局部极小值问题，提出了一种去中心化的DG避撞协同制导律和策略融合机制。将DG与一致性编队理论结合，改进性能指标，实现去中心化，并利用Lyapunov法证明稳定性。针对单个目标面对多方追击者时存在多个逃逸策略难以确定的问题，通过设计兴趣函数，将各单一逃逸策略动态加权，提出了策略融合机制。最后引入动态调节因子改进APF，避免局部极小值的出现。仿真结果验证了所提制导律的有效性。

针对多弹协同制导系统存在部分参数未知和外部扰动的问题，设计了有限时间收敛的三阶滑模非线性扩张状态观测器。将滑模控制方法与扩张状态观测器(Extended State Observer, ESO)相结合，构建了一种新型固定时间收敛的滑模面和滑模趋近律，有效提高了观测器的跟踪速度和精度。利用Lyapunov函数验证了观测器的固定收敛时间，并对系统受扰动时的估计误差进行分析。仿真结果表明，与传统ESO相比，所提出的滑模非线性扩张状态观测器在性能上有显著提高。

The application of missile cluster technology in military operations is significant, offering a way to transform future combat and enhance system effectiveness. This paper explores distributed cooperative guidance technology for missile clusters attacking mobile targets, considering external factors like network attacks and internal issues such as modeling errors and missile collisions. Utilizing Adaptive Dynamic Programming (ADP), optimal control, and sliding mode control, the study focuses on Differential Game (DG) based strategies.
A distributed collision-avoidance guidance law using DG is developed to address challenges like target identification and missile collisions. The approach divides the cluster into lead and slave agents, constructs a game performance index, and uses ADP for optimal control. Multi-agent consistency and sliding mode control facilitate distributed cooperation, with stability confirmed via Lyapunov methods. The Artificial Potential Field (APF) method ensures collision avoidance, validated through simulations.
A decentralized DG collision-avoidance guidance law and strategy fusion mechanism are proposed to address cluster collapse and APF local minima. DG is combined with formation theory, and decentralization is achieved by improving the performance index. ADP solves the optimal control strategy online, with stability proven via Lyapunov. A strategy fusion mechanism dynamically weights escape strategies using an interest function, and a dynamic regulator enhances APF to avoid local minima. Simulations validate the guidance law's effectiveness.
A third-order sliding mode nonlinear extended state observer with finite time convergence is designed to solve the problem of unknown parameters and external disturbance in multi-missile cooperative guidance system. By combining the sliding mode control method with the Extended State Observer (ESO), a new sliding mode surface and the sliding mode reaching law with fixed time convergence are constructed, which effectively improves the tracking speed and accuracy of the observer. The fixed convergence time of the observer is verified by Lyapunov function, and the estimation error is analyzed when the system is disturbed. Simulation results show that compared with traditional ESO, the performance of the proposed sliding mode nonlinear extended state observer is significantly improved.

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