Haoyu Cheng

This paper proposes a three-dimensional prescribed-time hierarchical cooperative guidance law (3-D PTHCGL) for rapid and reliable head-on encirclement in cooperative interception of high-speed targets under incomplete directed communication topology, target maneuvers, and limited terminal engagement time. The proposed method consists of a distributed estimator layer (DEL) and a local controller layer (LCL), addressing three key issues in head-on encirclement formation: information acquisition, rapid convergence, and geometric configuration. In the DEL, a distributed prescribed-time estimator (DPTE) is developed to enable followers without direct communication with the leader to estimate the leader’s states from neighborhood information within a prescribed time. In the LCL, a prescribed-time extended state observer (PTESO) and a three-dimensional prescribed-time cooperative guidance law (3-D PTCGL) are designed to estimate target-maneuver-induced disturbances and unknown states, and to guarantee prescribed-time convergence of estimation and cooperative tracking errors. Furthermore, virtual line-of-sight (LOS) angles are introduced based on a three-dimensional head-on interception kinematic model to characterize the head-on encirclement configuration, and range-to-go together with radial relative velocity are adopted instead of time-to-go to reduce sensitivity to time-estimation errors. Simulation results demonstrate the effectiveness and robustness of the proposed method in achieving prescribed-time head-on encirclement and simultaneous attack without a speed advantage.

The purpose of this paper is to develop formation control strategies for heterogeneous multi-intelligent-agent systems in complex environments, with the goal of enhancing their performance, reliability, and stability. Complex flight conditions, such as navigating narrow gaps in urban high-rise buildings, pose considerable challenges for agent control. To address these challenges, this paper proposes a consensus-based formation strategy that integrates graph theory and multi-consensus algorithms. This approach incorporates time-varying group consistency to strengthen fault tolerance and reduce interference while ensuring obstacle avoidance and formation maintenance in dynamic environments. Through a Lyapunov stability analysis, combined with minimum dwell time constraints and the LaSalle invariance principle, this work proves the convergence of the proposed control scheme under changing network topologies. Simulation results confirm that the proposed strategy significantly improves system performance, mission execution capability, autonomy, synergy, and robustness, thereby enabling agents to successfully maintain formation and avoid obstacles in both homogeneous and heterogeneous clusters in complex environments.

To solve the problem that multiple missiles should simultaneously attack unmeasurable maneuvering targets, a guidance law with temporal consistency constraint based on the super-twisting observer is proposed. Firstly, the relative motion equations between multiple missiles and targets are established, and the topological model among multiple agents is considered. Secondly, based on the temporal consistency constraint, a cooperative guidance law for simultaneous arrival with finite-time convergence is derived. Finally, the unknown target maneuvering is regarded as bounded interference. Based on the second-order sliding mode theory, a super-twisting sliding mode observer is devised to observe and track the bounded interference, and the stability of the observer is proved. Compared with the existing research, this approach only needs to obtain the sliding mode variable which simplifies the design process. The simulation results show that the designed cooperative guidance law for maneuvering targets achieves the expected effect. It ensures successful cooperative attacks, even when confronted with strong maneuvering targets.

In this study, a novel nonfragile deep reinforcement learning (DRL) method was proposed to realize the finite-time control of switched unmanned flight vehicles. Control accuracy, robustness, and intelligence were enhanced in the proposed control scheme by combining conventional robust control and DRL characteristics. In the proposed control strategy, the tracking controller consists of a dynamics-based controller and a learning-based controller. The conventional robust control approach for the nominal system was used for realizing a dynamics-based baseline tracking controller. The learning-based controller based on DRL was developed to compensate model uncertainties and enhance transient control accuracy. The multiple Lyapunov function approach and mode-dependent average dwell time approach were combined to analyze the finite-time stability of flight vehicles with asynchronous switching. The linear matrix inequalities technique was used to determine the solutions of dynamics-based controllers. Online optimization was formulated as a Markov decision process. The adaptive deep deterministic policy gradient algorithm was adopted to improve efficiency and convergence. In this algorithm, the actor–critic structure was used and adaptive hyperparameters were introduced. Unlike the conventional DRL algorithm, nonfragile control theory and adaptive reward function were used in the proposed algorithm to achieve excellent stability and training efficiency. We demonstrated the effectiveness of the presented algorithm through comparative simulations.

In order to improve the computational accuracy of the AOA (angle of arrival) location, an AOA location method based on CTLS (constrained total least squares) and incorporating the effect of station errors is investigated. Its approximate closed-form solution is derived by first calculating the least squares solution, then substituting the least squares solution into the approximate closed-form solution and iterating to calculate the final result. Subsequently, since the result of the Fisher information matrix is minimized when the stations are uniformly distributed, the final accuracy of the AOA target location is improved by finding the optimal relative position deployment strategy for the sensor-carrying station in 3D space, under the assumption that the sensor carriers are uniformly distributed. Simulations show that, in the presence of station errors, the accuracy of AOA location is improved by approximately 10% using this paper’s calculation method, compared to the common least squares location method. In the process of target location, the Fisher information matrix is used to calculate the lower bound of the location result for the CRLB (Cramer–Rao lower bound), which is optimal when the azimuth is uniformly distributed and all on the same horizontal plane, if its pitch angle is 35.56°.