This letter presents an adaptive coverage control strategy for multi-agent systems with obstacle avoidance in the presence of actuator faults and time-varying uncertainties. The strategy is based on a leader-follower approach. Assuming that the motion of the leader is given, one distributes the followers within the leader's obstacle-free sensing range so that collisions with obstacles can be avoided. An optimized distribution is achieved through the Centroidal Voronoi Tessellation (CVT) and a function approximation technique based immersion and invariance (FATII) coverage controller is constructed to realize the CVT. The stability of the FATII coverage controller is established and its validity is tested by simulations.

This paper focuses on the cooperative localization of maneuvering targets on the ground from unmanned aerial vehicles (UAVs) with bearing-only measurements. A maneuvering target tracking algorithm is proposed based on the interactive multiple models (IMM) framework. In order to fully utilize the localization redundancies and improve the accuracy of target localization in multi-UAVs system, a belief factor for state fusion is proposed considering the positioning and measurement capabilities of each UAV. Moreover, a trajectory smoothing algorithm is proposed by the Quasi-uniform B-spline (QUBS) method, which is utilized for reducing the localization error caused by measurement noise. By calculating the discrete curvature of the maneuvering target trajectory, an adaptive identification method of turn rate is derived to alleviate the delay of the model switch. Besides, a maneuvering target tracking model is established and a unified framework based on the IMM method is proposed to improve the applicability of the maneuvering target tracking. Simulation results demonstrate that the proposed turn rate identification based on IMM (TRIIMM) algorithm outperforms the other algorithms in terms of tracking accuracy and takes the least time cost.

This paper presents a robust prescribed performance control approach and its application to nonlinear tail-controlled missile systems with unknown dynamics and uncertainties. The idea of prescribed performance function (PPF) is incorporated into the control design, such that both the steady-state and transient control performance can be strictly guaranteed. Unlike conventional PPF-based control methods, we further tailor a recently proposed systematic control design procedure (i.e. approximation-free control) using the transformed tracking error dynamics, which provides a proportional-like control action. Hence, the function approximators (e.g. neural networks, fuzzy systems) that are widely used to address the unknown nonlinearities in the nonlinear control designs are not needed. The proposed control design leads to a robust yet simplified function approximation-free control for nonlinear systems. The closed-loop system stability and the control error convergence are all rigorously proved. Finally, comparative simulations are conducted based on nonlinear missile systems to validate the improved response and the robustness of the proposed control method.

The rapid development of the anti-missile weapon systems brings new challenges to the cooperative penetration strategy solution and the guidance law design for hypersonic vehicles. This paper studies the cooperative game penetration guidance problem for multiple hypersonic vehicles with performance constraints. A cooperative differential game model for hypersonic vehicles is proposed wherein a cooperative gain is incorporated to assign maneuvering ratios in different cooperative missions and the ground target is considered as a member of the interceptor swarm to be able to attack the target after penetration. Then, an improved prescribed performance adaptive dynamic programming (ADP) algorithm is developed to seek the Nash equilibrium guidance solution quickly and accurately. In order to ensure the steady-state and transient approximation performance of neural networks strictly, a prescribed performance function (PPF) is introduced into the weight updating law design. Using the developed strategies, it is proven that the desired approximation performance in ADP can be achieved. Finally, a comparative simulation with a two-on-three game is conducted to verify the effectiveness of the proposed method.

This paper investigates a velocity tracking control approach for air‐breathing supersonic vehicles with uncertainties and external disturbances. Considering angle of attack is difficult to be precisely measured in practice, extended state observer technique is introduced into the state reconstruction design. In order to avoid possible oscillations in the design of the traditional extended state observer (TESO), a modified extended state observer (MESO) is developed, where a new smooth function is proposed to replace nonsmooth function of TESO. On the basis of it, an active disturbance rejection controller (ADRC) is designed for velocity control systems. Simultaneously, the closed‐loop stability is rigorously proved by using Lyapunov theory. Finally, numeric simulations are conducted to validate the effectiveness of the proposed method.

This paper proposes an improved back-stepping control approach and its application to small nonlinear UAV control systems with uncertainties such as external disturbance. Unlike traditional back-stepping control methods, the idea of prescribed performance function (PPF) is incorporated into the control design, such that both the transient and steady-state control performance can be strictly guaranteed. Moreover, we design a novel tracking differentiator to avoid the "differential expansion" problem well caused by the calculation of derivative. Significantly, the function approximators (e.g. neural networks) that are widely used to address the unknown nonlinearities in the nonlinear control designs are not needed. Finally, the numerical simulation verifies the convergence and robustness of the system, and the results show that the control strategy can obtain better transient and steady-state performance.