Aiming at maneuvering, input saturation, and communication interference in the controller design for formation control multi-agent systems, a novel nonlinear bounded controller is proposed. Based on coordinates transformation, reference information is processed, and nonlinear effects of maneuvering are analyzed. Then a nonlinear controller is established with graph theory, consensus algorithm, and Lyapunov method, which guarantee the stability of the controller. For input saturation avoidance, adaptive parameters are put forward with the Lyapunov function. Considering the communication breaks, various conditions of the sensing graph are discussed for stable formation control, and a dynamic programming regulator is proposed for unknown position reference needed for formation keeping. Comparison with the traditional consensus method is provided in numerical simulation to verify the stability and feasibility of the proposed strategy.
To address the problem and reduce the risk wherein a ship‐based cross‐eye jammer could set a protected ship as a target, an unmanned platform equipped with a jammer is proposed to be used. However, the jamming situation violates the original cross‐eye jamming conditions and introduces some new elements. In this regard, the impact of outboard cross‐eye jamming in the presence of ship echoes is derived in detail. The analysis shows that the outboard cross‐eye jamming combines the characteristics of outboard active and traditional cross‐eye jamming gains. It can reduce the standard cross‐eye jamming requirement of the jamming‐to‐signal ratio (JSR) and improve the jamming effect. The phase difference of a cross‐eye jammer can be controlled at 0° or 180° to obtain a larger angular positioning deviation based on the JSR requirement, thus providing a theoretical basis for applications of unmanned outboard cross‐eye jamming.
This paper conducts research on obstacle and collision avoidance under kinematical constraints in unknown environments, while communication and simultaneous localization and mapping (SLAM) are unavailable to agents. Then a strategy based on mixed-integer programming is proposed, in which velocity constraints are established with a modified Barrier function for obstacles that can be completely detected. As for obstacles that cannot be completely detected, a feasible set is built for the velocity programming based on the convex theory, and the contradicted constraints are addressed with the logic metric method. Besides, the actuator saturation is avoided by converting kinematical constraints into the restrictions on the magnitude, the restrictions on the direction, and the negative correlations between the components of the velocity respectively. Given that invalid nominal velocity leads to collisions, a virtual goal is estimated for nominal velocity improvement. In addition, the local extremum brought by empty programming space due to multiple constraints is repaired by fixing the velocity constraints. The feasibility of the proposed strategy is analyzed, and numerical simulations are provided to verify the effectiveness of the proposed strategy.
This paper conducts research on collision and obstacle avoidance of multi-agent systems without mapping ability, while the constrained agent can only detect obstacles within a limited distance, then a velocity programing strategy is proposed considering the lack of a high-resolution map and the challenge of the modeling of complex obstacles. Based on the detecting information of nearby members and obstacles, a discontinuous velocity programing space is constructed by imposing the constraints on the velocity. To obtain expansive programing space, two different ways are utilized to establish the velocity constraints of avoiding various obstacles. For obstacles that can be viewed as virtual circular obstacles, a barrier function is introduced to restrict the radial component of the velocity. As for the obstacle that can only be detected partially, we use the border lines to construct a velocity feasible domain, and the domain is approximated by the polygonal region using the convex theory. Then, the nominal velocity is utilized as the objective and a nonlinear dynamic programing regulator is proposed. Furtherly, velocity limits generated from the system kinematic constraints are incorporated into the regulator. Finally, three tests are carried out and the feasibility of the proposed regulator is verified.
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