Centroidal Voronoi Tessellation and Model Predictive Control–Based Macro-Micro Trajectory Optimization of Microsatellite Swarm

Abstract

Probabilistic swarm guidance enables autonomous microsatellites to generate their individual trajectories independently so that the entire swarm converges to the desired distribution shape. However, it is essential to avoid crowding for reducing the possibility of collisions between microsatellites. To determine the collision-free guidance trajectory of each microsatellite from the current position to the target space, a collision avoidance algorithm is necessary. A synthesis method is proposed that generate the collision avoidance trajectories. The idea is that the trajectory planning is divided into macro-planning and micro-planning; macro-planning guides where the microsatellites move step by step from the initial cube to the target cube by probabilistic swarm guidance with Centroidal Voronoi tessellation, while the micro-planning is to generate the optimal path for each step and finally reach the specified position in the target cube by model predictive control. Simulation results are presented for the collision-free guidance trajectory of microsatellites to verify the benefits of this planning scheme.

Disturbance observer‐based model predictive control of a coaxial octorotor with variable centre of gravity

“…The rotors moment of inertia (J tp ) is assumed to be 2.8 × 10 −5 . The rotors angular speed has been limited as 0 ≤ 𝜔 i ≤ 700 ( rad s ), which based on the force generation relationship in (22) will result in lifting force bounds as 0 ≤ F i ≤ 14.6 (N). The position and attitude controller parameters are tuned as…”

“…Various researches have been conducted in the field of MPC for UAVs and its application has been successful. In [22], MPC was used as a part of collision‐free guidance trajectory generation algorithm for microsatellites to guide them from current position to the target space. In [23], the active disturbance rejection and predictive control methods have been used for translational and rotational dynamics control of a quadrotor UAV, respectively.…”

Attack‐defense differential game to strength allocation strategies generation

“…However, confrontation situation would like to be changed in practice (e.g., a large number of strength reinforced one side, causing a change in the confrontation situation). Therefore, as one of our future work, receding horizon optimization technique 41 can be carried out to handle the strength time‐vary attack‐defense strength allocation strategies generation problem.…”

Distributed prescribed‐time leader‐following formation control for second‐order multi‐agent systems with mismatched disturbances

“…As the unbounded gain goes to infinity, the error states go to zero which results in the prescribed‐time regulation, that is, the convergence time can be offline pre‐assigned. Furthermore, the power terms of the error states in the finite‐/fixed‐time control protocols 3,57 make the controller more complex and difficult to be implemented in practical applications. By adopting the time scaling functions, the proposed control protocol is simpler and easier to be implemented.…”

“…During the past few decades, the cooperative control of a multi-agent system (MAS) has received considerable attention and been studied extensively due to its widely applications in military and civil areas, such as surveillance, firefighting, and express delivery. [1][2][3] The existing topics can mainly be classified as the consensus problem, the tracking problem, and the formation problem. 4 Consider the consensus problem, the information exchanged among the agents is utilized to converge the states of the agents to a common value.…”