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.

This article addresses the optimal control problem for a class of continuous-time nonlinear systems with time-varying bounded disturbances. A novel approximate optimal control policy that incorporates a continuous robust control command is developed within the framework of adaptive dynamic programming. It makes the controlled system robust to the bounded time-varying disturbance rather than just the disturbance that vanishes as the system state converges. Moreover, an improved concurrent learning neural network (NN) weight updating algorithm that involves a switching mechanism is presented, and it can work without persistent/finite assumptions. Then, with the uniformly ultimately bounded stability guaranteed by Lyapunov theory, the closed-loop controlled system achieves approximate optimality and strong robustness. The superiority and effectiveness of the suggested control approach have been demonstrated via robotic trajectory tracking experiments.

In this paper, a variable structure PID controller with a good convergence rate and robustness for satellite attitude is proposed. In order to improve the system convergence rate, the variable structure for the proportional and differential term was designed, and an angular velocity curve with a better convergence rate was achieved by this variable structure. In addition, an integral partitioning algorithm was designed, and the system robustness to disturbance torque was improved; meanwhile, the negative effect of the integral term was avoided during the converging process. The actuator failure condition was also considered, and a fault tolerant control algorithm was designed. System stability was analyzed by the Lyapunov method, and its performance was demonstrated by numerical simulation.

This paper presents a simultaneous task assignment and trajectory planning method for unmanned system swarm by using optimal transport and model predictive control (OT-MPC). Unlike the conventional hierarchical assignment and planning, the proposed approach addresses both the task assignment and trajectory planning subproblems concurrently. To be specific, a unified cost function is designed to solve task assignment and trajectory planning problem. Moreover, the multi-tasks are assigned by using optimal transport, which establishes an optimal mapping between tasks and unmanned system vehicles based on transportation cost. The trajectory planning is achieved by using model predictive control, which generates high-quality navigation trajectories considering obstacle avoidance. Finally, the proposed method is applied to the unmanned surface vehicles swarm. Numerical simulations and experiments were conducted to validate the effectiveness of the proposed method.

This study is dedicated to address the predefined-time attitude tracking control problem of spacecraft in the presence of actuator fault, unmeasurable angular velocity, system uncertainties, and external disturbance. Invoking the attitude measurement feedback only, a model-free adaptive state observer is designed to estimate the unmeasurable angular velocity. Its estimation error is governed to be stable with predefined-time convergence. Using this estimation, a continuous and non-singular sliding surface-based predefined-time adaptive controller is synthesized. This controller does not have singularity and is free of chattering. It ensures the attitude tracking error to be stable with predefined-time convergence. The proposed controller's effectiveness is finally verified through numerical examples.

The macro high-level control problem of microsatellite swarm subject to safety constraints is studied in this paper. The macroscopic motion dynamics of this swarm is described by the probability transition of the Markov chain with its state described by probability density distribution. Based on this model, a macro equilibrium density controller is first presented. Unlike the conventional global measurement-based control methods, this controller is implemented by using local measurement, which is provided by a decentralized counting method. The computation and the communication burden can be decreased significantly. Then, a fast temporary transition control effort is designed and added to the macro equilibrium density controller to synthesize a robust and fast macro density controller. It is proved that the swarm's state can asymptotically and fast converge to the desired density distribution. The key features of this robust controller are that it can not only balance the swarm transition cost and the transition rate despite multiple constraints, but also has great robustness to initial states. The swarm control can be achieved with fast convergence rate even for the case that the microsatellites are extensively crowded at the initial. The effectiveness of this control scheme is finally verified by numerical simulation.