In this article, we propose a self-triggered distributionally robust model predictive control algorithm for linear discrete systems with state chance constraints and unbounded stochastic disturbances. Assuming that only the first and second moments of the disturbance are accessible, we transform the objective function into a compact quadratic form and reformulate the state chance constraints into linear inequalities, which is more tractable when solving. In order to reduce communication and sampling times of the system, we propose a self-triggered update scheme, in which the state sampling and the control input sequence are updated when the control performance predicted based on the current sampling exceeds that of the periodic sampling scheme. We demonstrate that the optimization problem in the proposed self-triggered model predictive control (MPC) method is recursively feasible and stable. Numerical simulation results verify the effectiveness of the proposed algorithm.

This article studies 2-D formation stabilization and maneuvering of mobile agents governed by double-integrator dynamics. The desired formation is described by a set of triple-agent interior angles. A carefully chosen such set of angle constraints guarantees that the desired formation is angle rigid. To achieve the desired angle rigid formation, a stabilization control law is proposed using only local velocity and direction measurements. We show that the closed-loop dynamics of the formation, when each agent is modeled by a double-integrator, are closely related to the corresponding one in single-integrator agent dynamics. Sufficient conditions are constructed to guarantee the closed-loop stability for identical and distinct velocity damping gains, respectively. To guide an angle rigid formation to move with the desired translational velocity, orientation, and scale, formation maneuvering laws are then proposed. Simulation examples are also provided to validate the results.

Angle rigid formations have the advantage of requiring only local bearing/direction measurements in their implementation. However, the capability of controlling the orientation and scale of these formations has not been explored. This undetermined orientation and scale can degrade the robustness of the formation against measurement noise. To maintain both advantages of requiring less sensor measurements and sustaining robustness against measurement noise, this article aims to achieve a desired angle rigid formation while simultaneously controlling its orientation and scale. In this article, we first design a formation algorithm for the first three agents to achieve a desired triangular formation with prescribed orientation and scale. Using the control gain design technique, we then design formation control algorithms for the remaining agents such that the overall desired formation can be achieved under a vertex addition operation. We present the role of generic property from angle rigidity for the formation’s stability analysis. We also highlight that with one additional relative position measurement or two additional communication channels, the local convergence to the corresponding desired formation can be improved to a global convergence. Experiments are conducted to validate the theoretical results and the advantages are highlighted in comparison with other two formation control laws.

This paper utilizes the distributed model predictive control (DMPC) method to investigate the formation control problem of unmanned aerial vehicles (UAVs) in the obstacle environment and establishes cooperative capability evaluation metrics of the swarm. Based on the DMPC approach, the formation cost function is constructed to adjust the relative positions and velocities of UAVs, ensuring the desired formation. Additionally, to address the obstacle avoidance problem in the formation, the obstacle avoidance function is designed to provide safe formation control in the obstacle environment. To evaluate the cooperative capability of UAVs, we design evaluation metrics from multiple dimensions to reflect the swarm’s cooperative capability. Finally, the simulation results show the effectiveness of the formation control method with obstacle avoidance and the applicability of the swarm’s cooperative capability evaluation metrics.