Multi-robot coordination is fundamental to various applications, including autonomous exploration, search and rescue, and cooperative transportation. This paper presents an optimal consensus framework for multi-robot systems (MRSs) that ensures efficient rendezvous while minimizing energy consumption and addressing actuator constraints. A critical challenge in real-world deployments is actuator limitations, particularly wheel velocity saturation, which can significantly degrade control performance. To address this issue, we incorporate Pontryagin Minimum Principle (PMP) into the control design, facilitating constrained optimization while ensuring system stability and feasibility. The resulting optimal control policy effectively balances coordination efficiency and energy consumption, even in the presence of actuation constraints. The proposed framework is validated through extensive numerical simulations and real-world experiments conducted using a team of Robotarium mobile robots. The experimental results confirm that our control strategies achieve reliable and efficient coordinated rendezvous while addressing real-world challenges such as communication delays, sensor noise, and packet loss.

This paper proposes a safe reinforcement learning filter (SRLF) to realize multicopter collision-free trajectory tracking with input disturbance. A novel robust control barrier function (RCBF) with its analysis techniques is introduced to avoid collisions with unknown disturbances during tracking. To ensure the system state remains within the safe set, the RCBF gain is designed in control action. A safety filter is introduced to transform unsafe reinforcement learning (RL) control inputs into safe ones, allowing RL training to proceed without explicitly considering safety constraints. The SRLF obtains rigorous guaranteed safe control action by solving a quadratic programming (QP) problem that incorporates forward invariance of RCBF and input saturation constraints. Both simulation and real-world experiments on multicopters demonstrate the effectiveness and excellent performance of SRLF in achieving collision-free tracking under input disturbances and saturation.

This paper designs a servo control system based on sliding mode control for the shape control of elastic objects. In order to solve the effect of non-smooth and asymmetric control saturation, a Gaussian-based continuous differentiable asymmetric saturation function is used for this goal. The proposed detection approach runs in a highly real-time manner. Meanwhile, this paper uses sliding mode control to prove that the estimation stability of the deformation Jacobian matrix and the system stability of the controller are combined, which verifies the control stability of the closed-loop system including estimation. Besides, an integral sliding mode function is designed to avoid the need for second-order derivatives of variables, which enhances the robustness of the system in actual situations. Finally, the Lyapunov theory is used to prove the consistent final boundedness of all variables of the system.