This letter considers autonomous rendezvous maneuver and proximity operations of two spacecraft in presence of obstacles. A strategy that combines guidance and control algorithms is analyzed. The proposed closed-loop system is able to guarantee a safe path in a real environment, as well as robustness with respect to external disturbances and dynamic obstacles. The guidance strategy exploits a suitably designed artificial potential field (APF), while the controller relies on sliding mode control (SMC), for both position and attitude tracking of the spacecraft. As for the position control, two different first order SMC methods are considered, namely the component-wise and the simplex-based control techniques. The proposed integrated guidance and control strategy is validated by extensive simulations performed with a six degree-of-freedom (DOF) orbital simulator and appears suitable for real-time control with minimal on-board computational effort. Fuel consumption and control effort are evaluated, including different update frequencies of the closed-loop software.

Unmanned Aerial Vehicles (UAVs) operating in complex environments require advanced control strategies beyond traditional linear approaches. This work presents a comprehensive comparative analysis of Nonlinear Model Predictive Control (NMPC) and Model Predictive Path Integral (MPPI) control for UAV trajectory tracking, with an emphasis on real-time implementation feasibility on embedded hardware. A modular ROS 2 framework enables runtime controller selection using CasADi/Acados for NMPC and JAX for MPPI implementations. Processor-in-the-Loop experiments on NVIDIA Jetson Orin Nano hardware evaluate computational performance under realistic resource constraints. Results demonstrate that MPPI achieves superior tracking performance, with an 18.6% improvement in overall RMSE compared to NMPC (0.8480 m to 0.6897 m) for trajectory following. Both controllers achieve real-time performance on embedded hardware, with GPU acceleration proving critical for MPPI success, enabling a 17.63 ms median computation time versus 31.02 ms for CPU-only execution. Systematic parameter analysis reveals optimal MPPI configurations of 40 horizon steps and 800–1250 samples for balancing performance with computational constraints imposed by the 50 Hz (20 ms) control frequency inherent to PX4 hardware compliance. This study validates that mainstream computational frameworks can deliver satisfactory real-time control performance on standard robotics hardware, significantly enhancing accessibility for practical UAV deployment while providing clear guidelines for control strategy selection in resource-constrained applications.

This paper considers a robust sliding mode control method for a spacecraft attitude control. The design of the control law is based on the reaching law approach for continuous-time systems. A novel method is proposed to design the parameters of both the reaching law and the sliding surface. The reaching law ensures that during the reaching phase the states of the system remains bounded. Then, taking into account the parameters of the mathematical model, the bounds are defined so that the control law does not overload the actuator limits, whatever the initial conditions. Furthermore, a variable gain is considered for the control law, to provide chattering alleviation of the control input. Numerical simulations are performed to show the effectiveness of the proposed approach.

This paper presents an adaptive observer-based navigation strategy for spacecraft in Circular Relative Orbit (CRO) scenarios, addressing challenges in proximity operations like formation flight and uncooperative target inspection. The proposed method adjusts observer gains based on the estimated state to achieve fast convergence and low noise sensitivity in state estimation. A Lyapunov-based analysis ensures stability and accuracy, while simulations using vision-based sensor data validate the approach under realistic conditions. Compared to classical observers with time-invariant gains, the proposed method enhances trajectory tracking precision and reduces control input switching, making it a promising solution for autonomous spacecraft localization and control.