This paper addresses the problem of predefined-time attitude tracking control for rigid spacecraft subject to external disturbances and unknown inertia parameters. First, a predefined-time non-singular sliding surface is designed to ensure that the closed-loop system converges within a predefined time. Second, to tackle the unwinding problem inherent in quaternion-based modeling, a potential function is introduced in the controller design to guarantee anti-unwinding performance even outside the sliding surface. The proposed controller not only suppresses chattering but also ensures both anti-unwinding behavior and predefined-time convergence. Then, an event-triggered mechanism is developed to reduce communication burden while avoiding Zeno behavior. The proposed control method can make the attitude tracking error converge to an arbitrary predefined residual set. Finally, simulation results verify the effectiveness of the proposed method.

This paper introduces a novel neural network adaptive fractional-order sliding mode control strategy based on the minimal learning parameter method (MLPNN-FOSMC). This method aims to solve the problem of relative position control in satellite formation flying (SFF), especially in the presence of model uncertainties, external disturbances, input saturation, and unknown actuator gains. First, the mathematical model for satellite relative positioning is derived. Then, a fractional-order sliding mode controller is introduced and integrated with a radial basis function (RBF) neural network and the minimal learning parameter (MLP) strategy to compensate for errors caused by model uncertainties and external disturbances. At the same time, adaptive control is employed to mitigate the impact of unknown actuator gains on the system. To address the non-smooth input saturation nonlinearity problem, a new saturation function with the smoothing properties of the hyperbolic tangent function is introduced. The system’s stability is ensured by employing the Lyapunov theorem. Finally, a comparative analysis with traditional sliding mode control (SMC) and neural network sliding mode control based on minimal learning parameter (MLPNN-SMC) highlights the superiority of the proposed method.

In this paper, a novel, effective, and feasible autonomous phase control algorithm based on the extended Kalman filtering (EKF) and neural network is firstly developed for addressing the problems of configuration maintenance of remote sensing satellite constellation. A balanced moment arm optimization method is employed to design the installation structure layout of the chemical propulsion system in the satellite. On that basis, an autonomous orbit control strategy is presented for controlling the phase of satellites, and the EKF algorithm is utilized to determine the orbit used to calculate the satellite phase. A radial basis function (RBF) neural network-based attitude control method is proposed to solve the attitude disturbance problem in the course of the phase control, and the RBF neural network is utilized to approximate the coupling torque of the orbit control. The simulation results demonstrate the feasibility of the designed automatic phase control strategy of the satellite.

This study explores the challenge of tracking control for spacecraft formation flying (SFF) in the presence of dynamic uncertainties and external perturbations. Firstly, sliding mode control combined with backstepping control is used to address saturation issues. Then, neural networks, minimal parameter learning, and adaptive control are integrated to handle dynamic uncertainties and actuator failures. To alleviate the communication load, an event‐triggered mechanism is ultimately implemented, which leads to the development of an adaptive sliding mode fault‐tolerant control algorithm based on an event‐triggered neural network. This control architecture achieves significant advancements over traditional techniques: (1) ensuring system robustness and adaptability in complex scenarios with uncertain system dynamics and external disturbances, effectively counteracting actuator failures and input saturation issues; (2) significantly reducing transmission and computational burdens in resource‐limited networked systems through the adoption of event‐triggered control (ETC) mechanisms; (3) achieving high‐precision tracking performance for SFF without relying on prior knowledge of the system’s inherent dynamics, environmental disturbances, or potential actuator deficiencies. The Lyapunov approach is utilized to confirm the closed‐loop system’s boundedness. Finally, the proposed method’s efficacy is confirmed via simulations with a two‐satellite formation.

Mobile robot navigation in complex environments requires efficient global planning and reactive local obstacle avoidance. This paper proposes a hybrid path planning algorithm that integrates an improved Bidirectional Rapidly-exploring Random Tree (BI-RRT) for global guidance with an enhanced Dynamic Window Approach (DWA) for local execution. The BI-RRT algorithm incorporates a multi-sampling point strategy, dynamic step-size adjustment, and an Artificial Potential Field (APF)-inspired node selection mechanism to reduce sampling redundancy and improve path quality. The DWA evaluation function is augmented with historical trajectory information and dynamic obstacle prediction to enable smoother avoidance of moving obstacles. The hierarchical architecture establishes a continuous transition from global path optimization to local obstacle avoidance, with the global BI-RRT path providing waypoints for the local DWA controller. Experimental validation was conducted through MATLAB simulations across three environmental scales (50 × 50, 100 × 100, and 200 × 200) with obstacle densities ranging from 15% to 25%, ROS-based tests, and physical experiments on a Jetauto robot platform. Compared to baseline methods including RRT*, BI-RRT, APF-RRT*, and Informed-RRT*, the proposed algorithm improves search efficiency, achieving an 88–93% reduction in sampling nodes and 89–97% reduction in computation time compared to standard RRT*, while maintaining 100% success rate across tested environments. Physical robot experiments demonstrate 8.17% reduction in path length, 13.85% reduction in execution time, and 40% reduction in turning maneuvers compared to conventional BI-RRT. The algorithm maintains highly reliable navigation performance across the specifically evaluated test environments, including dynamic obstacle scenarios, demonstrating its viability under the tested conditions.