This paper addresses the anti-disturbance safety control problem in spacecraft inspection missions, considering multiple positional obstacle constraints and attitude restrictions, both forbidden and mandatory, with logical relationships. To address this challenge, a novel Composite Anti-Disturbance Safety Control (CADSC) method is proposed, which combines control barrier functions with disturbance observers. The proposed CADSC framework achieves guaranteed safety control under complex constraints while explicitly addressing external disturbances and model uncertainties. First, positional obstacles are modeled using quadratic surface equations. At the same time, attitude constraints are formulated with logical operators, incorporating the interactions among star trackers, optical cameras, solar panels, and space environment vectors. Then, safe velocity and angular velocity are computed by solving Quadratic Programming (QP) problems based on the spacecraft’s kinematic equations. The simplicity and disturbance-free nature of the kinematic model allow for efficient and accurate solutions to the QP problem, ensuring real-time applicability in mission-critical scenarios. Furthermore, proportional-like position and attitude controllers are developed to track the computed safe velocities. These controllers incorporate disturbance estimation techniques to compensate for external disturbances and model uncertainties, thereby enhancing the spacecraft’s robustness. Finally, numerical simulations are conducted to validate the effectiveness of the proposed control strategy.

This paper addresses the problem of end-effector position-tracking control for micro-nano free-floating space robots in Cartesian space without relying on explicit analytical kinematic or dynamic models. To address this challenge, we develop a two-layer learning architecture. In the first layer, a deep neural network is used for kinematic learning to capture the nonlinear mapping from end-effector Cartesian coordinates to joint angular velocities and to generate reference joint trajectories. In the second layer, a Koopman-operator-based network is employed to construct an approximately linearized representation of the joint-space dynamics of free-floating space robots. Based on this model, we propose a terminal fractional-order model predictive control scheme that incorporates the Grünwald-Letnikov fractional-order operator, thereby enhancing online control performance and improving tracking speed and accuracy relative to conventional model predictive control. Simulation results verify the effectiveness of the proposed method, demonstrating accurate and rapid end-effector trajectory tracking, all without requiring explicit analytical kinematic and dynamic models in the controller design, while the training pipeline relies solely on input-output trajectories.

This study addresses the challenge of ensuring safe spacecraft proximity operations, focusing on collision avoidance between a chaser spacecraft and a complex-geometry target spacecraft under disturbances. To ensure safety in such scenarios, a safe robust control framework is proposed that leverages implicit neural representations. To handle arbitrary target geometries without explicit modeling, a neural signed distance function (SDF) is learned from point cloud data via a enhanced implicit geometric regularization method, which incorporates an over-apporximation strategy to create a conservative, safety-prioritized boundary. The target's surface is implicitly defined by the zero-level set of the learned neural SDF, while the values and gradients provide critical information for safety controller design. This neural SDF representation underpins a two-layer hierarchcial safe robust control framework: a safe velocity generation layer and a safe robust controller layer. In the first layer, a second-order cone program is formulated to generate safety-guaranteed reference velocity by explicitly incorporating the under-approximation error bound. Furthermore, a circulation inequality is introduced to mitigate the local minimum issues commonly encountered in control barrier function (CBF) methods. The second layer features an integrated disturbance observer and a smooth safety filter explicitly compensating for estimation error, bolstering robustness to external disturbances. Extensive numerical simulations and Monte Carlo analysis validate the proposed framework, demonstrating significantly improved safety margins and avoidance of local minima compared to conventional CBF approaches.

This paper investigates the safety control problem in spacecraft proximity operations, focusing on collision avoidance between a complex-shaped servicing spacecraft and an arbitrarily shaped target spacecraft while also satisfying the line-of-sight constraint of its onboard camera. To solve this problem, a higher-order ellipsoid combination with arbitrary position and attitude is designed to approximate the complex shape of the servicing spacecraft, and a point cloud model is used to approximate the target spacecraft. The logsum-exp function is introduced to transform the original collision constraints related to multiple points in the point cloud model into a continuously differentiable collision avoidance constraint, thereby effectively constructing the collision avoidance constraints. The line-of-sight constraint is defined by combining the position, attitude, and camera field of view, ensuring that the target operating point is always within the camera's field of view. Additionally, the safety constraints with safety margin are designed by considering the tracking error bound. A safety-guaranteed trajectory is derived from a potentially unsafe reference trajectory by solving the control barrier function-based quadratic program problem. Finally, based on the control Lyapunov barrier function, we design a 6-degrees of freedom performance tracking controller that ensures the weighted sum of pose tracking error is always within the predefined tracking error bound. Numerical simulations, including comparative simulations and multiple trajectory cases, verify the effectiveness and practicality of the proposed strategy for handling collision and line-of-sight constraints. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

This article addresses the problem of robust end-effector pose control for micro-nano free-flying space robots operating in proximity to targets. To tackle this issue, we propose a hierarchical control framework that integrates trajectory generation, data-driven modeling, and contraction-based controller design. First, reference trajectories are generated via coordinate transformations between the current and target poses, leveraging reaction null-space techniques to prioritize end-effector pose accuracy while optimally preserving target observation through base-mounted camera orientation maintenance. Second, we introduce a structured deep bilinear Koopman framework that embeds domain-specific kinematic priors, enabling linear representations of the underlying nonlinear dynamics. This architecture achieves superior modeling accuracy and generalization over conventional Koopman approaches and facilitates efficient controller synthesis via its linear embeddings. Building on this formulation, we design a contraction-based feedback controller within the lifted Koopman space, ensuring stable pose tracking while mitigating the dependence on prior knowledge of the nonlinear dynamics for contraction metric construction. This method avoids computationally prohibitive metric searches in nonlinear regimes while providing formal stability guarantees. Simulations conducted in MuJoCo demonstrate the effectiveness of the proposed framework.

Secure attitude maneuvers of spacecraft in Very Low Earth Orbit (VLEO) are affected by composite disturbances, including center of mass variations and atmospheric drag. For systems subject to mandatory constraints, such disturbances can compromise attitude tracking precision and potentially lead to safety violations. To address this challenge, a maneuvering control scheme based on disturbance observation and barrier function is proposed. First, a coupled spacecraft attitude dynamics model is developed to reveal the influence of composite disturbances. Second, a composite controller incorporating a nonlinear disturbance observer and a state-dependent barrier function is designed to perform real-time compensation of these disturbances while ensuring constraints compliance. The effectiveness and robustness of the proposed approach are validated through numerical simulations.

This paper proposes a geometric control barrier function (Geometric CBF) for safety-critical systems evolving on SE(3), addressing the fundamental incompatibility between classical Euclidean safety constraints and Lie group dynamics. Traditional CBFs fail to capture the geometric structure of SE(3), where rotations and translations are inherently coupled. By rederiving CBF conditions through the Riemannian geometry of SE(3), we construct forward invariant sets that respect the group's differential structure, enabling provably safe coordination. A decentralized geometric CBF based safety-filter architecture is developed, where each agent resolves local quadratic programming (QP) problems using only neighbor-relative configurations, minimizing control modifications while guaranteeing safety. Numerical validation demonstrates the framework's capability to coordinate heterogeneous super-ellipsoid rigid bodies with complex geometries through intricate spatial maneuvers, maintaining provable safety guarantees during aggressive trajectory tracking.

T-type three-level inverters have been extensively utilized in renewable energy generation, motor drive systems, and other power conversion applications. However, failures in semiconductor devices critically reduce the operational reliability of power conversion systems. While significant progress has been made in the diagnosis of single-switch open-circuit (OC) faults, the precise location and detection of simultaneous double-switch OC faults remain challenging. Therefore, this paper proposes a fault diagnosis method, integrating an improved adaptive sliding mode observer (IASMO) and dynamic current threshold detection. First, the IASMO is constructed through the hybrid logic dynamic model, achieving accurate and rapid estimation of phase currents. Then, integrating estimated with actual currents accomplishes the design of detection variables and adaptive thresholds. Subsequently, fault location variables are formulated to achieve accurate localization of both single-switch and double-switch faults. Finally, Simulation and experimental results demonstrate that the proposed method effectively identifies 18 types of OC faults within 75% of the current cycle, with high efficiency and robustness.

In aerobic composting of food waste, acidification of the material (acidified food waste, AFW) often occurs and consequently leads to failure of fermentation initiation. In this study, we solved this problem by adding Saccharomyces cerevisiae inoculants. The results showed that the inoculation with S. cerevisiae effectively promoted the composting process. In 2 kg composting, inoculation with S. cerevisiae significantly elevated the pile temperatures by 4 14 °C, accompanied by a rapid increase in pH from 4.5 to 6.0. In 15 kg composting, total acid decreased faster and the thermophilic stage above 50 °C was prolonged by 3 days longer than in the control. The residual oxygen content in the reactor indicated that S. cerevisiae, which proliferated during composting, increased microbial activity and reduced ammonia emission during the thermophilic phase. Cell density analysis showed that compost inoculated with S. cerevisiae promoted thermophilic bacterial propagation. Metagenomic analysis showed that the dominant bacteria in the AFW compost were Firmicutes, Proteobacteria, Bacteroidetes, and Actinobacteria, and the relative abundance of Bacillus, Thermobacillus, and Thermobifida increased when inoculated with S. cerevisiae. These results indicate that the inoculation of S. cerevisiae is an effective strategy to improve the aerobic composting process of AFW by accelerating the initial phase and altering microbial community structure in the thermophilic phase. Our findings suggest that S. cerevisiae can be applied to aerobic composting of organic wastes to effectively address the problem of acidification.

This article addresses the challenge of achieving spacecraft attitude control with guaranteed performance while significantly reducing actuator activation frequency. To tackle this issue, we propose the concept of switched hybrid control and further integrate it with a modified prescribed-performance control (PPC) scheme. To enhance the robustness of the PPC control, we introduce the concept of a zeroing barrier function (ZBF). Coupled with a projection-operator-based modification dynamics, this approach assesses and adjusts the envelope in response to the risk of violating performance envelope constraints. Subsequently, a control mode switching strategy, considering the safety of the performance envelope and the system’s motion velocity, is proposed. This strategy automatically switches between intermittent and continuous control modes to select an appropriate control command execution strategy, thereby reducing actuator activation frequency under proper circumstances. Furthermore, we demonstrate the boundedness of the closed-loop system for different control modes and establish a uniform upper bound of the Lyapunov certificate throughout the entire time domain, thereby proving the overall uniformly ultimately bounded (UUB) of the system. Finally, numerical simulation results are presented to validate the effectiveness of the proposed control scheme.

Backup control barrier function (Backup CBF) is a tractable formulation that ensure the feasibility of CBF-based quadratic programming (QP) problems through implicitly defined control invariant set. Backup CBF is based on two fundamental ingredients: a known backup set and a fixed backup policy, and the implicitly defined control invariant set is evaluated through forward integration by backup policy in a finite horizon online. However, there is a lack of systematic methods for providing the backup set and backup policy for a system with input constraints, making it difficult to synthesize a backup CBF. In this paper, we propose a method for second-order systems to obtain backup policy and backup set practically, rather than empirical choice, by constructing a more conservative explicit CBF-QP with auxiliary velocity constraints integrated. The proposed method warms up a backup CBF by an explicit conservative CBF-QP under mild assumptions, while without excessive conservatism, allowing the tasks of safety and feasibility to be guaranteed theoretically. We demonstrate the efficacy of the proposed method in simulation.

Industrial robots are widely used in industrial production as mechanical devices. It is essential to guarantee that their control software operates safely and properly, as any functional or security-related defects may lead to serious incidents. However, industrial robots are programmed mostly in proprietary languages varying from vendor to vendor, making it challenging to formally analyze their correctness in a unified way. One of the most representative robot programming languages is the RAPID language proposed by ABB Robotics. In this paper, we present K-RAPID, a formal executable semantics of RAPID in the K-Framework (K). K-RAPID is developed according to the official ABB documentation and defined in a generic extensible manner. It can be used either for validating the correctness of compiler implementation or analyzing the control programs written in RAPID. We evaluate the correctness of K-RAPID by executing 563 test programs collected from multiple sources and comparing the results against the official robot simulation environment RobotStudio. The results suggest that K-RAPID covers the core features of RAPID correctly. Moreover, we show how we could apply K-RAPID to verify RAPID programs using LTL model checking and to provide a formal specification of RAPID to uncover inappropriate behaviors in the programs.

This paper focuses on the problem of spacecraft attitude control in the presence of time-varying parameter uncertainties and multiple constraints, accounting for angular velocity limitation, performance requirements, and input saturation. To tackle this problem, we propose a modified framework called Compatible Performance Control (CPC), which integrates the Prescribed Performance Control (PPC) scheme with a contradiction detection and alleviation strategy. Firstly, by introducing the Zeroing Barrier Function (ZBF) concept, we propose a detection strategy to yield judgment on the compatibility between the angular velocity constraint and the performance envelope constraint. Subsequently, we propose a projection operator-governed dynamical system with a varying upper bound to generate an appropriate bounded performance envelope-modification signal if a contradiction exists, thereby alleviating the contradiction and promoting compatibility within the system. Next, a dynamical filter technique is introduced to construct a bounded reference velocity signal to address the angular velocity limitation. Furthermore, we employ a time-varying gain technique to address the challenge posed by time-varying parameter uncertainties, further developing an adaptive strategy that exhibits robustness on disturbance rejection. By utilizing the proposed CPC scheme and time-varying gain adaptive strategy, we construct an adaptive CPC controller, which guarantees the ultimate boundedness of the system, and all constraints are satisfied simultaneously during the whole control process. Finally, numerical simulation results are presented to show the effectiveness of the proposed framework.

The control for spacecraft flying around mission is investigated in this paper based on the prescribed performance control (PPC). Considering the challenges in the traditional PPC framework, we design an adaptive adjustable performance function, which can self-adjust the performance requirement according to the saturation of the control input. Besides, a combined switching controller is designed and the controller behaves as a PPC in the convergence stage, and gradually switches to a nonlinear controller after entering a steady state, which can avoid the over-control problem caused by high-gain PPC after entering the steady state. Finally, the numerical simulations illustrate the effectiveness of the proposed control method.

This paper investigates the boresight alignment control problem under safety and performance requirements involving pointing-forbidden constraints, attitude angular velocity limitations, and pointing accuracy requirements. Meanwhile, the parameter uncertainty issue is taken into account simultaneously. To address this problem, we propose a modified composite framework integrating the Artificial Potential Field (APF) methodology and the PrescribedPerformance Control (PPC) scheme. The APF scheme ensures safety, while the PPC scheme is employed to realize an accuracyguaranteed control. A Switched Prescribed-Performance Function (SPPF) is proposed to facilitate the integration, which monitors various constraints and further establishes compatibility between safety and performance concerns by presenting a special PPC freezing mechanism. To further address the parameter uncertainty, we introduce the Immersion-and-Invariance (I&I) adaptive control technique to derive an adaptive APF-PPC composite controller, guaranteeing the closed-loop system's asymptotic convergence. Finally, numerical simulations are carried out to validate the effectiveness of the proposed scheme.

This paper investigates the optimal tracking control problem of free-floating space robots in the presence of non-ideal factors, such as model uncertainties and external disturbances. To address this issue, we first leverage the deep Koopman operator, facilitated with a deep neural network, to establish an offline formulation of a global linearization model for a micro-nano free-floating space robot. Based on the estimated linearization model, we then employ the model predictive control method for online optimization control, achieving a significantly reduced computational burden. Additionally, a decay factor is integrated into the model predictive control optimization objective function to balance the precision of joint angles tracking with the suppression of base satellite attitude disturbance. To further address the modeling inaccuracies and external disturbances, we incorporate a disturbance observer based on a radial basis function neural network for online compensation within the model predictive control framework. This augmentation enhances tracking precision and robustness. Several groups of simulation results are carried out to demonstrate the effectiveness of the proposed method, showing its capability of energy consumption, disturbance rejection and enhanced robustness. These highlight the potential of the proposed method to improve the control performance of free-floating space robots, even in the absence of a precise dynamic model.

In this paper, the safety control problem for spacecraft inspection mission is investigated, which means that there is no position and attitude obstacle collisions between the inspection spacecraft and the defined forbidden zones. We propose a control strategy that ensures safety, utilizing the control barrier function to address multiple safety constraints with logical relationships among them. Firstly, the position and attitude constraints are defined, and the hybrid logical relationship among different attitude constraints is analyzed. To handle complex position and attitude constraints, the Kreisselmeier-Steinhauser function is introduced to approximate the max/min function, thereby expressing them as continuous and differentiable functions. Subsequently, the control barrier function-based quadratic programming (CBF-QP) problems are solved to generate safe velocity and angular velocity. These problems have explicit solutions and do not require extensive computational resources. The safe position and attitude are then obtained by integrating the safe velocity and angular velocity. Notably, the forbidden regions are expanded by a constant in the construction of CBF-QP problems, which acts as a margin for the tracking controller’s performance. Finally, attitude and position tracking controllers based on the control Lyapunov barrier function (CLBF) are designed respectively, taking into account the lumped disturbance in the system, which means that the attitude and position control are computed independently. Numerical simulation results demonstrate that the proposed control strategy effectively handles complex constraints in inspection missions, ensuring system stability and safety, even under severe conditions.

In this paper, we proposed a distance based prescribed performance switching control for spacecraft flying around mission. The proposed control strategy investigates the chattering phenomenon and unreasonable prescribed performance function (PPF) problems in prescribed performance control (PPC) frame work. For the chattering phenomenon, a switch control strategy is designed and the control input is switched between the PPC and a nonlinear controller, then the chattering phenomenon can be avoided as the nonlinear controller doesn't contain the infinity term. As for the unreasonable PPF, an adaptive PPF is designed based on a second order system and the external input of the system is related to unattainable control force. Then the stability of the switched system is analysis based on the Lyapunov theory. Finally, numerical simulations are conducted and the effectiveness of the proposed control strategy is illustrated.

This paper investigates the safety guaranteed problem in spacecraft inspection missions, considering multiple position obstacles and logical attitude forbidden zones. In order to address this issue, we propose a control strategy based on control barrier functions, summarized as "safety check on kinematics" and "velocity tracking on dynamics" approach. The proposed approach employs control barrier functions to describe the obstacles and to generate safe velocities via the solution of a quadratic programming problem. Subsequently, we design a proportional-like controller based on the generated velocity, which, despite its simplicity, can ensure safety even in the presence of velocity tracking errors. The stability and safety of the system are rigorously analyzed in this paper. Furthermore, to account for model uncertainties and external disturbances, we incorporate an immersion and invariance-based disturbance observer in our design. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed control strategy.

This paper focuses on the spacecraft attitude control problem with intermittent actuator activation, taking into account the attitude rotation rate limitation and input saturation issue simultaneously. To address this problem, we first propose a composite event-trigger mechanism, which composed of two state-dependent trigger that governing the activation and deactivation of actuators. Subsequently, by introducing the cascaded decomposition of Backstepping control philosophy, the designed trigger mechanism is then applied to the decomposed dynamical subsystem, providing a layered intermittent stabilization strategy. Further, the basic intermittent attitude controller is extended to a "constrained version" by introducing a strictly bounded virtual control law and an input saturation compensation auxiliary system. By analyzing the local boundedness of the system on each inter-event time interval, a uniformly, strictly decreasing upper boundary of the lumped system is further characterized, thereby completing the proof of the system's uniformly ultimately boundedness (UUB). Finally, numerical simulation results are illustrated to demonstrate the effectiveness of the proposed scheme.