This letter proposes a novel scheme for resilient estimation under random sensor failures and colored measurement noise, without requiring hardware-redundant designs or complex adaptive mechanisms. The key insight is to leverage prior constraints to increase information redundancy. Resilience against both disturbances is achieved through a Kalman-Petovello filter, which is derived by whitening the colored noise via measurement differencing and incorporating the prior constraints as pseudo measurements. Theoretical analysis and numerical simulations demonstrate the efficacy of the proposed scheme.

This article investigates the collaborative transportation planning and control of tethered multi-rotor unmanned aerial vehicles within intelligent transportation systems. These unmanned aerial vehicles handle heavy-load delivery, including emergency airdrop and aerial assembly of structural components. To ensure algorithm generality, obstacles, loads, and unmanned aerial vehicles are modeled as unions of convex sets. Collision avoidance constraints, originally nondifferentiable due to convex set distances, are exactly reformulated into differentiable forms via strong duality. This leads to a smooth, optimization-based trajectory planning framework with obstacle avoidance. Considering composite disturbances, a robust nonlinear distributed model predictive control strategy based on constraint tightening is developed, ensuring robust feasibility and stability without terminal constraints. Numerical simulations in cluttered environments validate the method’s effectiveness and applicability to next-generation aerial logistics and emergency response in complex terrains.

Motivated by the practical challenges of tethered satellite formations (TSFs), this article focuses on the state estimation problem for systems with limited payload capacity, subject to probabilistic sensor failures and complex soft constraints. As prior knowledge, soft constraints reveal the interdependence of internal states, providing insights into observability preservation under probabilistic sensor failures. Unlike existing approaches, we propose a soft-constrained estimation scheme that fully leverages prior constraint knowledge to preserve observability and enhance performance via a constrained particle filter (CPF). Within the Bayesian framework, the CPF fully leverages the soft constraints to truncate both the prior and posterior distributions. The convergence analysis is also presented. Based on this scheme, we investigate the maximum tolerable sensor failures for TSF. Surprisingly, it is proven thatn-body TSF ($n \ge 3$) with typical configurations can tolerate up ton-1 positioning sensor failures. This proof enables mission designers to sustain system observability even with up ton-1 sensor failures, thereby obviating redundant configurations while ensuring orbital mission reliability. Extensive simulations validate the effectiveness of the proposed scheme and its filter performance.

This work focuses on the safety of learning-based control for unknown nonlinear system, considering the stability of learned dynamics and modeling mismatch between the learned dynamics and the true one. A learning-based scheme imposing the stability constraint is proposed in this work for modeling and stable control of unknown nonlinear system. Specifically, a linear representation of unknown nonlinear dynamics is established using the Koopman theory. Then, a deep learning approach is utilized to approximate embedding functions of Koopman operator for unknown system. For the safe manipulation of proposed scheme in the real-world applications, a stable constraint of learned dynamics and Lipschitz constraint of embedding functions are considered for learning a stable model for prediction and control. Moreover, a robust predictive control scheme is adopted to eliminate the effect of modeling mismatch between the learned dynamics and the true one, such that the stabilization of unknown nonlinear system is achieved. Finally, the effectiveness of proposed scheme is demonstrated on the tethered space robot (TSR) with unknown nonlinear dynamics.

Event cameras, when combined with inertial sensors, show significant potential for motion estimation in challenging scenarios, such as high-speed maneuvers and low-light environments. While numerous methods exist for producing such estimations, most boil down to solving a synchronous discrete-time fusion problem. However, the asynchronous nature of event cameras and their unique fusion mechanism with inertial sensors remain underexplored. In this article, we introduce a monocular event-inertial odometry method called asynchronous event-inertial odometry (AsynEIO), designed to fuse asynchronous event and inertial data within a unified Gaussian process (GP) regression framework. Our approach incorporates an event-driven front-end that tracks feature trajectories directly from raw event streams at a high temporal resolution. These tracked feature trajectories, along with various inertial factors, are integrated into the same GP regression framework to enable asynchronous fusion. With deriving analytical residual Jacobians and noise models, our method constructs a factor graph that is iteratively optimized and pruned using a sliding-window optimizer. Comparative assessments highlight the performance of different inertial fusion strategies, suggesting optimal choices for varying conditions. Experimental results on both public datasets and our own event-inertial sequences indicate that AsynEIO outperforms existing methods, especially in high-speed and low-illumination scenarios.

This paper researches a novel global terminal sliding mode control(GTSMC) on a tethered satellites system(TSS) under outer disturbances, and the effect of PI/PD compensation in restraining chattering on sliding surface is appended. By taking advantage of the finite-time convergence of traditional terminal sliding surface, the sliding surface with global and terminal sliding motion is proposed, and the convergent time by GTSMC is qualitatively evaluated by the sliding surface. Then the integral/derivative function of the low-pass filtered switching control is appended in GTSMC. By virtue of the accuracy of integral and the damping of derivative, respectively, the persisting on sliding surface is eliminated, such that the chattering effect of the controlled system on the surface is restrained consequently. Finally, simulations of the proposed control on TSS is shown to validate the theoretical analyses.

Recent works have combined monocular event camera and inertial measurement unit to estimate the SE(3) trajectory. However, the asynchronicity of event cameras brings a great challenge to conventional fusion algorithms. In this paper, we present an asynchronous event-inertial odometry under a unified Gaussian Process (GP) regression framework to naturally fuse asynchronous data associations and inertial measurements. A GP latent variable model is leveraged to build data-driven motion prior and acquire the analytical integration capacity. Then, asynchronous event-based feature associations and integral pseudo measurements are tightly coupled using the same GP framework. Subsequently, this fusion estimation problem is solved by underlying factor graph in a sliding-window manner. With consideration of sparsity, those historical states are marginalized orderly. A twin system is also designed for comparison, where the traditional inertial preintegration scheme is embedded in the GP-based framework to replace the GP latent variable model. Evaluations on public event-inertial datasets demonstrate the validity of both systems. Comparison experiments show competitive precision compared to the state-of-the-art synchronous scheme.

This study investigates the formation planning problem of tethered multirotor unmanned aerial vehicle (UAV) cooperative transportation with unknown payload and cable length. Normally, the transportation formation and trajectory are given in advance or designed based on the coupled system model. It is challenging to dynamically generate flexible formations in response to changing environments when the payload and cable length are unknown. This paper proposes an online formation planning method for multirotor UAVs. First, by analyzing the tension on cables, we propose some formation criteria and further construct a corresponding performance function of optimization. Then, desired trajectories/formations that can reduce the cost functions are generated by using the admittance model. Next, an estimation-based formation tracking control is designed, which ensures that multirotor UAVs follow the desired trajectories/formations. Finally, numerical simulations and experiments are conducted to demonstrate the effectiveness of the proposed method.Note to Practitioners—This paper is motivated by the formation planning problem of tethered multirotor UAV cooperative transportation. In industry and production applications, a team of multirotor UAVs has a larger load capacity than a single one. Nevertheless, the formation planning of the tethered cooperative transportation is challenging, especially when the payload and cable length are unknown. Rather than give a predefined formation or trajectory, this paper suggests an online formation planning method for multirotor UAVs in case of unknown payload and cable length. The method is implemented through the following three parts: 1) By analyzing the tension on cables, we propose some formation criteria and further construct a corresponding performance function of optimization. 2) By using the admittance model, we generate desired trajectories/formations that can minimize the proposed cost function. 3) By estimating cable tension, we design formation tracking control laws for multirotor UAVs to follow the desired trajectories/formations. The proposed formation planning method does not rely on the knowledge of the payload and length of cables, which makes it can be easily applied to extensive industry, production, and military practice. Finally, numerical simulations and experiments are conducted to demonstrate the feasibility of the proposed method.

In this article, the event-triggered fixed-time tracking control is investigated for uncertain strict-feedback nonlinear systems involving state constraints. By employing the universal transformed function (UTF) and coordinate transformation techniques into backstepping design procedure, the proposed control scheme ensures that all states are constrained within the time-varying asymmetric boundaries, and meanwhile, the undesired feasibility condition existing in other constrained controllers can be removed elegantly. Different from the existing static event-triggered mechanism, a dynamic event-triggered mechanism (DETM) is devised via constructing a novel dynamic function, so that the communication burden from the controller to actuator is further alleviated. Furthermore, with the aid of adaptive neural network (NN) technique and generalized first-order filter, together with Lyapunov theory, it is proved that the states of closed-loop system converge to small regions around zero with fixed-time convergence rate. The simulation results confirm the benefits of developed scheme.

The tethered formation system has been widely studied due to its extensive use in aerospace engineering, such as Earth observation, orbital location, and deep space exploration. The deployment of such a multitethered system is a problem because of the oscillations and complex formation maintenance caused by the space tether's elasticity and flexibility. In this article, a triangle tethered formation system is modeled, and an exact stable condition for the system's maintaining is carefully analyzed, which is given as the desired trajectories; then, a new control scheme is designed for its spinning deployment and stable maintenance. In the proposed scheme, a novel second-order sliding mode controller is given with a designed nonsingular sliding-variable. Based on the theoretical proof, the addressed sliding variable from the arbitrary initial condition can converge to the manifold in finite time, and then sliding to the equilibrium in finite time as well. The simulation results show that compared with classic second sliding-mode control, the proposed scheme can speed up the convergence of the states and sliding variables.

This article addresses the stabilization control issue for the postcapture tethered system by tethered space robot (TSR). Due to the physical characteristics of space tether by nature, there exists no control inputs on the in-plane/out-of-plane channels of the tether; therefore, the postcapture tethered system is a typical multiinput and multioutput underactuated system. In this article, we propose an energy-based controller for the underactuated system subject to input saturation and the nonnegativity constraint of the tether tension. First, we give the dynamic model of the postcapture tethered system, with consideration of the three attitude angles of the postcapture combination, the in-plane/out-of-plane angles, and the tether length. Second, we list the analysis process of the system’s equilibrium points. Third, we give the detailed controller design process, and verify the stability of the system by invoking the Lyapunov techniques and the extended Barbalat’s lemma. Finally, numerical simulations and comparison results with the hierarchical sliding mode controller are conducted to validate the performance improvement of the developed control strategy.

The use of maneuverable tethered space net robot (TSNR) is a promising solution for active space debris capture and removal due to its large envelop and easy capture method. However, the flexibility and elasticity of the underactuated net present a new challenge to the control scheme. In this article, a fuzzy-based sliding-mode control is proposed and applied to the TSNR. The main contribution is that an adaptive super-twisting sliding-mode control (ASTSMC) is investigated with a novel adaption law based on a fuzzy estimator, eliminating the need for a derivative of uncertainty. The key advantage of the proposed scheme is that the passive adaption law of traditional ASTSMC is improved to an active law, and complex oscillations can be directly estimated and suppressed. The dynamics equations of the TSNR are first derived, and the control problem of the system is specified. For the unmeasurable and boundary-unknown uncertainties, the adaptive fuzzy logic scheme is employed to approximate the complex uncertainties. Stability analysis and approximation convergence of the proposed control scheme are then verified via Lyapunov stability analysis. Finally, numerical simulations on the TSNR are provided to confirm the effectiveness and robustness of the proposed scheme.

This article focuses on the topic of fixed-time consensus for second-order multiagent systems (MASs) with disturbances. Based on an integration of the nominal control part and discontinuous integral sliding control part or continuous super-twisting-like control part, some control protocols are presented to achieve consensus tracking in the fixed time. A distributed integral sliding mode (ISM) and a fixed-time convergent command filter are introduced. The performance of nominal dynamics is dominated by the nominal control part which ensures the fixed-time convergence in the ISM surface. The discontinuous sliding mode or continuous super-twisting-like part related to the ISM is utilized to compensate disturbances within fixed convergence time. In this article, we have designed the continuous fixed-time consensus tracking controllers which can eliminate the chattering phenomenon and simultaneously guarantee the convergence precision. Independent of any initial state values, the restrictive bound of the convergence time is estimated. Some fair comparisons are performed to demonstrate the merits of the proposed strategies.

UAVs face many challenges in autonomous obstacle avoidance in large outdoor scenarios, specifically the long communication distance from ground stations. The computing power of onboard computers is limited, and the unknown obstacles cannot be accurately detected. In this paper, an autonomous obstacle avoidance scheme based on the fusion of millimeter wave radar and monocular camera is proposed. The visual detection is designed to detect unknown obstacles which is more robust than traditional algorithms. Then extended Kalman filter (EKF) data fusion is used to build exact real 3D coordinates of the obstacles. Finally, an efficient path planning algorithm is used to obtain the path to avoid obstacles. Based on the theoretical design, an experimental platform is built to verify the UAV autonomous obstacle avoidance scheme proposed in this paper. The experiment results show the proposed scheme cannot only detect different kinds of unknown obstacles, but can also take up very little computing resources to run on an onboard computer. The outdoor flight experiment shows the feasibility of the proposed scheme.

The postcapture of a partially failed spacecraft by space manipulators will bring a mutation in the dynamics of the combination. Both the inertia properties and the thruster configuration matrix will change significantly. The unknown dynamics of the partially failed spacecraft also cause a tremendous technical challenge for attitude takeover control. Accordingly, this paper describes a novel reconfigurable control system for postcapture attitude takeover of a partially failed spacecraft with parametric uncertainties, whose fuel has been exhausted or whose actuators have partial malfunctions. First, the reconfigurable control law is designed by command filtering adaptive back-stepping control to guarantee the system performance and global asymptotic stability considering inertia parametric uncertainties. Second, the thrusters are reconstituted, without changing the thruster physical configuration. Finally, the thrusters' forces can be redistributed by the dynamic control reallocation method based on constrained quadratic programing. Numerical simulations validate the feasibility of the proposed approach for postcapture attitude takeover control of a partially failed spacecraft with parametric uncertainties. Note to Practitioners-This paper presents a methodology for a partially failed spacecraft with parametric uncertainties. It focuses on solving the attitude takeover control of the partially failed spacecraft perfectly, while considering the position and speed constraints of the actuator and avoiding the plume impact to the spacecraft. The proposed command filtering adaptive back-stepping control can be used for spacecraft's thruster reconfiguration considering the parametric uncertainties. Furthermore, the dynamic control reallocation method is particularly useful for future applications that include spacecraft with redundant actuators.

Since the Tethered Space Robot (TSR) has been a research focus as an application of the space tether, a wide range of problems arise in the different phases of the capture mission. In this paper, we propose a new control scheme for the retrieval of passive space debris after captured by a TSR. Under this control scheme, target can be retrieved rapidly, and both of oscillations of tether and target are converged well. First, we derive the equations of attitude motions for the compound system when the passive target satellite is captured by Tethered Space Robot, where the base satellite (chaser) and the space debris (target) are modeled as rigid bodies and the attachment points of the tether are offset from the centers of mass of the two bodies. Then based on the specifics of equations, we divide the retrieval into two phases, and set a threshold for the retrieval. In different phases, different priorities are presented, and different control schemes are given. Finally, the simulation results are shown to prove that the target satellite could be retrieved rapidly and smoothly in a small oscillation, and the oscillation of tether is totally converged at the end of retrieval.