The evolution of network densification, from terrestrial Ultra-Dense Networks (UDNs) to aerial deployments of Unmanned Aerial Vehicles (UAVs), intensifies interference from line-of-sight links while imposing stringent energy constraints, making resource optimization critical. The Joint Power and Admission Control (JPAC) problem is fundamental to achieving efficient network services, but traditional algorithms face performance limitations in the high-dimensional sparse scenarios created by these large-scale networks. To address this, this paper proposes a novel algorithm, Adaptive Sieving with Gradual Removals (ASGR), inspired by the Adaptive Sieving (AS) method. While AS focuses solely on admitting links, ASGR enhances this process by incorporating a gradual removal mechanism. This key difference allows ASGR to proactively prune unpromising links, leading to higher-quality feasible solutions. Both theoretical analysis, which establishes a complexity ofO(Ulog3U) in sparse scenarios, and experimental results confirm that ASGR achieves a significant computational speedup over mainstream algorithms. The proposed algorithm attains this acceleration while achieving comparable results for total transmission power and the number of admitted links, and its comprehensive performance remains competitive in general scenarios.

Wheeled self-reconfigurable robots (WSRRs), a new type of multi-robot system with flexible configurations and task adaptability, have an extensive application prospects in unstructured mission environments. In this paper, based on the nonholonomic constraints and Lagrange method, the combinatorial modal kinematics and dynamics of WSRRs with arbitrary reconfiguration scale are established. At the kinematic level, based on the nonholonomic constraints, a smooth obstacle avoidance strategy based on the safety geofences is designed to ensure safety. At the dynamic level, an adaptive fault-tolerant mechanism is introduced to ensure reasonable torque distribution and avoid tracking performance degradation. Meanwhile, an improved extended state observer (IESO) is elaborated, through which the high-frequency ocsillation from measurement noises and peaking phenomenon from initial observer errors can be suppressed, and the robust velocity tracking control under unknown lumped disturbances is realized. Finally, a real-world WSRRs experiment is constructed to verify the proposed method's fault tolerance, robustness, and safety comparatively.

This paper proposes a precision autonomous landing system for unmanned aerial vehicles (UAVs) targeting high-speed moving platforms. By integrating gimbal-based precise positioning, smooth trajectory generation, and dynamically robust control, the system addresses key challenges in high-speed landing scenarios, such as significant visual localization deviations and difficulties in dynamic trajectory planning and control. The study introduces the Comprehensive Coordinate System (CCS-3AG) to eliminate dynamic optical-axis misalignment errors in the gimbal, thereby enhancing the gimbal’s ranging accuracy and control precision. We combine an enhanced single-stage minimum control (MINCO) trajectory framework (L-MINCO) with a bidirectional command update strategy to achieve fast and accurate trajectory planning that accounts for dynamic delays, and designs an Incremental Nonlinear Dynamic Inversion (INDI) controller for high-dynamic command tracking. Simulation and real-flight experiments demonstrate that, at target speeds between 0 and 7.7 m/s, the system attains an average landing precision of 0.108 meters, with a success rate of 97.78% across 90 actual landing tests, outperforming existing landing methods. This work provides a highly robust solution for UAV logistics delivery and emergency landing scenarios.

Unmanned Aerial Vehicles (UAVs) play an important role in various applications, where precise trajectory tracking is crucial. However, conventional control algorithms for trajectory tracking often exhibit limited performance due to the underactuated, nonlinear, and highly coupled dynamics of quadrotor systems. To address these challenges, we propose HBO-PID, a novel control algorithm that integrates the Heteroscedastic Bayesian Optimization (HBO) framework with the classical PID controller to achieve accurate and robust trajectory tracking. By explicitly modeling input-dependent noise variance, the proposed method can better adapt to dynamic and complex environments, and therefore improve the accuracy and robustness of trajectory tracking. To accelerate the convergence of optimization, we adopt a two-stage optimization strategy that allow us to more efficiently find the optimal controller parameters. Through experiments in both simulation and real-world scenarios, we demonstrate that the proposed method significantly outperforms state-of-the-art (SOTA) methods. Compared to SOTA methods, it improves the position accuracy by 24.7% to 42.9%, and the angular accuracy by 40.9% to 78.4%.

This paper presents a universal and adaptive control framework for reconfigurable vehicles subject to composite motion disturbances, incorporating a collaborative fault-tolerant mechanism. A model-based cascaded control architecture is developed based on the vehicle’s kinematic and dynamic models. To ensure safety, an improved adaptive geofencing strategy is proposed which integrates capability constraints with barrier functions in the kinematic loop. For dynamic feedback, an adaptive gain-filtered extended state observer enables accurate disturbance estimation from noisy outputs and improves robustness via feedforward compensation. Meanwhile, a collaborative fault-tolerant mechanism further allocates control inputs to mitigate actuator faults. Finally, experimental results validate the proposed method’s effectiveness under complex interference scenarios.

Nonholonomic vehicles in distributed networks are prone to triggering nested velocity and acceleration saturation during reactive safety formations, exacerbating oscillations. This paper proposes a hybrid secure distributed collaborative frame-work, integrating compound adaptive anti-windup strategies with vehicle kinematics and safe geofences to achieve smooth and effective obstacle and collision avoidance while suppressing saturation-induced oscillations. The vehicle’s safe behavior for bypassing obstacles is formed via acceleration envelopes from safe geofences and input saturation, which generate constraint velocity commands. Additionally, a low-trigger and power-adjustable enhanced artificial potential field is integrated into the safety coordination to fine-tune vehicle maneuvers at extremely close distances to hazardous targets, ensuring high reliability. Safe acceleration envelopes and nested kinematic saturation are utilized to design a compound adaptive auxiliary dynamic system, smoothing oscillations induced by dual command constraints during formation. A distributed formation controller is further designed to enable multitasking collaboration in formations. The overall stability is mathematically analyzed, and the method’s superior smoothness and safety in task coordination are validated through simulations and experiments with vehicle clusters. Note to Practitioners—In response to the severe trajectory oscillations caused by saturation triggered by existing reactive avoidance approaches, this paper proposes a novel hybrid safety collaborative control based on the nonholonomic vehicle kinematics that markedly enhances the smoothness and safeness of formations in obstacle environments. The integration of safety acceleration envelopes, as well as low-trigger and adjustable artificial potential functions, markedly mitigates oscillations from reaction saturation compared with the solitary traditional artificial potential functions, as evidenced by simulations and experiments that demonstrate reduced oscillation amplitudes and shorter recovery times when evading hazardous targets using the proposed method. In addition, existing velocity/acceleration nested windups in actual applications are concurrently considered for the first time, and the corresponding compound adaptive anti-windup method is employed to smooth oscillations caused by control saturation. The security and smoothing strategies outlined allow for collaborative operations in more complicated obstacle environments and enable the deployment of larger vehicle clusters in confined spaces, significantly enhancing multi-vehicle collaboration’s economic viability and efficiency. Furthermore, the safety collaborative control framework designed for kinematics is conveniently structured for engineers as a standalone module, which is easily transferrable to commercial robotic products. The composite approach to safeness and smoothness can also be applied in other unmanned and manned collaborative scenarios.

The existence of compound velocity and acceleration windups in clusters of nonholonomic mobile robots can seriously constrain the smoothness and stability of the overall motion. This article proposes a leader–follower-based distributed formation control framework for smooth and robust clustering of multiple nonholonomic mobile robots under compound windups of velocity and acceleration and unknown perturbations. The decoupled position and orientation kinematics and substrate wheel velocity dynamics are modularly devised via feedback linearization techniques to enable upper-level cooperative error regulation and lower-level wheel velocity trajectory tracking. The auxiliary dynamic system based on the velocity envelope generated by compound motion windups and the WMR kinematic is integrated into the collaborative error, adaptively mitigating the detrimental windup effects. The adaptive saturated extended state observer is utilized to flatly estimate unknown perturbations in the wheel velocity dynamics with enhanced robustness. Finally, the overall stability analyses are done based on Lyapunov’s theorem, and contrastive simulations and plentiful experiments are conducted to attest to the validity and availability.

Aggressive maneuvering is crucial for aerial vehicles to execute adversarial and penetration missions. However, this challenges the accurate tracking control of drones due to uncertainties induced by high-speed flight. Therefore, firstly, a highly dynamic tracking control framework is proposed to actualize the accurate tracking of aggressive trajectories with velocities up to 15 m/s (i.e., 54 km/h) and acceleration of 2 g. Secondly, in order to mitigate the impact of conjoint effects on uncertainty estimation during aggressive flights and to ensure that uncertainty is smoothly compensated, a novel adaptive nonlinear extended state observer (ANESO) with noise suppression and peak attenuation capabilities is designed. Finally, extensive comparative simulation and real-world practical experimental results certify the superiority of the proposed control strategy in tracking aggressive trajectories.

Mobile self-reconfigurable robots (MSRRs), as a novel multi-robot system with flexible configurations and task adaptability, hold promising applications in unstructured task environments. However, existing autonomous docking strategies are primarily applied in laboratory settings and face numerous challenges and limitations in actual applications, including differences in sensor characteristics, safety threats, and saturation constraints. To address these issues, this paper proposes a segmented secure docking control framework based on global localization and local perception to achieve stable and reliable reconfiguration of MSRRs in practical applications. Specific contributions include the implementation of a dual-layer constraint framework for safeness of units in the long-distance phase against velocity and acceleration nested windups, and the integration of active line-of-sight (LOS) correction and adaptive windup driving mobile units to achieve precise and rapid locking of docked positions within the LOS in the close-range phase. Finally, the validity of the proposed method is verified via physical experiments, offering an innovative approach to deploying MSRRs in complex scenarios.

Uncertainty estimation in real-world scenarios is challenged by complexities arising from peaking phenomena and measurement noises. This article introduces a novel scheme for practical uncertainty estimation to mitigate peaking dynamics and enhance overall dynamic behavior. A fusion estimation framework for lumped uncertainties using multiple extended state observers (ESOs) is constructed, and the low-frequency adaptive parameter learning technique is employed to approximate the optimal fusion. The adaptive fusion estimation not only attenuates transient peaks in uncertainty estimation but also attains fast convergence and high accuracy under the high-gain scheduling of ESOs. Furthermore, the robustness of uncertainty estimation against measurement noises is enhanced by cascading filters in the proposed adaptive fusion framework for multiple ESOs. Extensive theoretical analyses are executed to verify practical applicability in peak and noise rejection. Finally, simulations and experiments on the wheel velocity system of a mobile robot are conducted to test the validity and feasibility.

The composite velocity and acceleration constraints suffered by nonholonomic mobile robots during motion severely hamper the stability and smoothness of the cluster. This paper presents an applicable control framework for the robust and smooth implementation of distributed formations based on nonholonomic mobile robot clusters against velocity and acceleration saturation. The decoupled feedback linearization control of cascaded position, heading and wheel velocity is used to achieve distributed time-varying formations with modular and scalable stabilization of the triple-level errors. The adaptive auxiliary dynamics are incorporated into the distributed coincident errors to reduce velocity and acceleration saturation, which improves the overall transient results and stability range. The adaptive saturation extended observer in the substrate dynamics is conceived to simultaneously estimate the lumped disturbances and suppress undesired peaks, smoothly enhancing the robustness of wheel velocity control. Ultimately, comparative simulations and extensive experiments for mobile robots are performed to test validity and practicality.

Model structure representation and fast estimation of perturbations are two key research aspects in adaptive control. This work proposes a composite local learning adaptive control framework, which possesses fast and flexible approximation to system uncertainties and meanwhile smoothens control inputs. Local learning, which is a nonparametric regression approach, is able to automatically adjust the structure of approximator based on data distribution from the local region, but it is sensitive to the outliers and measurement noises. To tackle this problem, the regression filter technique is employed to attenuate the adverse effect of noises by smoothing the output response and state features. In addition, the stable integral adaptation is integrated into local learning framework to further enhance the system robustness and smoothness of the estimation. Through the online elimination of uncertainties, the nominal control performance is recovered when the plant encounters violent perturbations. Stability analysis and numerical simulations are performed to demonstrate the effectiveness and benefits of the proposed control method. The proposed approach exhibits a promising performance in terms of rapid perturbation elimination and accurate tracking control.

This work investigates finite-time tracking control for a multi-input–multi-output plant with multisource uncertainties. A multivariable finite-time prescribed performance control scheme is proposed, where the norm of the tracking error vector is constrained with a prescribed bound. Due to the positiveness of the norm of the error vector, a novel error transformation is given to transform the “constrained” problem into an equivalent “unconstrained” problem. Meanwhile, the composite neural adaptive law is established to attenuate the effect of multisource uncertainties. The parametric perturbations are counteracted by neural adaptive terms. Time-varying uncertain control gains and external disturbances in the multivariable systems are compensated by adaptively estimating their bounds and applying the Lyapunov control design. To tackle the practical tracking problem, the aforementioned method is integrated into a dynamic-surface-based backstepping framework. Additionally, the practical quaternion-based attitude tracking problem is addressed, in which a quaternion-based form of error-norm constraint is constructed to express the generality and scalability of our proposed.

In this article, finite-time control is investigated for multiple time-delayed large-scale nonlinear systems against state and input constraints. There are multiple time-varying delays, intermittent actuator faults, and intermittent sensor faults in this model. Takagi–Sugeno fuzzy model is used to describe the nonlinear system. There are few tries to studying finite-time control for large-scale nonlinear systems. First, a dynamic output feedback controller is designed in this article to make the system finite-time bounded. Then, an augmented closed-loop model is constructed. Sufficient conditions of finite-time control are given by the fuzzy Lyapunov function. Finally, the feasibility of the approach is verified by two simulation examples.