Zheshuo Zhang

Reliable collision warning from 4D millimeter-wave radar is hindered by the evaluation of deterministic distance–speed boundaries on uncertain track states. Directly learning the alarm decision may improve flexibility, but obscures safety-critical logic. To reconcile robustness with interpretability, this paper develops a hybrid uncertainty-aware warning framework in which a lightweight multilayer perceptron (MLP) estimates deviations from nominal radar tracks, whereas the final binary decision remains governed by explicit geometric and kinematic conditions. Validation-residual calibration transforms the instantaneous estimates into empirical upper margins, and a projection-bounded leakage recursion preserves recent uncertainty during brief observation gaps. The margins act only on the time-based entry branch; the hard-distance override, motion-direction gate, confirmation logic, and release rule remain deterministic. The framework is evaluated using 36 fixed-radar, single-reflector physical recordings and 216 trial-isolated controlled-degradation realizations derived from them. The calibrated margins attain 95.24% range-error coverage and 94.04% speed-error coverage relative to the nominal tracks. Among 198 realizations with a reference entry, robust-margin methods reduce late or missed warnings from 15 to 2. At this protected operating point, the calibrated MLP decreases alarm disagreement from 4.94% for a fixed margin to 3.90%, compared with 4.16% for a calibrated linear model. The results identify a condition-dependent margin-allocation benefit while retaining an auditable warning policy for the tested recordings.

This paper investigates the flight-control problem of a vector–rotor UAV (VR-UAV) for orchard crop-inspection tasks, where wind acts as the dominant external disturbance source. In such tasks, the UAV is required to maintain position while adjusting its attitude for flexible sensor pointing. For a conventional quadrotor UAV (QUAV), however, position and attitude are strongly coupled because the thrust directions are fixed relative to the fuselage, which limits its ability to perform stable hovering and directional sensing simultaneously. Although gimbal-based solutions can provide sensing-direction adjustment, they may become less suitable for wind-affected low-altitude inspection tasks involving large, elongated, or multi-sensor payloads, due to the added mass, inertia, structural compliance, and vibration sensitivity introduced by the additional mechanism. To address these limitations, this paper proposes a compact VR-UAV platform together with an adaptive robust constraint-following control (ARCFC) method. By incorporating tilting motors for thrust-vector adjustment, the proposed VR-UAV enables decoupled regulation of position and attitude, thereby improving fixed-point hovering capability and flexible sensor pointing. From the control perspective, the thrust-vectoring mechanism introduces strongly nonlinear coupled dynamics, while wind-induced disturbances and modeling uncertainties further complicate the control problem. To address these challenges, a constraint-following control framework is developed to handle the nonlinear dynamics, and an adaptive robust compensation mechanism is introduced to estimate the uncertainty bound online and compensate for unknown but bounded disturbances. The closed-loop stability and robustness of the proposed method are rigorously established by theoretical analysis. Comparative simulation results demonstrate that, relative to a conventional QUAV, the proposed VR-UAV with ARCFC achieves superior flight stability, stronger wind-disturbance rejection, and better trajectory-tracking performance in wind-affected orchard inspection scenarios.

In high-rise firefighting scenarios, unmanned aerial vehicles (UAVs) equipped with water-spraying systems are subjected to high-intensity and rapidly time-varying reaction forces induced by high-speed water jets. These forces introduce mismatched uncertainties with unknown bounds and make stable flight control particularly challenging. To address this problem, this paper proposes an adaptive robust constraint-following control (ARCFC) strategy for vector–rotor UAVs (VRUAVs). The controller is developed directly for the strongly nonlinear dynamics of the VRUAV without resorting to model linearization. Within a constraint-following-based nonlinear regulation framework, water-jet effects are explicitly modeled as rapidly time-varying uncertainties with unknown bounds, and an adaptive law is introduced to estimate conservative uncertainty bounds online for robust compensation. Lyapunov-based analysis is conducted to establish the uniform boundedness and uniform ultimate boundedness of the closed-loop system, and simulation results are presented to verify the effectiveness of the proposed approach. Compared with representative conventional control methods, the proposed ARCFC strategy provides improved disturbance-rejection capability and enhanced flight stability under demanding firefighting conditions.

This article proposes an adaptive robust diffeomorphism-based constraint-following control (ARDCFC) method for an unmanned aerial vehicle (UAV) swarm system to achieve fast and precise formation (FPF). FPF means that the UAV swarm system has the ability to converge to the desired formation within a short time while maintaining high accuracy. This control problem imposes requirements on both transient and steady-state performance, as well as collision avoidance under time-varying (possibly fast and irregular) uncertainties with unknown bounds. The FPF requirements and collision avoidance are formulated as inequality constraints, whereas the ideal swarm performance is formulated as a series of equality constraints. A diffeomorphism approach is adopted to transform all the above inequalities and equality constraints into unified equality constraints. The uncertainties are accounted for by adaptively estimating and utilizing a conservative bound. ARDCFC regards the equality constraints as control targets, thereby formulating FPF control with simultaneous collision avoidance as a constraint-following problem. The robustness and effectiveness of the proposed formation control method are demonstrated through rigorous proofs and simulations. To the best of our knowledge, this is probably the first study to simultaneously guarantee FPF and collision avoidance for an uncertain UAV swarm system.

This paper addresses the challenge of excessive control effort in uncertainty compensation to achieve agile and accurate formation control in unmanned aerial vehicle swarm systems (UAVSS). Specifically, we optimize control parameters based on uncertainty information characterized by fuzzy set theory. We propose an adaptive robust agile and accurate formation control (ARAAFC) approach with optimality, developed in two steps. First, agile and accurate formation constraints under uncertainty are enforced through an adaptive robust control design with a tunable parameter structure, ensuring deterministic performance for UAVSS, in terms of uniform boundedness and ultimate uniform boundedness. Second, fuzzy optimality of tunable control parameters is performed by solving a two-players Nash game, which optimizes control effort while ensuring agile and accurate formation performance. The existence of the Nash equilibrium is theoretically proven, and the procedure for obtaining the Nash equilibrium is provided. The robustness and effectiveness of the proposed ARAAFC are validated by rigorous proofs and simulations.

The integration of lift airfoils (LA) into high-speed trains (HSTs) offers significant advantages, such as improved transport efficiency, reduced wheel-rail forces, and lower energy consumption. However, these benefits are accompanied by challenges to ride comfort and operational safety due to the varying aerodynamic loads experienced at high speeds. Existing passive suspension systems are insufficient in addressing these issues effectively. Therefore, it is crucial to develop an active suspension system that can mitigate the impact of fluctuating aerodynamic forces on LA-HSTs. An adaptive robust displacement inequality constraint-following control (AICFC) active suspension system is proposed, leveraging constraint-following control theory to enhance HST stability under aerodynamic load disturbances. Additionally, a linear eddy current brake (ECB) is integrated with the AICFC active suspension to further improve operational safety in complex environments. Simulation results demonstrate that the proposed AICFC-based active suspension system significantly reduces car-body vibrations and displacements, thereby enhancing ride comfort. Moreover, the combination of the active suspension and ECB enhances operational safety beyond what the AICFC active suspension alone can achieve. Finally, this study incorporates the delay effects of both the actuator and ECB to evaluate the performance of the active suspension system. The findings indicate that these delays can degrade the enhancement of dynamic performance in HSTs-LA achieved by the active suspension. However, the active suspension retains a significant and unparalleled advantage over passive suspension systems.

A major challenge in robust control design of robotic systems is finding a comprehensive uncertainty bound (CUB) with low conservativeness for uncertainty compensation. This study proposes a two-phase learning approach to learn the CUB for robust control of robotic systems, considering uncertainties with unknown bounds. The goal is to drive the system to follow a class of servo constraints that may be nonholonomic, i.e., constraint-following control (CFC) design. The first phase trains a deep neural network (DNN) to approximate the ensemble system uncertainty using offline supervised learning. The second phase constructs an adaptive law to learn the CUB online, covering the offline learning error. To our knowledge, this is the first CFC combining DNN with adaptive law to learn a less conservative CUB to save control effort and to eliminate complex manual derivations. The effectiveness and merits of the proposed control are endorsed by theoretical proofs, simulations, and experiments on a quadrotor unmanned aerial vehicle.

A robust constraint-following control (CFC) scheme is proposed for active suspension system to improve ride comfort of high-speed trains (HSTs) by employing nonlinear stiffness of bio-inspired structure (BIS). The control problem of driving active suspension system to emulate nonlinear dynamics of BIS is formulated as servo constraint-following. The disturbance observer is theoretically integrated into CFC framework to address critical practical issues, including parameter uncertainty, suspension nonlinearity and aerodynamic disturbance. The complex uncertainty decomposition and uncertainty bound function design can be avoided in the control design compared with past adaptive CFC. Lyapunov stability theory is applied to prove the uniform ultimate boundedness of proposed robust CFC, enabling the constraint-following error to converge to a very small range. The simulation results demonstrate that the active suspension system could exactly follow the servo constraints of reference BIS with high constraint-following accuracy regardless of parameter uncertainty and external disturbance. And the active suspension system can effectively suppress the vibration and improve the ride comfort steadily with random structure parameters and strong aerodynamic loads. This study also provides a new insight and an alternative approach for designing robust CFC in the active suspension of HSTs.