Search Results (72)

Hub motor-driven vehicles can control vehicle attitude by regulating the speed and torque of four wheels, supporting safe and stable lane changing and obstacle avoidance. However, under high-speed scenarios, these vehicles often suffer from poor stability, limited comfort, and inadequate trajectory tracking accuracy during lane changing and obstacle avoidance operations. To address these challenges, this study proposes a lane changing obstacle avoidance control strategy for hub motor-driven vehicles based on collision risk prediction. A fuzzy controller featuring a variable weight objective function is designed to balance lane changing efficiency and ride comfort, thereby generating an optimal lane changing and obstacle avoidance trajectory. Furthermore, a linear time-varying model predictive controller (LTV-MPC) is developed, which adaptively adjusts both the weighting coefficient of lateral displacement error in the objective function and the prediction horizon of the controller, enabling dynamic tuning of vehicle trajectory tracking accuracy. A dSPACE hardware-in-the-loop (HIL) platform was established to conduct simulations under typical obstacle avoidance scenarios. The simulation results show that under two easily destabilized conditions—high-adhesion, high-speed, large-curvature, and low-adhesion, medium-speed, large-curvature maneuvers—the proposed optimized control strategy limits the maximum lateral trajectory tracking error to 0.116 m and 0.143 m, representing reductions of 58.6% and 79.6% compared with the baseline control strategy. These results demonstrate that the proposed method enhances trajectory tracking accuracy and stability during lane changing and obstacle avoidance maneuvers. Full article

Feeding unmanned aerial vehicles (UAVs) in aquaculture face critical challenges due to time-varying mass, strong coupling, and environmental disturbances, which hinder the effectiveness of conventional control strategies. This paper proposes a robust dual-loop model predictive control (MPC) framework optimized by an adaptive niche radius genetic algorithm (ANRGA). The outer loop employs MPC for position regulation using virtual acceleration inputs, while the inner loop applies MPC for attitude stabilization with dynamic inertia adaptation. To overcome the limitations of manual weight tuning, ANRGA adaptively optimizes the weighting factors, preventing premature convergence and improving global search capability. System stability is theoretically ensured through Lyapunov analysis and the small-gain theorem, even under variable-mass dynamics. MATLAB simulations under representative trajectories—including spiral, figure-eight, and feeding cruise paths—demonstrate that the proposed ANRGA-MPC-MPC achieves position errors below 0.5 m, enhances response speed by approximately 58% compared with conventional MPC, and outperforms benchmark controllers in terms of accuracy, robustness, and convergence. These results confirm the feasibility of the proposed method for precise and energy-efficient UAV feeding operations, providing a promising control strategy for intelligent aquaculture applications. Full article

Stable attitude control of unmanned or autonomous operations of vehicles moving in three spatial dimensions is essential for safe and reliable operations. Rigid body attitude control is inherently a nonlinear control problem, as the Lie group of rigid body rotations is a compact manifold and not a linear (vector) space. Prior research has shown that the largest possible domain of convergence is provided by smooth attitude feedback control laws are obtained using a Morse function on $\mathrm{SO}\left(3\right)$ as a measure of the attitude stabilization or tracking error. A polar Morse function on $\mathrm{SO}\left(3\right)$ has four critical points, which precludes the possibility of global convergence of the attitude state. When used as part of a Lyapunov function on the state space (the tangent bundle $\mathrm{TSO}\left(3\right)$) of attitude and angular velocity, it gives a globally continuous state-dependent feedback control scheme with the minimum of the Morse function as the almost globally asymptotically stable (AGAS) attitude state. In this work, we explore the use of explicitly time-varying gains for Morse functions for rigid body attitude control. This strategy leads to discrete switching of the indices of the three non-minimum critical points that correspond to the unstable equilibria of the feedback system. The resulting time-varying feedback controller is proved to be AGAS, with the additional desirable property that the time-varying gains destabilize the (locally) stable manifolds of these unstable equilibria. Numerical simulations of the feedback system with appropriate time-varying gains show that a trajectory starting from an initial state close to the stable manifold of an unstable equilibrium, converges to the desired stable equilibrium faster than the corresponding feedback system with constant gains. Full article

Rapid and precise intervention in disaster and medical-aid scenarios demands aerial platforms that can both survey and physically interact with their environment. This study presents the design, fabrication, modeling, and experimental validation of a one-piece, 3D-printed quadcopter with an integrated six-degree-of-freedom aerial manipulator robotic arm tailored for emergency response. First, we introduce an ‘X’-configured multi-rotor frame printed in PLA+ and optimized via variable infill densities and lattice cutouts to achieve a high strength-to-weight ratio and monolithic structural integrity. The robotic arm, driven by high-torque servos and controlled through an Arduino-Pixhawk interface, enables precise grasping and release of payloads up to 500 g. Next, we derive a comprehensive nonlinear dynamic model and implement an Extended Kalman Filter-based sensor-fusion scheme that merges Inertial Measurement Unit, barometer, magnetometer, and Global Positioning System data to ensure robust state estimation under real-world disturbances. Control algorithms, including PID loops for attitude control and admittance control for compliant arm interaction, were tuned through hardware-in-the-loop simulations. Finally, we conducted a battery of outdoor flight tests across spatially distributed way-points at varying altitudes and times of day, followed by a proof-of-concept medical-kit delivery. The system consistently maintained position accuracy within 0.2 m, achieved stable flight for 15 min under 5 m/s wind gusts, and executed payload pick-and-place with a 98% success rate. Our results demonstrate that integrating a lightweight, monolithic frame with advanced sensor fusion and control enables reliable, mission-capable aerial manipulation. This platform offers a scalable blueprint for next-generation emergency drones, bridging the gap between remote sensing and direct physical intervention. Full article

Accurate prediction of the shield attitude is critical for controlling the excavation direction, ensuring construction safety, and advancing the sustainability of shield tunneling by reducing energy and environmental disturbance. Traditional prediction methods for the shield attitude have a certain lag and low prediction accuracy, and existing machine learning methods lack research on the varying importance of different parameters affecting the shield attitude, while also ignoring the global information characteristics of the data. To accurately predict the shield attitude and support sustainability-oriented operations, this study proposes a novel prediction model based on a project in Shenyang, China. The model utilizes a channel domain attention mechanism to learn the importance of various influencing parameters and extracts spatial features via a convolutional neural network. Additionally, it captures long-range dependency and local temporal features using a transformer augmented with a bidirectional long short-term memory network. Experimental results show that the proposed model achieves lower MAE and RMSE and higher R² compared with baseline and sub-models. Its generalization and reliability are further validated using data from another shield tunnel section. From a sustainability perspective, timely and high-fidelity predictions enable proactive steering that reduces unnecessary corrective actions and extreme operating states (e.g., thrust/torque spikes), which are associated with higher energy use, accelerated consumable wear, over-grouting, and potential surface disturbance. Finally, integrating the model’s predictions with onsite adjustment measures effectively mitigates alignment deviations, contributing to more energy-efficient, resource-conscious, and low-disturbance trajectory control. Full article

Accurate trajectory tracking of quadrotor UAVs remains challenging due to highly nonlinear dynamics, model uncertainties, and time-varying external disturbances, which make it difficult to achieve both precise position tracking and stable attitude regulation under control constraints. To tackle these coupled problems, this paper develops a hierarchical control framework in which the outer-loop particle swarm optimization (PSO)-compensated model predictive controller (PSMC) adaptively mitigates prediction errors and enhances robustness, while the inner-loop enhanced linear quadratic regulator (LQR), augmented with gain scheduling and control-rate relaxation, accelerates attitude convergence and ensures smooth control actions under varying flight conditions. A Lyapunov-based stability analysis is conducted to ensure closed-loop convergence. Simulation results on a helical reference trajectory show that, compared with the conventional MPC–LQR baseline, the proposed framework reduces the mean tracking errors by more than 13.2%, 17.1%, and 28% in the x-, y-, and z-directions under calm conditions, and by more than 34%, 26.2%, and 46.8% under wind disturbances. These results prove that the proposed hierarchical PSMC–LQR framework achieves superior trajectory tracking accuracy, strong robustness, and high practical implement ability for quadrotor control applications. Full article

The accurate acquisition and real-time calculation of the attitude angle of precision firefighting extinguishing projectiles are essential for ensuring stable flight and precise extinguishing agent release. However, measuring the roll attitude angle in such projectiles is challenging due to their highly dynamic nature and environmental disturbances such as fire smoke, high temperature, and electromagnetic interference. Traditional methods for measuring attitude angles rely on multi-sensor fusion schemes, which suffer from complex structure and high cost. This paper proposes a single-gyro attitude calculation method based on micro-electromechanical inertial measurement units (MIMUs). This method integrates Fourier transform time-frequency analysis with a second-order Infinite Impulse Response (IIR) bandpass filtering algorithm optimized by dynamic coefficients. Unlike conventional fixed-coefficient filters, the proposed algorithm adaptively updates filter parameters according to instantaneous roll angular velocity, thereby maintaining tracking capability under time-varying conditions. This theoretical contribution provides a general framework for adaptive frequency-tracking filtering, beyond the specific engineering case of firefighting projectiles. Through joint time-frequency domain processing, it achieves high-precision dynamic decoupling of the roll angle, eliminating the dependency on external sensors (e.g., radar/GPS) inherent in conventional systems. This approach drastically reduces system complexity and provides key technical support for low-cost and high-reliability firefighting projectile attitude control. The research contributes to enhancing the effectiveness of urban firefighting, forest fire suppression, and public safety emergency response. Full article

This study focuses on the cooperative formation control problem of six-degree-of-freedom (6-DOF) fixed-wing unmanned aerial vehicles (UAVs) under constraints of limited communication resources and strict time requirements. The core innovation of the proposed framework lies in the deep integration of a dynamic self-triggered communication mechanism (DSTCM) with a prescribed-time control strategy. Furthermore, a fuzzy control strategy is designed to effectively suppress system disturbances, enhancing the robustness of the formation. The designed DSTCM not only retains the adaptive triggering threshold characteristic of dynamic event-triggered communication, significantly reducing communication frequency, but also completely eliminates the need for continuous state monitoring required by traditional event-triggered mechanisms. As a result, both communication and onboard computational resources are effectively conserved. In parallel, a novel time-varying unilateral constrained performance function is introduced to construct a prescribed-time controller, which guarantees that the formation tracking error converges to a predefined residual set within a user-specified time. The convergence process is independent of initial conditions and strictly adheres to full-state constraints. A rigorous Lyapunov-based stability analysis demonstrates that all signals in the closed-loop UAV velocity and attitude system are semi-globally uniformly ultimately bounded (SGUUB). Furthermore, the proposed DSTCM ensures the existence of a strictly positive lower bound on the inter-event triggering intervals of the UAVs, thereby avoiding the occurrence of Zeno behavior. Numerical simulation results are provided to verify the effectiveness and superiority of the proposed control scheme. Full article

This study addresses robust trajectory tracking for quadrotor unmanned aerial vehicles (QUAVs) under partial state measurements and bounded external disturbances. To this end, a control framework is introduced that integrates backstepping sliding mode control (BSMC) with a super-twisting observer (STO). In this scheme, only position and attitude are directly measured while the STO reconstructs the linear and angular velocities in real time. The estimated states are then fed into the control law, enabling accurate trajectory tracking and robust performance without full-state feedback or explicit disturbance compensation. The approach is validated through three simulation scenarios: nominal full-state feedback, observer-based control without disturbances, and observer-based control under bounded time-varying perturbations. Quantitative metrics confirm consistent tracking accuracy and closed-loop stability across all scenarios. These results demonstrate the effectiveness of the integrated BSMC–STO framework for QUAV operations in sensor-limited and disturbance-prone environments. Full article

As an essential technology for precision airdrop missions, parafoil systems have gained widespread adoption in military and civilian applications due to their superior glide performance and maneuverability compared to conventional parachutes. Addressing the trajectory-tracking control challenges of the parafoil system under significant wind disturbances, characterized by wind uncertainty and system underactuation, this paper proposes a stochastic model predictive control (SMPC) framework based on Markov-based multi-scenario optimization. Traditional deterministic model predictive control (MPC) methods often exhibit excessive conservatism due to reliance on worst-case assumptions and fail to capture the time-varying nature of real-world wind fields. To address these limitations, a high-fidelity dynamic model is developed to accurately characterize aerodynamic coupling effects, overcoming the oversimplifications of conventional three-degree-of-freedom point-mass models. Leveraging Markov state transitions, multiple wind-disturbance scenarios are dynamically generated, effectively overcoming the limitations of independent and identically distributed hypotheses in modeling realistic wind variations. A probabilistic constraint-reconstruction strategy combined with a rolling time-domain covariance update mechanism mitigates uncertainties and enables cooperative optimization of inner-loop attitude stabilization and outer-loop trajectory tracking. The simulation results demonstrate that the SMPC framework achieves superior comprehensive performance compared to deterministic MPC, evidenced by significant reductions in maximum position error, average position error, and control effort variation rate, along with a 94% tracking success rate. By balancing robustness, tracking precision, and computational efficiency, the method provides a theoretical foundation and a promising simulation-validated solution for airdrop missions. Full article

To address the problems of insufficient trajectory tracking accuracy, pronounced jitter over undulating terrain, and limited disturbance rejection in orchard mobile robots, this paper proposes a trajectory tracking control strategy based on a double-loop adaptive sliding mode. Firstly, a kinematic model of the orchard robot is constructed and a time-varying integral terminal sliding surface is designed to achieve global fast finite-time convergence. Secondly, a sinusoidal saturation switching function with a variable boundary is employed to suppress the high-frequency chattering inherent in sliding mode control. Thirdly, an improved double-power reaching law (Improved DPRL) is introduced to enhance disturbance rejection in the inner loop while ensuring continuity of the outer-loop output. Finally, Lyapunov stability theory is used to prove the asymptotic stability of the double-loop system. The experimental results show that attitude angle error settles within 0.01 rad after 0.144 s, while the position errors in both the x-axis and y-axis directions settle within 0.01 m after 0.966 s and 0.753 s, respectively. Regarding position error convergence, the Integral of Absolute Error (IAE)/Integral of Squared Error (ISE)/Integral of Time-Weighted Absolute Error (ITAE) are 0.7629 m, 0.7698 m, and 0.2754 m, respectively; for the attitude angle error, the IAE/ISE/ITAE are 0.0484 rad, 0.0229 rad, and 0.1545 rad, respectively. These results indicate faster convergence of both position and attitude errors, smoother control inputs, and markedly reduced chattering. Overall, the findings satisfy the real-time and accuracy requirements of fast trajectory tracking for orchard mobile robots. Full article

A dual-loop fault-tolerant control scheme is investigated for UAV attitude control systems subject to actuator faults, input saturation, and external disturbances in this paper. In the outer loop of attitude angles, a nonlinear dynamic inversion controller is developed as baseline controller for fast response and is augmented by a neural network disturbance observer to enhance the adaptability and robustness. Considering input saturation, actuator faults, and external disturbances in the inner loop of attitude angle velocities, the unbalanced input saturation is first converted into a time-varying system with unknown parameters and disturbances using a nonlinear function approximation method. An L1 adaptive fault-tolerant controller is then introduced to compensate for the effects of lumped uncertainties including system uncertainties, actuator faults, external disturbances, and approximation errors, and the stability and performance boundaries are verified by Lyapunov theorem and L1 reference system. Some simulation examples are carried out to demonstrate its effectiveness. Full article

This paper investigates the interval type-2 (IT2) fuzzy adaptive fixed-time bipartite consensus self-triggered control for multiple quadrotor unmanned aerial vehicles with deferred full-state constraints and input saturation under cooperative-antagonistic interactions. First, a uniform nonlinear transformation function, incorporating a shifting function, is constructed to achieve the deferred asymmetric constraints on the vehicle states and eliminate the restrictions imposed by feasibility criteria. Notably, the proposed framework provides a unified solution for unconstrained, constant/time-varying, and symmetric/asymmetric constraints without necessitating controller reconfiguration. By employing interval type-2 fuzzy logic systems and an improved self-triggered mechanism, an IT2 fuzzy adaptive fixed-time self-triggered controller is designed to allow the control signals to perform on-demand self-updating without the need for additional hardware monitors, effectively mitigating bandwidth over-consumption. Stability analysis indicates that all states in the closed-loop attitude system are fixed-time bounded while strictly adhering to deferred time-varying constraints. Finally, illustrative examples are presented to validate the effectiveness of the proposed control scheme. Full article

Accurate and robust satellite attitude control is essential for a wide range of scientific and commercial space missions, including Earth observation, communication, and navigation. However, maintaining consistent tracking performance remains challenging when external disturbances are unknown, time-varying, or difficult to model accurately. This paper proposes an adaptive learning gain (ALG)-based nonlinear control framework for satellite attitude control under such uncertain conditions. The proposed method integrates a backstepping design with an ALG mechanism that dynamically adjusts control gains in real time according to the actual tracking error, without requiring prior knowledge of disturbance characteristics or extensive gain tuning. Unlike conventional adaptive or disturbance observer-based approaches, the controller guarantees that tracking errors remain within user-defined performance bounds while reducing excessive control effort. The effectiveness of the proposed scheme is validated through detailed simulations of a Multibody satellite model implemented in MATLAB/Simulink(R2024a),demonstrating improved tracking accuracy, adaptability, and control efficiency under significant disturbance variations. The results suggest that the proposed framework offers a systematic and practical solution for attitude control in aerospace applications where disturbance environments are highly uncertain. Full article

Tailless unmanned aerial vehicles (UAVs) achieve high-agility maneuvers with flight control systems. The attainable moment set (AMS) provides critical theoretical foundations and constraints for their optimization. A computational method is proposed herein to address controllability limitations caused by nonlinear aerodynamic effectiveness. This method incorporates dual constraints on control surface angles and angular rates for the nonlinear AMS, aiming to meet the demands of attitude tracking dynamics in flight control systems. First, a quantitative model is established to correlate dual deflection constraints with aerodynamic moment amplitude and bandwidth limitations. Next, we construct a computational framework for the incremental attainable moment set (IAMS) based on differential inclusion theory. For monotonic nonlinear aerodynamic effectiveness, the vertices of the IAMS are updated using local interpolation, yielding the incremental nonlinear attainable moment set (INAMS). When non-monotonic nonlinearity occurs, stationary points are calculated to adjust the control effectiveness matrix and admissible control set, thereby reducing computational errors induced by non-monotonic characteristics. Furthermore, the effective actions set, derived from a time-varying incremental nonlinear attainable moment set, quantifies the residual moment envelope of tailless UAVs during maneuvers. Comparative simulations indicate that the proposed method achieves correct computation under nonlinear aerodynamic conditions while reliably determining safe flight boundaries during control failure. Full article