Jul 2026· International Journal of Robust and Nonlinear Control· 0 citations· 35 references
Abstract
The multi‐speed planetary hybrid drivetrain features a complex structure, where transient variations from multiple excitation sources are highly coupled with vehicle structural parameters, leading to severe torsional vibration issues, especially during engine start‐stop processes. This study proposes an unknown input‐compensated least mean square (UIC‐LMS) adaptive algorithm for active torsional vibration suppression. An engine excitation model is established through time‐ and frequency‐domain analyses, while a six‐degree‐of‐freedom dynamics model of the drivetrain is developed using the lumped mass method, with frequency‐domain analysis revealing the resonance mechanisms. To achieve precise torque estimation under uncertainties, a reduced‐order unknown input observer (UIO) is designed by reconstructing measured outputs. The least mean square (LMS) adaptive filtering then computes motor compensation torque, with real‐time parameter adjustment via a transversal filter. Hardware‐in‐the‐loop test demonstrate that the UIO improves estimation accuracy by over 78.9% compared to the Luenberger observer, showing superior robustness against unknown input disturbances and measurement noise. Under UIC‐LMS control, maximum amplitude fluctuations of transmitted torque and torsional angle in the torsional vibration damper are reduced by over 44.8%, while those in the driveshaft are reduced by more than 72.5%. Drawing parallels with advanced equivalent‐input‐disturbance rejection strategies for uncertain systems, the algorithm maintains superior effectiveness across drivetrain parameter uncertainties by integrating UIO‐based lumped disturbance observation with LMS adaptive compensation. Real‐vehicle validation confirms the algorithm's robustness and high torsional vibration suppression rates in embedded controllers.
To address the horizontal vibration problem of high‐speed elevator car systems induced by multi‐source excitations, including guide‐rail irregularities, traction machine disturbances, and shaft aerodynamic effects, this paper proposes an adaptive control method integrating natural logarithmic sliding mode control and state observation. First, the aforementioned multi‐source excitations and system uncertainties were categorized into three types: parameter variations, external disturbances, and state variations. Based on this, the State–Parameter–Uncertainty High‐Speed Elevator Vibration Model (SPU‐HEVM) was established. Second, a sliding‐mode observer was designed to reconstruct the system state, and unknown parameters were updated online using a projection‐adaptive law. Building on this, the natural logarithmic sliding surface was integrated with the sliding‐mode observer and the projection‐adaptive law into a unified control framework, enabling performance‐oriented parameter design and reducing reliance on exact models and trial‐and‐error tuning. Finally, the accuracy of the model was verified through real‐world elevator experiments and multi‐source composite simulations, and the proposed method was compared with adaptive sliding mode control (ASMC) and adaptive terminal sliding mode control (ATSMC). The results demonstrate that the proposed method in this paper can accurately estimate system states and parameters, with typical vibration acceleration values reduced by more than 25% compared to ASMC, significantly improving vibration suppression performance and system robustness.
Xiaofan Zhao, Qin He, Yao Zhu et al.· International Journal of Rob...· 0 citations
This paper proposes a hybrid control strategy for flexible‐link manipulators that combines offline trajectory planning with adaptive tracking to suppress residual elastic vibration (REV) under unknown bounded disturbances. A rigid–flexible coupling dynamics model is first established using the floating‐frame method and Lagrange's equations, forming a differential–algebraic system as the foundation for controller design and optimization. A parameterized cosine‐based joint trajectory is optimized via particle swarm optimization to reduce endpoint deformation. The resulting trajectory is tracked using an adaptive sliding‐mode backstepping controller enhanced with a nonlinear disturbance observer. Simulations show that the optimized trajectory reduces REV amplitude and shortens motion time compared with the reference trajectories considered in this study. Under the considered numerical conditions, the proposed controller yields smaller residual deformation than the selected proportional–derivative reference controller in both the disturbance‐free and disturbed cases. These results indicate that, within the adopted modeling assumptions, disturbance description, observer applicability range, and actuator‐amplitude‐constrained setting, the proposed strategy can improve tracking performance and vibration suppression for the flexible manipulator.
Mingming Shi, B. Rong, X. Rui et al.· International Journal of Mec...· 0 citations
Torsional vibrations induced by seismic excitation can significantly increase structural demand, particularly in systems with geometric or stiffness irregularities. This proof-of-concept study proposes a hybrid adaptive control framework that combines passive gyroscopic damping with reinforcement learning (RL)-based adaptive torque control for torsional response mitigation. A simplified three-degree-of-freedom (3DOF) structural model is evaluated under bidirectional earthquake loading, while the control policy is trained using the Proximal Policy Optimization (PPO) algorithm to generate adaptive torque directly from measured system states without explicit system identification. Numerical simulations using recorded earthquake ground motions show that the proposed approach reduces root-mean-square torsional displacement to 0.012 ± 0.001 rad, representing a 65.7% reduction relative to the uncontrolled case and improved performance over passive gyroscopic control alone. Sensitivity analyses involving variations in mass, stiffness, and damping indicate stable controller performance across the structural parameter ranges considered. The trained controller achieved sub-millisecond inference time with minimal computational overhead, supporting its computational feasibility for future real-time implementation. The results demonstrate the potential of integrating physically grounded gyroscopic damping with data-driven adaptive control for intelligent torsional vibration mitigation.
S. Stephen, Ali Hadi, O. Akinradewo et al.· Asian Journal of Civil Engin...· 0 citations
This paper addresses multi-source composite disturbances in photovoltaic cleaning UAVs during continuous operation, including fluid-structure sloshing in the tank, time-varying mass decay, and near-wall unsteady aerodynamic interference. We propose an adaptive sliding-mode composite disturbance rejection strategy with physical-prior boundary constraints. Traditional controllers based on constant-rigid-body assumptions often induce trajectory overshoot or even instability under such conditions because they cannot effectively compensate the coupled effects of transient fluid impacts and mass loss. Therefore, we first establish a six-degree-of-freedom variable-mass nonlinear UAV dynamic model that includes additional Coriolis-related terms. On this basis, a collaborative disturbance-rejection architecture integrating intelligent sensing and online estimation modules is proposed: On one hand, a nonlinear disturbance observer (NDO) and a robust projection-based identification law are designed to achieve online decoupling and compensation of external low-frequency gusts and internal parameter drift; on the other hand, sloshing-force extrema extracted from fluid-structure simulations are used as dynamic constraint thresholds to construct a constrained adaptive sliding-mode controller (ASMC) with anti-chattering characteristics. Lyapunov theory proves the uniformly ultimately bounded (UUB) property of closed-loop states. Numerical simulations show that the proposed strategy confines roll-angle deviation within 0.15 rad under strong liquid-surface agitation and achieves zero-error altitude tracking with steady-state error below ±0.015 m during monotonic mass decay induced by continuous discharge. In addition, the physical-prior mechanism suppresses actuator chattering and saturation caused by high-frequency switching at the control source. The proposed architecture combines low computational cost with strong robustness, providing effective theoretical and technical support for engineering deployment of special liquid-carrying UAVs.
Weijia Li, Hexu Yang, Feng Xie et al.· International Conference on...· 0 citations
Offshore wind turbine blade repair requires stable material removal and precise force regulation under curved‐surface contact and environmental disturbance. To address these challenges, this paper proposes an integrated constant‐force grinding method that combines a passive compliant end‐effector, a particle swarm optimization‐backpropagation neural network (PSO‐BP), and an improved active disturbance rejection control (ADRC) strategy. First, a passive compliant end‐effector with variable stiffness is designed to improve contact adaptability and reduce grinding impact on curved blade surfaces. Second, a PSO‐BP model is established to predict the material removal rate (MRR) and surface roughness (Ra) under different grinding conditions, thereby providing data‐driven support for process‐state evaluation and parameter scheduling. Third, based on a controller‐oriented force‐dynamics model, an improved ADRC framework integrating a tracking differentiator, nonlinear extended state observer, nonlinear state error feedback, and PSO‐BP‐assisted gain scheduling is developed for constant‐force grinding. A Lyapunov‐based analysis shows that the closed‐loop system is uniformly ultimately bounded under bounded disturbances and bounded scheduling error. Simulation and experimental results demonstrate that, compared with PID and standard ADRC, the proposed method achieves higher force‐tracking accuracy, stronger disturbance rejection, and better grinding quality. Under equivalent initial damage conditions, it produces the lowest post‐grinding surface roughness, indicating that the proposed method provides an effective solution for offshore blade grinding repair.
Yuhang Xue, Xinrong Liu, Tianhao Wang· Journal of Field Robotics· 0 citations
In conventional model predictive control, three‐level inverter‐fed induction motor systems are susceptible to parameter mismatch, leading to degraded control performance. To enhance parametric robustness against nonlinear dynamics and impulsive noise, this paper proposes a model‐free predictive torque control using a correntropy criterion–based unscented Kalman filter (CCUKF). First, an ultralocal model is employed to consolidate system uncertainties into a lumped disturbance. Second, the sigma‐point sampling method of the unscented Kalman filter accurately captures nonlinear statistical characteristics, avoiding the linearization errors inherent in the extended Kalman filter and improving state estimation accuracy. Furthermore, the correntropy criterion is introduced to optimize the Kalman gain, robustly suppressing non‐Gaussian noise and outliers caused by electromagnetic interference. Experimental results demonstrate improvements in both dynamic response and steady‐state performance, along with effective suppression of torque fluctuations, showing superior performance compared with conventional methods while reducing dependence on motor parameters.
Bo Yang, Zerun Liu, Zhaoxun Li et al.· International journal of cir...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
