Qing Zhang

Villa elevator noise is predominantly concentrated in the 20–500 Hz frequency range, with a reverberation time of 0.2 s, which is far shorter than the 0.4 s of conventional elevators. This makes it challenging for adaptive filters to guarantee effective noise reduction performance. A novel deep learning-based active noise cancellation method is proposed based on Dominant Transmission Path and Complex-spectrum Network (DTPC-Net). First, a transmission path model was established, and the primary noise source and its dominant propagation direction were identified based on coherence analysis and energy distribution. Subsequently, a one-dimensional modeling strategy oriented toward the dominant propagation direction is proposed to address the high computational complexity of three-dimensional modeling. Furthermore, a convolutional recurrent network was designed to estimate the complex spectrum of noise signal. A hierarchical long short-term memory module and a Squeeze-and-Excitation attention module were incorporated to capture dynamically varying noise features. In addition, a predictive compensation mechanism based on blank frame padding was proposed to train the neural network and mitigate system delay. Finally, noise was reduced by 10.29 dB and 8.76 dB when the villa elevator operates at 0.4 m/s and 1 m/s, respectively. And the corresponding A-weighted reductions were 7.62 dBA and 7.54 dBA.

Purpose: To solve the poor real-time performance of active control systems for ultra-high-speed elevator horizontal vibration suppression, which is caused by sensor signal delays and unknown external disturbances. Design/methodology/approach: First, a nonlinear 8-degree-of-freedom dynamic model of elevator horizontal vibration with a state delay mechanism was established. Then, a GRU-based state estimator was developed and a modified DDPG-NoisePER algorithm was proposed, integrated into GRU-DDPG-NoisePER to train the optimal delayed-state control strategy. Findings: The GRU-DDPG-NoisePER-based strategy reduces the root mean square of car vibration acceleration by over 59%, outperforming DDPG-NoisePER by more than 1.02 times and DDPG-PER by more than 1.32 times. Practical implications: The GRU-DDPG-NoisePER algorithm can be directly applied to ultra-high-speed elevator horizontal vibration control. It solves real-time issues caused by state delays, reduces equipment wear from excessive vibration, lowers maintenance costs, and provides a feasible technical solution for elevator manufacturers to optimize product performance in practical engineering scenarios. Social implications: As a core transportation tool in high-rise buildings, stabilized ultra-high-speed elevators improve passengers' travel comfort (reducing vibration discomfort) and safety. The technology also drives technical upgrading of the elevator industry, aligns with smart city transportation needs and indirectly guarantees efficient and reliable urban vertical transportation. Originality/value: This study innovatively integrates GRU with a modified DDPG (DDPG-NoisePER) to form GRU-DDPG-NoisePER. It effectively addresses state delay issues, improves DRL training efficiency, enhances vibration control effect and has strong practical application potential.

To address horizontal vibrations in high-speed elevators caused by track unevenness and guide rail joints excitations and considering the multi-variable coupling of high-speed elevator systems, a robust dynamic game distributed model predictive control strategy based on dynamic multi-agent interaction is proposed. First, the coupling constraints of the elevator system are analyzed, and a multi-agent dynamic interaction model is established. Then, dynamic game theory and distributed model predictive control are integrated to minimize the dynamic information interaction objective function, achieving cooperative optimal control for multi-agent distributed model prediction. Additionally, a terminal constraint-based Lyapunov function is constructed to ensure asymptotic stability, mitigating the impact of predictive interaction information on system stability. Robust state feedback and H∞ performance metrics are then designed to suppress disturbances in the dynamic game system. Simulation results show that the acceleration responses of the multi-agent dynamic interaction car system under two typical rail excitations outperform those under passive control and multi-agent model predictive control (MAMPC) in terms of peak values and A95, validating the effectiveness of the proposed vibration reduction strategy.

Horizontal vibration of cars and high energy consumption of active controllers are two major problems in ultra-high-speed elevators, which will seriously affect ride comfort and cost. To address these issues, a novel active control method with deep reinforcement learning (DRL) based on a multivariate state observation (MSO) reward mechanism was proposed in this study. An eight-degree-of-freedom nonlinear dynamics model for horizontal vibration of cars in ultra-high-speed elevators was first established with precise simulation of the random guide rail excitation and the lateral aerodynamic forces. Subsequently, an MSO reward mechanism integrating multi-scale information such as vibration velocity, deflection angular velocity, and control force, was developed and added to the Softmax deep double deterministic policy gradients (SD3) algorithm. Finally, active control strategies for horizontal vibration were trained on the dynamics model using the SD3-MSO algorithm and three other DRL algorithms including deep deterministic policy gradient (DDPG), Twin delayed DDPG, and SD3. The results indicate that the active control using SD3-MSO algorithm could significantly reduce the horizontal vibration acceleration and deflection angular acceleration of the car. Vibration reduction performance is further improved by utilizing a smaller active control force. The root mean square (RMS) value of the active control force decreases by over 26% compared to the traditional fuzzy logic controller, demonstrating its superior energy-saving capacity.

To solve the horizontal vibration problem of high-speed elevators caused by unevenness of guide rails and excitation of guide rail joints, considering the uncertainty of high-speed elevator parameters and multivariable coupling, an adaptive explicit distributed model predictive control strategy based on a multi-agent car system of high-speed elevators is proposed. First, the coupling constraints of high-speed elevator car are analyzed, and the horizontal vibration model of multi-agent car system is established. Second, the topology relationship of car multi-agent communication based on graph theory is analyzed, and the optimal control law of agent distribution model prediction is realized offline by multi-parameter programming technology. Meanwhile, by introducing a correction term with parameter estimation error, the uncertain parameter terms of the multi-agent car system can be estimated online. Design an adaptive explicit distributed model predictive control strategy for multi-agent car systems by combining offline collaborative distributed optimization with online uncertain parameter adaptive estimation. Finally, the time-domain and frequency-domain responses of high-speed elevator multi-agent car systems under two typical guide excitations are analyzed by simulation calculation and compared with the numerical results under passive control, adaptive control, and model predictive control. The results show that after the proposed control strategy is adopted, the mean horizontal vibration acceleration of the car system decreases by more than 48.4%, which further verifies the effectiveness of the proposed control strategy.

The offshore platform tower crane is affected by sea breeze, which causes severe vibration, which causes damage to the crane structure and reduces the working efficiency and performance of the crane. Therefore, the vibration suppression under the complex environment of sea breeze has become a key problem to be solved in practical engineering. Firstly, a pulsating wind excitation model with wind speed changing at any time is established to simulate the time sequence of downwind and crosswise wind loads, which is used to reflect the actual wind speed fluctuations. Secondly, the pulsating wind model is applied to the crane finite element model, and the structural vibration characteristics are explored by analyzing the dynamics of the tower crane under wind excitation，the dynamic model of tower crane system under pulsating wind excitation is established, and the system state equation is constructed. Considering the vibration displacement performance of tower crane structure, a hybrid [ ] optimal performance vibration active control strategy is proposed. Finally, simulation experiments are carried out. The results show that, compared with the AA controller under the same conditions, the root mean square of displacement under downwind excitation decreases by 65.77%, 64.31% and 63.13%, respectively, and the root mean square of displacement under transverse wind excitation decreases by 62.8%, 65.51% and 62.6%, respectively. The results show that the proposed control method can effectively suppress tower crane vibration.

The elevator technology is increasingly advancing toward higher speeds, and thus, the study on aerodynamic drag reduction has become urgent and important. This paper proposes for the first time a high-speed elevator aerodynamic drag reduction method based on the bionic design of boxfish. Through shape analysis of the boxfish, a bionic fairing model for high-speed elevators was constructed. Finally, the aerodynamic characteristics and airflow velocity of the two elevator shapes under various parameters were subjected to comparative analysis. The results show that after the flow field reached stability, the drag coefficient of the boxfish-inspired bionic flow guide model (Fs) was reduced by 71.5% compared with the traditional rectangular structure (Car), and the oscillation of aerodynamic parameters of the structure was weakened. The boxfish-inspired bionic flow guide model exerted minimal effect on the pressure and velocity distribution in the wake field, which promoted the reduction in the lateral vibration of the elevator and enabled its smooth operation. The vortex at the rear of the boxfish-inspired bionic flow guide model became shorter and more concentrated toward the center, and the Strouhal number increased by 38.5%, which led to an increased frequency of vortex shedding in the elevator wake. The drag reduction effect of the boxfish-inspired bionic flow guide model is directly proportional to the blockage ratio, reaching up to 57%, and can considerably reduce energy conversion under various inclination angles.

Due to its complex nonlinear, underdriven, and strongly coupled characteristics, the tower crane will cause the load to swing violently when working, which will cause the tower crane to tip over in serious cases, and there exists a huge safety hazard. In this article, a new artificial neural network sliding mode control method is designed for the control problems of tower crane lifting in position and load swing prevention, which has strong robustness to disturbances and unmodeled dynamics, and ensures that the tower crane lifting is accurately tracked in position and suppresses the load swing at the same time. First, a nonlinear dynamics model of a five-degree-of-freedom tower crane considering the actual working conditions is established. Aiming at the problem that it is difficult to effectively control the nonlinear model of the tower crane system, a new neural sliding mode controller and compensation controller are designed based on the sliding mode control theory and using radial basis function neural network. The neural sliding mode controller is used to approximate the sliding mode equivalent controller with uncertainty and strong nonlinearity, and the compensation controller realizes the compensation of the neural sliding mode controller for the difference between the system control inputs and the uncertainty of the system. The convergence and stability of the proposed control system is rigorously demonstrated using the Lyapunov stability theory. Simulation studies have been carried out to verify the correctness of the model established in this article, as well as the excellent control performance of the control system and the ability to deal with system uncertainty, proving its strong robustness.