For the sake of realizing dynamic switch among three steering modes for the four wheel independent steering (4WIS) rescue vehicle in the non-stop state, the switch control strategy and the optimized wheel switch trajectory are studied in this paper. Aiming at ensuring the constant turning radius of the 4WIS rescue vehicle in the switch process of steering modes, the steering angle relationships between the driving wheel and driven wheels are derived, and the desired driving wheel angle of the target steering mode is calculated. The B-spline curve is proposed to design the switch trajectory of the driving wheel. Two objectives including minimizing the sudden change of vehicle dynamic parameters and minimizing the energy consumption in the switch process are discussed. A multi-objective genetic algorithm (MOGA) based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed to deal with the optimization problem of the wheel switch trajectory. The numerical simu-lations based on Matlab/Simulink indicate that the presented algorithm can generate the Pareto optimal solutions and is suitable for the optimization of the wheel switch trajectory. In addition, the proposed switch control strategy of steering modes for the 4WIS rescue vehicle in the non-stop state is validated to be reasonable and effective.

Effective reuse and recycling of end-of-life (EOL) products are essential for improving resource efficiency and protecting environment. Currently, many parts of construction machinery in EOL stage can still be reused directly because they are designed with extreme high strength to meet the bad working conditions, which minimizes the impact on the environment. In this context, a quality evaluation approach for reuse parts is proposed in this paper. Ontology model is built for representing evaluation information with semantic properties and constructing the semantic relevance among the various concepts involved in parts reusing domain, thus achieving the integrating, sharing, and reusing of evaluating parts knowledge. On that basis, analytic hierarchy process (AHP) is put forward to quantify the reusability degree of the parts of EOL construction machinery. Furthermore, combined with ontology, rule-based-reasoning method is utilized to get suggested strategies for reuse parts. In addition, a web-based system is developed to assist manufacturers in managing reuse parts, and a case study is analyzed to demonstrate the proposed quality evaluation approach.

Due to complex and uncertain driver behaviors during pedestrian collision avoidance (PCA) maneuvers, it is challenging to accurately describe human driver behaviors for manual takeover of automated vehicles. In this context, this paper proposes a novel modeling method integrating neuromuscular dynamics with a long short-term memory (LSTM) network. A neuromuscular dynamics model is responsible for capturing the dynamic interaction between the steering system and the neuromuscular system of the driver, whereas a LSTM network is used to describe the visual behaviors of the driver accurately during manual takeover. A series of high-fidelity driving simulator experiments with 12 participants were conducted to acquire driver behaviors during manual takeover under PCA conditions. Scenarios with manual takeover steering and conventional manual steering were compared to analyze driver behaviors under PCA maneuvers. In terms of root mean square error, mean absolute error, and determination coefficient, the experimental results indicated that the proposed method had higher identification accuracy than a method combining two-point visual modeling and neuromuscular dynamics. The proposed method also captured the dynamic interaction between the vehicular steering system and the neuromuscular system. Thus, in the interest of developing manual takeover control systems of automated vehicles, the proposed method can better represent lateral behaviors of drivers.

To improve the road feeling of the steer-by-wire (SBW) system, a fractional order PID (proportion-integral-derivative) method with a fault tolerant module is proposed in this paper. Firstly, the overall road feeling control strategy of the SBW system is introduced, and then the mathematical model of road feeling control is established. Secondly, a fractional order PID (FOPID) controller is designed to control torque of the road feeling motor. Furthermore, genetic algorithm (GA) is applied to tune the FOPID controller’s parameters. Thirdly, a fault tolerant module aiming at potential failures of the motor’s torque sensor is studied to improve the reliability of the system. Kalman Filter (KF) algorithm is utilized in the fault tolerant module so as to detect failures of the motor’s torque sensor, and then fault tolerant module reconfigures the motor’s torque estimated by KF as a substitute when the torque sensor fails. Finally, simulations based on MATLAB are performed with the proposed control strategy to identify its performance, and the results demonstrate that the proposed control method is feasible and accurate.

Leveling of the crane support platform plays a vital role in operational safety and lifting efficiency; it requires both precise horizontal positioning and the rational distribution of outrigger load. However, the current synchronous leveling methods mainly focus on displacement synchronization leveling while neglecting the control of outrigger load, resulting in the problem of individual outrigger overloading. To address this problem, a synchronous leveling control method with variable load constraints (SLCM-VLC) is proposed in this paper based on the framework of model predictive control. Firstly, the proposed method conducts independent outrigger modeling and decoupling of outriggers through adjacent cross-coupling; then a displacement synchronization controller (DSC) is designed to ensure efficient synchronous leveling. Secondly, a collaborative controller of displacement and force (DFCC) under variable load constraints is designed to overcome the limitations of traditional independent optimization. Subsequently, an extended state observer (ESO) is introduced to compensate for environmental disturbances and control deviations. Finally, the effectiveness of the proposed method is verified through a co-simulation using Matlab, Adams, and Solidworks. The results show that, compared with existing leveling control methods, the proposed method can achieve high precision and rapid leveling under smaller peak load, thereby extending the service life of the platform’s electric cylinders.

In order to mitigate human-machine conflicts and optimize shared control strategy in advance, it is essential for the shared control system to understand and predict driver behavior. This paper proposes a method for predicting driver steering intention with a CNN-GRU hybrid machine learning model. The convolutional neural network (CNN) layer extracts features from the stochastic driver behavior, which is input to the gated-recurrent-unit (GRU) layer. And the driver’s steering intention is forecasted based on the GRU model. Our study was conducted using a driving simulator to observe the lateral control behaviors of 18 participants in four different driving circumstances. Finally, the efficiency of the suggested prediction approach was evaluated employing long-short-term-memory, GRU, CNN, Transformer, and back propagation networks. Experimental results demonstrated that the proposed CNN-GRU model performs significantly better than baseline models. Compared with the GRU network, the CNN-GRU network reduced the RMSE, MAE, and MAPE of the driver’s input torque by 33.22%, 32.33%, and 35.86%, respectively. The proposed prediction method also possesses adaptability to different driver behaviors.