This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study.

In order to improve the dynamic quality of traditional sliding mode control for an active suspension system, an optimal sliding mode control (OSMC) based on a genetic algorithm (GA) is proposed. First, the overall structure and control principle of the active suspension system are introduced. Second, the mathematical model of the quarter car active suspension system is established. Third, a sliding mode control (SMC) controller is designed to manipulate the active force to control the active suspension system. Fourth, GA is applied to optimize the weight coefficients of an SMC switching function and the parameters of the control law. Finally, the simulation model is built based on MATLAB/Simulink (version 2014a), and the simulations are performed and analyzed with the proposed control strategy to identify its performance. The simulation results show that the OSMC controller tuned using a GA has better control performance than the traditional SMC controller.

Considering the demand for vehicle stability control and the existence of uncertainties in the four-wheel steering (4WS) system, the mixed H2/H∞ robust control methodology of the 4WS system is proposed. Firstly, the linear 2DOF vehicle model, the nonlinear 8DOF vehicle model, the driver model, and the rear wheel electrohydraulic system model were constructed. Secondly, based on the yaw rate tracking strategy, the mixed H2/H∞ controller was designed with the optimized weighting functions to guarantee system performance, robustness, and the robust stability of the 4WS vehicle stability control system. The H∞ method was applied to minimize the effects of modeling uncertainties, sensor noise, and external disturbances on the system outputs, and the H2 method was used to ensure system performance. Finally, numerical simulations based on Matlab/Simulink and hardware-in-the-loop experiments were performed with the proposed control strategy to identify its performance. The simulation and experimental results indicate that the handling stability of the 4WS vehicle is improved by the H2/H∞ controller and that the 4WS system with the H2/H∞ controller has better handling stability and robustness than those of the H∞ controller and the proportional controller.

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.

Haptic-shared steering (HSS) systems have been reported to enhance vehicle safety and reduce the workload for drivers. However, few studies have focused on modeling the lateral vehicle control behaviors of distracted drivers for HSS. The current study models this type of behavior in a series of high-fidelity driving simulator experiments with 18 participants. Two experimental conditions for a double lane change task are tested: HGT-Constant (haptic guidance torque with a constant gain) and HGT-Adaptive (haptic guidance torque with an adaptive gain). A gated recurrent unit (GRU) network is used to model lateral control behavior during driving. The effectiveness of the GRU network-based lateral control model of distracted drivers is benchmarked using a state-of-the-art long short-term memory network, a back propagation network, an extreme learning machine, and a traditional two-point visual model with neuromuscular dynamics. Experimental results indicate that the GRU network has the highest accuracy in terms of root mean square error, mean absolute error, mean absolute percentage error, and determination coefficient. In addition, a simulation model of the driver-vehicle-road closed-loop system demonstrates that the proposed model predicts driver behavior with acceptable lateral position error.