Search

Aiming to address the tracking accuracy and anti-rollover problem of the unmanned mining truck path tracking process under the complex unstructured road conditions in mining areas, a coordinated control strategy for path tracking and anti-rollover based on topology theory is proposed. Moreover, optimal equilibrium weights are assigned to path tracking control and anti-rollover control to ensure that the path tracking accuracy of the mining vehicle can be effectively improved in a safe and stable driving state. Regarding the path tracking problem, a lateral preview error model is established, and a path tracking controller is designed using LQR (linear quadratic regulator) control theory. In the design of the anti-rollover controller, the effects of understeer and trip-type rollover on the stability of the vehicle are taken into account, and the ideal transverse swing angular velocity and trip-type rollover evaluation index are introduced for controller design, which reduce the effects of the curves and roadway excitation on the mining truck and improve the rollover motion. Based on a joint simulation using Trucksim and Simulink and the construction of a hardware-in-the-loop simulation platform for verification, the single control strategy and coordinated control strategy are compared and analyzed. The final simulation results show that the tracking error, yaw velocity, and center of mass side deviation angle are optimized by 45%, 32.5%, and 20%, respectively. Therefore, the Extension theory-based coordinated controller satisfies the complex road conditions in the mining area and improves the tracking accuracy to the maximum extent while ensuring the safety and smoothness of vehicle driving and exhibiting good adaptability and robustness. [ABSTRACT FROM AUTHOR]

Considering the hybrid dynamical characteristics of multimode hybrid electric vehicle during a car‐following process, a novel energy management strategy based on hybrid system theory is proposed in this article. Firstly, nonlinear powertrain model and longitudinal dynamics of a single‐shaft parallel hybrid electric vehicle is built, and hybrid characteristics combined with continuous dynamics and discrete events during operating mode transition are described. Secondly, the nonlinear powertrain model and characteristics are approximated by the piecewise affine method, the mixed‐logic dynamic modeling method is introduced and system variables are defined. Then the hybrid dynamical model for multimode hybrid electric vehicle in a car‐following process is established with the hybrid system description language. Thirdly, according to the principle of receding horizon control, a hybrid model predictive controller is designed and applied for optimizing the multiobjective energy management problem in the car‐following process. Finally, the proposed control strategy is compared with a rule‐based control strategy, and the simulation results show that the proposed control strategy achieves fuel efficiency improvement while ensure the dynamic performance, driving safety and ride comfort during the car‐following process, and the designed controller meets the requirement of deep fusion of car‐following and energy management. [ABSTRACT FROM AUTHOR]

The road condition is complex and variable during driving; hence, the suspension system cannot easily suppress the vertical vibration of the vehicle. To effectively improve the vehicle dynamic performance under different road conditions, this study proposes a switching control strategy for the semi-active suspension based on the road profile estimation. First, the mapping relationship between different driving conditions and requirements of vehicle dynamic performances is studied. Then, a switching controller with multiple sub-controllers is designed on the basis of the modified skyhook control. The optimal control gains are obtained by adopting the cuckoo search algorithm. In addition, to realise the automatic switching of the controller under different road conditions, an online road profile estimation method, including road disturbance estimation and road roughness classification, is developed. Bench test results show that the proposed control algorithm has an obvious effect on improving the vehicle ride comfort and handling performance, which verifies the effectiveness of the road estimation results and the superiority of the switching control strategy. [ABSTRACT FROM AUTHOR]

• The idea of fast-slow switching for full road conditions is proposed. • The stability of the switching system is analyzed. • The accurate estimation of road profile/level and state variables are realized. The hybrid electromagnetic actuator integrates a magnetorheological damper, and a linear motor can effectively reduce the energy consumption of linear electromagnetic active suspension by switching the control parameters with the same dynamic performances. Thus, the intelligent switching control of hybrid electromagnetic active suspension (HEMAS) is investigated with a fast–slow switching method for full road conditions for the first time, which uses the road level and system state variables as the switching rules. First, the intelligent switching control system architecture of HEMAS is designed, and the switching logic is refined. Then, the modified skyhook control strategy is adopted, and the control parameters (including overfull and transient objective control parameters) are optimized for different driving conditions. Then, the simultaneous estimation method of road profile and system state variables and the identification method of road level are studied. The identification result is used as a prerequisite for intelligent switching of system control parameters. Last, a road adaptive intelligent switching control system is designed, and the stability of the switching system is analyzed. Furthermore, a hardware-in-the-loop bench test is performed. The test results verify the effectiveness of the intelligent switching control system in improving the comprehensive performances (dynamic and energy-saving performances) of HEMAS. [ABSTRACT FROM AUTHOR]