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In this paper, an adaptive controller is proposed for an active suspension system to achieve optimal compromise performance for vehicles equipped with non-pneumatic wheels under different road conditions. Firstly, the effective vertical stiffness of the non-pneumatic wheel (NPW) was identified through the static force-deflection tests. Then, the effect of the variations in NPW stiffness and mass on the vibration responses was investigated using a quarter-vehicle model. In order to coordinate the ride comfort and handling performance of the vehicle for different road excitations, an adaptive controller was synthesized using the model predictive control (MPC) theory together with an H ∞ state observer. The control gains for different control objectives were determined using a genetic algorithm (GA). Simulations indicate that the proposed controller can adapt to different road excitations and effectively enhance the dynamic performance of the vehicle. Specifically, by applying adaptive control, the root-mean-square (RMS) value of sprung mass acceleration (SMA) and the dynamic wheel load (DWL) coefficient are reduced by 19.4% and −9.3% on Class B roads and 12.4% and 3.8% on Class C roads, respectively, which is superior to the modified skyhook control (19.4% and −11.8% on Class B roads, and 19.3% and −12.3% on Class C roads). The effectiveness of simulation results was subsequently verified through hardware-in-the-loop experiments. [ABSTRACT FROM AUTHOR]

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]

A self-powered electromagnetic vibration suppression and absorption system integrated with a magnetorheological damper and a linear motor is designed to attenuate the negative effect of vertical vibration caused by the increased unsprung mass for in-wheel motor electric vehicles in this article. The magnetorheological damper is used as a suspension damper to suppress body vibration, and linear motor is used as a dynamic vibration absorber, namely, linear electromagnetic dynamic vibration absorber, to absorb tire vibration, and regenerates the vibration power to drive the magnetorheological damper, realizing self-power. Based on power flow theory, the power transfer mechanism of the vertical vibration for in-wheel motor electric vehicles and the regeneration potential are analyzed. The negative effect on the dynamic performance of in-wheel motor electric vehicles is analyzed through the root mean square of dynamic responses. Moreover, the specific structure scheme of the self-powered electromagnetic vibration suppression and absorption system is provided. The influence of system mass, stiffness, and damping of the linear electromagnetic dynamic vibration absorber on the dynamic performance is analyzed, and these parameters are optimized by particle swarm optimization. Simulation results show that in comparison with a passive damper, the self-powered electromagnetic vibration suppression and absorption system can reduce the body acceleration by 17.05%, which is better than the magnetorheological damper (10.08%). The self-powered electromagnetic vibration suppression and absorption system increases the tire dynamic load by 5.62%, but it is 8.68% less than the magnetorheological damper. Additionally, the regenerated power can offset the consumed power adequately to realize self-power. Finally, a bench test is conducted to verify the effectiveness and feasibility of the self-powered electromagnetic vibration suppression and absorption system. [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]

This paper presents a vehicle attitude compensation algorithm based on state observer for vehicle semi-active suspension system equipped with four magneto-rheological dampers (MR dampers). The proposed algorithm including magneto-rheological damper control algorithm, attitude compensation control algorithm, and design method of state observer is to effectively improve ride comfort and control vehicle body attitude. First, the actual equivalent damping of magneto-rheological damper is introduced into state observer, and the parameter matrix of suspension system is updated in real time via precise discretization method to enhance the estimation accuracy of state observer. Then, the velocity signal estimated by state observer is employed as the evidence to realize attitude compensation control for vehicle body. Finally, relevant co-simulations and hardware-in-the-loop test are conducted to verify the validity of the proposed control algorithm. Results of simulations and tests demonstrate that the application of the control algorithm proposed in this paper can significantly improve ride comfort of magneto-rheological suspension and optimize vehicle body attitude. [ABSTRACT FROM AUTHOR]