This paper presents a control algorithm for the motorized active suspension damper. The control algorithm consists of supervisory, upper-level and lower-level controllers. The supervisory controller determines the control modes, such as the passive mode, the roll mode and the body acceleration mode. The upper-level controller computes the damping force using linear quadratic control theory. The actuator input is determined by the lower-level controller. Three state estimators, namely the vehicle body’s velocity estimator, the suspension state estimator and the friction estimator, are proposed to estimate the sprung-mass and unsprung-mass velocities, the tyre deflection, the roll angle, the roll rate and the friction. The performance of the proposed control algorithm was evaluated via simulations and vehicle tests. It was shown from both simulations and vehicle tests that the proposed control algorithm can improve the ride quality using a motorized active suspension damper.
This paper describes torque distribution control of six-wheeled in-wheel motor vehicles by considering the friction circle of each wheel for enhanced terrain-driving performance. Using control allocation, the proposed torque distribution algorithm determines the torque command to each wheel, by considering the size of the friction circle. The friction circle of each wheel is estimated using a linear parameterized tyre model with two threshold values. The parameters and the threshold values are computed from measurements of the wheel speed, the yaw rate, the acceleration and the torque command signals using a recursive least-squares method. Simulation studies were conducted using TruckSim and MATLAB/Simulink co-simulations. It was confirmed that the proposed friction circle estimation algorithm can be successfully used for torque distribution to enhance the terrain-driving and hill-climbing performance.
This paper presents a mode control algorithm of motorized active suspension damper (MASD) for ride quality and energy efficiency. The control algorithm has been developed based on a Full-car model. The model consists of front and rear half-car dynamics. The proposed control algorithm consists of supervisory, upper-level and lower-level controllers. The control modes, such as passive and roll control modes are determined in the supervisory controller. The upper-level controller derives damping force using linear quadratic control theory. The lower level controller determines actuator input. At the same time, state estimators, vehicle body velocity estimator, suspension state estimator, are designed to estimate vehicle states used to control the actuator. The performance of the proposed mode control algorithm has been evaluated through simulations. It has been shown that the proposed MASD control algorithm improves the ride quality and energy efficiency.
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