This work presents an integrated pressure-tracking controller for a novel electro-hydraulic brake(EHB) system considering friction and hydraulic disturbances. To this end, a mathematical model of an EHB system, consisting of actuator and hydraulic sub-systems, is derived for describing the fundamental dynamics of the system and designing the controller. Due to sensor inaccuracy and measurement noise, a Kalman filter is constructed to estimate push rod stroke for generating desired master cylinder pressure. To improve pressure-tracking accuracy, a linear friction model is generated by linearizing the nonlinear Tustin friction model, and the unmodeled friction disturbances are assumed unknown but bounded. A sliding mode controller is designed for compensating friction disturbances, and the stability of the controller is investigated using the Lyapunov method. The performance of the proposed integrated controller is evaluated with a hardware-in-the-loop(HIL) test platform equipped with the EHB prototype. The test results demonstrate that the EHB system with the proposed integrated controller not only achieves good pressure-tracking performance, but also maintains robustness to friction disturbances.
A control allocation algorithm based on pseudo-inverse method was proposed for the over-actuated system of four in-wheel motors independently driving and four-wheel steering-by-wire electric vehicles in order to improve the vehicle stability. The control algorithm was developed using a two-degree-of-freedom(DOF) vehicle model. A pseudo control vector was calculated by a sliding mode controller to minimize the difference between the desired and actual vehicle motions. A pseudo-inverse controller then allocated the control inputs which included driving torques and steering angles of the four wheels according to the pseudo control vector. If one or more actuators were saturated or in a failure state, the control inputs are re-allocated by the algorithm. The algorithm was evaluated in Matlab/Simulink by using an 8-DOF nonlinear vehicle model. Simulations of sinusoidal input maneuver and double lane change maneuver were executed and the results were compared with those for a sliding mode control. The simulation results show that the vehicle controlled by the control allocation algorithm has better stability and trajectory-tracking performance than the vehicle controlled by the sliding mode control. The vehicle controlled by the control allocation algorithm still has good handling and stability when one or more actuators are saturated or in a failure situation.
