Precise Compound Control of Loading Force for Electric Load Simulator of Electric Power Steering Test Bench

Electric load simulator(ELS) systems are employed for electric power steering(EPS) test benches to load rack force by precise control. Precise ELS control is strongly influenced by nonlinear factors. When the steering motor rapidly rotates, extra force is directly superimposed on the original static loading error, which becomes one of the main sources of the final error. It is key to achieve ELS precise loading control for the entire EPS test bench. Therefore, a three-part compound control algorithm is proposed to improve the loading accuracy. First, a fuzzy proportional–integral plus feedforward controller with force feedback is presented. Second, a friction compensation algorithm is established to reduce the influence of friction. Then, the relationships between each quantity and the extra force are analyzed when the steering motor rapidly rotates, and a net torque feedforward compensation algorithm is proposed to eliminate the extra force. The compound control algorithm was verified through simulations and experiments. The results show that the tracking performance of the compound control algorithm satisfies the demands of engineering practice, and the extra force in the ELS system can be suppressed by the net torque corresponding to the actuator’s acceleration.

Adaptive Fuzzy Torque Control of Passive Torque Servo Systems Based on Small Gain Theorem and Input-to-state Stability

Passive torque servo system （PTSS） simulates aerodynamic load and exerts the load on actuation system, but PTSS endures position coupling disturbance from active motion of actuation system, and this inherent disturbance is called extra torque. The most important issue for PTSS controller design is how to eliminate the influence of extra torque. Using backstepping technique, adaptive fuzzy torque control （AFTC） algorithm is proposed for PTSS in this paper, which reflects the essential characteristics of PTSS and guarantees transient tracking performance as well as final tracking accuracy. Takagi-Sugeno （T-S） fuzzy logic system is utilized to compensate parametric uncertainties and unstructured uncertainties. The output velocity of actuator identified model is introduced into AFTC aiming to eliminate extra torque. The closed-loop stability is studied using small gain theorem and the control system is proved to be semiglobally uniformly ultimately bounded. The proposed AFTC algorithm is applied to an electric load simulator （ELS）, and the comparative experimental results indicate that AFTC controller is effective for PTSS.