To develop the pressure control algorithm for active braking of adaptive cruise control（ACC） system,a test bench with real parts of the tested vehicle is built.With the dynamic analysis of the active braking actuato...To develop the pressure control algorithm for active braking of adaptive cruise control（ACC） system,a test bench with real parts of the tested vehicle is built.With the dynamic analysis of the active braking actuators,it is demonstrated that different duty of pulse-width modulation（PWM） signals could control the pressure changing rate of the wheel cylinder.To obtain that signal,a modified proportional-integral-differential（PID） control algorithm is developed using the variable parameter method,the control value reset method,the dead zone method and the integral saturation method.Experimental results show that the delay and overshoot of the pressure response could be reduced considerably using the modified PID algorithm compared with the conventional one.The proposed pressure control algorithm could be used for the further development of the ACC＇s controller.展开更多
In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucia...In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucial for the vehicle design and safe control.However,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy.In this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified units.These techniques are utilized to obtain more accurate model for brake pressure state estimation applications.The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing.The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles.Based on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated accordingly.The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.展开更多
基金Supported by the Ministerial Level Advanced Research Foundation(40401040302)
文摘To develop the pressure control algorithm for active braking of adaptive cruise control（ACC） system,a test bench with real parts of the tested vehicle is built.With the dynamic analysis of the active braking actuators,it is demonstrated that different duty of pulse-width modulation（PWM） signals could control the pressure changing rate of the wheel cylinder.To obtain that signal,a modified proportional-integral-differential（PID） control algorithm is developed using the variable parameter method,the control value reset method,the dead zone method and the integral saturation method.Experimental results show that the delay and overshoot of the pressure response could be reduced considerably using the modified PID algorithm compared with the conventional one.The proposed pressure control algorithm could be used for the further development of the ACC＇s controller.
文摘In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucial for the vehicle design and safe control.However,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy.In this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified units.These techniques are utilized to obtain more accurate model for brake pressure state estimation applications.The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing.The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles.Based on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated accordingly.The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.
