Combined throttle and brake control for vehicle cruise control: A model free approach

This paper has been focused on the design of throttle and brake controller for vehicle speed control. The control goals of the speed control are reduction of the effects of model uncertainty and external disturbance caused by the nonlinearity of the servo-level dynamics and the unpredictable driving resistance such as inclination of road and aero dynamic drag force. The tracking performance also should be guaranteed. To that end, a model free approach has been proposed in this paper. The proposed controller uses modified linear longitudinal vehicle model with reasonable assumptions to derive throttle and brake control law and a few parameters in the vehicle model has been defined to represent the system delay and variation of the vehicle parameters during driving. Since the defined parameters named vehicle characteristic variables(VCVs) vary depending on the vehicle states, an adaptation algorithm has been developed to estimate the VCVs. The error dynamics of the vehicle acceleration and VCVs have been analyzed to prove the stability of the proposed algorithm. The tracking performance of the model free cruise controller has been verified by simulation. The results not only show good tracking performance but also verify that the MFCC considerably reduces the effects of model uncertainty and external disturbance using adaptation algorithm.

[1]  José Eugenio Naranjo,et al.  ACC+Stop&go maneuvers with throttle and brake fuzzy control , 2006, IEEE Transactions on Intelligent Transportation Systems.

[2]  Huei Peng,et al.  Optimal Adaptive Cruise Control with Guaranteed String Stability , 1999 .

[3]  Seungwuk Moon,et al.  Design, tuning, and evaluation of a full-range adaptive cruise control system with collision avoidance , 2009 .

[4]  Wei Ren,et al.  Vehicle longitudinal control using throttles and brakes , 1999, Robotics Auton. Syst..

[5]  R. Ervin,et al.  Human-Centered Design of an Acc-With-Braking and Forward-Crash-Warning System , 2001 .

[6]  C. Canudas-de-Wit,et al.  Model reference control approach for safe longitudinal control , 2004, Proceedings of the 2004 American Control Conference.

[7]  Petros A. Ioannou,et al.  Autonomous intelligent cruise control , 1993 .

[8]  J. K. Hedrick,et al.  Vehicle Speed and Spacing Control Via Coordinated Throttle and Brake Actuation , 1996 .

[9]  Brigitte d'Andréa-Novel,et al.  Model-free control of automotive engine and brake for Stop-and-Go scenarios , 2009, 2009 European Control Conference (ECC).

[10]  Wei Ren,et al.  Autonomous Intelligent Cruise Control with Actuator Delays , 1998, J. Intell. Robotic Syst..

[11]  M. Athans,et al.  On the optimal error regulation of a string of moving vehicles , 1966 .

[12]  Michael A. Goodrich,et al.  Model-based human-centered task automation: a case study in ACC system design , 2003, IEEE Trans. Syst. Man Cybern. Part A.

[13]  Rajesh Rajamani,et al.  Should adaptive cruise-control systems be designed to maintain a constant time gap between vehicles? , 2001, IEEE Transactions on Vehicular Technology.

[14]  Jing Zhou,et al.  Range policy of adaptive cruise control vehicles for improved flow stability and string stability , 2005, IEEE Transactions on Intelligent Transportation Systems.

[15]  Payman Shakouri,et al.  Adaptive Cruise Control System: Comparing Gain-Scheduling PI and LQ Controllers , 2011 .

[16]  Kyongsu Yi,et al.  Human driving data-based design of a vehicle adaptive cruise control algorithm , 2008 .

[17]  Vicente Milanés Montero,et al.  Model-free control techniques for Stop & Go systems , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.