Analyzing the influence of automatic steering system on the trajectory tracking accuracy of intelligent vehicle
Introduction
As one of the important parts of intelligent transportation system (ITS) [1], [2], intelligent vehicle (IV) can improve road traffic efficiency based on vehicle intelligent behaviors, such as environmental perception [3], route planning [4] and vehicle motion control. Among them, the trajectory tracking motion control is a core part of IV motion control which has attracted much attention.
Earlier papers study on trajectory tracking control system of IV has verified the accuracy and robustness of IV trajectory tracking controller. Tai [5] investigated the trajectory tracking accuracy of IV under the conditions of vehicle steering and obstacle avoidance motion, and designed the trajectory tracking controller based on fuzzy control algorithm to improve the IV trajectory tracking accuracy. Hatipoglu and Ozguner [6] did research on lateral control algorithm of IV to track desired yaw velocity, and designed the controller based on robust switching control method. Netto and Chaib [7] applied the self-tuning regulator into the concerned vehicle lateral model to deal with the vehicle lateral control problem. Lee and Yoo [8] proposed a new predictive control algorithm based on tracking deviation function, and the center-of-mass velocity and slip angle were considered as controlled objects. Chen and Luan [9] used adaptive model predictive control with linear time-variant prediction model in lane keeping system, and a cost function which consists of the errors between the target trajectory and predicted trajectory was considered in the control system. M. Chadli [10–11] studied the vehicle path-following control problem considering time delay and tire force saturation, and designed composite nonlinear feedback strategy to improve the robustness of the path tracking controller. Wang [12] presents a fast and accurate robust path-following control approach for a fully actuated marine surface vessel in the presence of external disturbances. Several simulation analysis and test results [[13], [14], [15]] also indicated the effectiveness of trajectory tracking control of autonomous robots and vehicle under parametric modeling uncertainty. However, most studies simplified the IV into Car-like model or bicycle model, and did not take the automatic steering system (ASS) into consideration.
To solve the problem, some researchers focus on the coupling mechanism between ASS and IV trajectory tracking motion system. M. Chadli [16] studied the stability condition of nonlinear EPS system with constraint and saturation control. Mammar and Koenig [17] investigated the influence of vehicle speed, pavement coefficient and the steering angle on vehicle yaw-rate under phase-plane condition, and used the feedback H∞ controller for active front steering system to improve vehicle handling stability. Ghani and Sam [18] used sliding mode control strategy to overcome different road friction coefficients and various disturbances on active steering vehicle system. Cerone and Milanese [19] addressed the problem of combining automatic lane-keeping and driver's steering for either obstacle avoidance or lane-change maneuvers for passing purposes and other desired maneuvers, through a closed-loop control strategy. Guo and Hu [20] designed the automatic steering controller for trajectory tracking of unmanned vehicles using genetic algorithms, and different control algorithms were compared to verify the effectiveness of designed control algorithm.
Those studies on coupling mechanism of ASS and IV trajectory tracking system have been successfully verified in applications, and each applied control strategy can partly improve the vehicle handling stability or trajectory tracking accuracy. However, few studies have drawn attention to the nonlinear interference analysis of IV trajectory tracking system, especially analyzed the influence of nonlinear factors of ASS on the accuracy and stability of IV trajectory tracking control system. In this paper, we propose a novel vision guided IV trajectory tracking control system which includes the nonlinear ASS, and the influence of interference torques and time delay of ASS on IV trajectory tracking is analyzed. This paper is arranged as follow:
In Section 2, the architecture of vision guided IV trajectory tracking control system based on expected yaw velocity is introduced. In Section 3, the ASS and IV dynamic models are established, and the nonlinear factors of ASS are considered. In Section 4, the IV trajectory tracking control algorithm and automatic steering control algorithm are built, which include the formula of designed virtual driving path, expected yaw velocity generator, back-stepping sliding mode controller and proportional-integral and derivative (PID) controller. In Section 5, the simulations of IV trajectory tracking controller with and without ASS are compared to verify the influence of nonlinear factors of ASS on the accuracy of IV trajectory tracking control system. Conclusions are drawn in Section 6.
Section snippets
Architecture of IV trajectory tracking control system
The architecture of IV trajectory tracking control system is shown in Fig. 1.
In Fig. 1, IV trajectory tracking control system includes sensors, trajectory tracking controller, ASS and vehicle system. The trajectory tracking controller is composed of two parts: expected yaw velocity generator and back-stepping sliding mode controller. The working principle of IV trajectory tracking control system is shown as follow:
When IV trajectory tracking control system starts, the vehicle sensors output the
Combined IV and ASS dynamic models
The IV dynamic and ASS models are nonlinear and time delay which greatly increase the difficulty in designing the trajectory tracking controller. To study the influence of ASS on the trajectory tracking accuracy of IV, the IV dynamic and ASS models are established in this section.
System control design
The IV control system consists of trajectory tracking controller and automatic steering controller, as shown in Fig. 4. In this system control architecture, the trajectory tracking controller output the reference steering angle to ASS in a smooth and continuous manner based on the target trajectory which has been introduced in Section 2, and the task of automatic steering controller is to direct the ASS to follow the reference steering angle at the best possible precision and in a short time.
Simulation analysis
In this section, several simulation tests are conducted to study the influence of ASS on the IV trajectory tracking accuracy. Firstly, the simulations of IV trajectory tracking controller with and without ASS are compared. Secondly, the nonlinear factors and time delay are considered to further analyze the influence of ASS on the IV trajectory tracking accuracy. Lastly, the noise interference is considered to investigate the influence of noise on the IV trajectory tracking accuracy. It should
Different simulation programs
In this section, the CarSim program is adopted to further testify the effectiveness of the proposed study based on the program characteristics. This Simulation assumes that IV run on DLC road, the vehicle velocity is set to 36 km/h, the preview distance is 23 m, and the contrast simulation results between MATLAB and CarSim are shown in Fig. 17. Besides, T1 and T2 in Fig. 17 refer to the Matlab/Simulink and Carsim and MATLAB joint simulation method.
Fig. 17 shows clearly that the simulation
Conclusion
This paper studies the influence of nonlinear factors of ASS on trajectory tracking accuracy of IV. A novel vision guided IV trajectory tracking control system based on expected yaw velocity is proposed at first, and the nonlinear ASS is considered in the architecture of IV trajectory tracking control system. The simulations of IV trajectory tracking controller with and without ASS are compared, the interference torques, time delay and noise are also considered to further analyze the influence
Acknowledgment
This work is supported by the program of the National Natural Science Foundation of China (Grant No. U1564201, Grant No. 51575240), National Natural Science Foundation ofJiangsu Province China (Grant No. JA460005), Natural Science Foundation of Colleges and Universities in Jiangsu Province China (16KJB580012), and the key project plan of Zhenjiang city(Grant No. GY2015029).
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