Driverless Bus Path Tracking Based on Fuzzy Pure Pursuit Control with a Front Axle Reference

Yang

Proceedings of the Institution of Mechanical Engineers, Part I:

et al. 2021

To realize the running control of distributed-drive and active-steering articulated virtual rail trains travelling on urban roads under non-contact virtual rail constraints, target trajectory generation and active-steering control are crucial issues. In this article, a novel tracking control method is proposed, which includes a dynamic target trajectory generation and a new active-steering tracking control system. First, a distributed-drive and active-steering articulated virtual rail train kinematics model with n-sections is derived, and then a new target trajectory generation method is proposed using data filtering and compression, coordinate transformation and spline difference, and the simulation comparison shows that the proposed method has less data storage space and high computational efficiency. Second, a new active-steering tracking control system composed of a rear axle preview active-steering controller, a front axle coordinated steering controller, and a differential-distribution controller is designed to achieve tracking control and coordinated movement of distributed-drive and active-steering articulated virtual rail train. Finally, a distributed-drive and active-steering articulated virtual rail train simulation model was constructed in ADAMS, and then simulations are performed under three rail conditions and compared with the other two methods, which show that the proposed method has good tracking control accuracy, adaptability, and superiority under various rails and different speeds.

Section: Control Point Coordinate Calculationmentioning

confidence: 99%

A novel tracking control method for the distributed-drive and active-steering articulated virtual rail train

Yang

Proceedings of the Institution of Mechanical Engineers, Part I:

et al. 2021

“…To meet these requirements, Chen et al (2021) of the University of California, Berkeley proposed the nearest strategy optimization algorithm combined with a PP tracking method to construct a vehicle controller. Yu et al (2020) proposed an improved PP model based on fuzzy control, which improved the stability of the PP model. Shan et al (2015) propose to use a cyclotron curve instead of a circle to improve the PP tracking control, which can improve the degree of curve fitting.…”

Section: Introductionmentioning

confidence: 99%

An improved pure pursuit path tracking control method based on heading error rate

Wang

Chen

Wang

2022

Purpose In path tracking, pure pursuit (PP) has great superiority due to its simple control. However, when in agricultural applications, the performance and accuracy of PP are not so well; it cannot be tracked in time has slow convergence, and low tracking accuracy. Furthermore, in some severe driving scenarios, PP is insufficient to convey the effects of the tracking error. This paper aims to propose an autonomous driving controller to improve the PP model based on heading error rate (Improved PP-improved search strategy ant colony optimization [ISSACO]). Design/methodology/approach First, the heading error rate is added as the control method in the PP model. Second, the predicted heading error was selected as the objective function; the ISSACO is used to obtain the minimum value of the predicted heading error. A PP controller is integrated with the heading error rate by ISSACO to better deal with tracking error by trading off between PP and heading error rate. Third, the ISSACO was used to obtain the optimal values of PP and heading error rate weight. Finally, the error feedback adaptive dynamic adjustment of the improved algorithm is realized to reduce the convergence time and tracking error. Findings The proposed method was tested on a four-wheeled vehicle robot, and the effectiveness of its convergence was proved. Experiments show that the proposed method can effectively reduce the tracking error, increase convergence, then improve the robot’s working quality. Originality/value An adaptive improved PP path tracking control is proposed, which considers both heading error rate and parameter uncertainties. The new autonomous controller has a simple structure and is easy to implement. It can be adjusted according to the path tracking status to improve the adaptability of the system.

Mathematical Problems in Engineering

“…Xu and Peng presented an optimal control algorithm for the path tracking of automated vehicles [30]. To solve the problem of reducing tracking errors, Yu et al proposed a path tracking controller based on the Fuzzy Pure Pursuit Control with a Front Axle Reference method [31].…”

Section: Introductionmentioning

confidence: 99%

Optimum Path Tracking Control for the Inverse Problem of Vehicle Handling Dynamics Based on the hp-Adaptive Gaussian Pseudospectral Method

Liu

Sun

Cui

2021

We propose a vehicle path-tracking method based on the hp-adaptive Gaussian pseudospectral method (GPM), which tackles the problem of the slow convergence speed of the optimal control of vehicle path tracking. First, we establish a kinematic vehicle model by considering the path constraints and boundary constraints during the process of tracking the described path of a vehicle. Subsequently, finding the minimum error of the lateral distance between the prescribed path and the expected trajectory is set as the performance objective function. Finally, the vehicle path tracking problem is transformed into an optimal control problem. The optimization algorithm is combined with the sequential quadratic programming algorithm to optimize problems related to the control and state variables and the boundary and path constraints. The simulated results show that the hp-adaptive GPM can improve the convergence rate of the optimal control problem for vehicle path tracking. The proposed hp-adaptive pseudospectral method has a higher solving efficiency compared with traditional approaches for the vehicle path-tracking problem. Concurrently, the verification results of a real vehicle test indicate the feasibility of the proposed algorithm for solving the vehicle path tracking problem. This research provides valuable insight into the design work of lane changes and is an important step towards the design of feedback control laws for path tracking.