Adaptive modified Stanley controller with fuzzy supervisory system for trajectory tracking of an autonomous armoured vehicle

Amer, Noor Hafizah; Hudha, Khisbullah; Zamzuri, Hairi; Aparow, Vimal Rau; Abidin, Amar Faiz Zainal; Kadir, Zulkiffli Abdul; Murrad, Muhamad

doi:10.1016/j.robot.2018.03.006

Cited by 56 publications

(36 citation statements)

References 35 publications

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“…Reference [13] studied the adaptive tire slip rate control method. In order to satisfy the adaptive speed and heading angle deviation of the Stanley controller, the expert library is established based on particle swarm optimization algorithm, and a adaptive parameter mechanism of the stanley controller based on fuzzy supervisory system is studied [14][15].…”

Section: A Pure Prusuit and Stanley Control Methodsmentioning

confidence: 99%

“…Based on the vehicle kinematics information, using the preview distance heading angle deviation and the vehicle position deviation as the controller design basis, the vehicle path tracking control methods are studied [8][9][10][11]. To further improve the control precision, the adaptive preview distance control strategy to achieve vehicle motion control under different speeds and road curvature conditions are studied [13][14][15]. This type of controllers have a simple layout and are suitable for controlling the position of the vehicle.…”

Section: A Pure Prusuit and Stanley Control Methodsmentioning

confidence: 99%

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Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao

Tian

Wang³

et al. 2020

IEEE Access

104

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Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This paper introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.

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Section: A Pure Prusuit and Stanley Control Methodsmentioning

confidence: 99%

Section: A Pure Prusuit and Stanley Control Methodsmentioning

confidence: 99%

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao

Tian

Wang³

et al. 2020

IEEE Access

104

View full text Add to dashboard Cite

show abstract

“…As mentioned by Naranjo et al [47], the main reason for using the fuzzy approach is that a suitable driving process model is essential for automatic steering wheel control, and, nevertheless, classical approaches frequently fail to yield appropriate models of complex (nonlinear, time-varying, ill-defined) processes-and driving a car certainly falls into this category-whereas fuzzy-logic-based control methods provide an alternative tool for dealing with car and subsystem complexity. However, if the constructions of the rules and membership functions are very complicated, the empirical approach is not only time consuming and labor intensive, but also cannot ensure that the optimum rules and the membership functions are found [48,49]. In order to achieve a model that can tolerate imprecision and has controller parameters that are easy to set at the same time, model predictive control (MPC) [50,51] is one of promising options.…”

Section: Related Workmentioning

confidence: 99%

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

Zeng

Liu

et al. 2020

Sensors

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How to make a controller robust and stable to reject the disturbance of uncertainty is an inevitable challenge. Aiming at addressing the lateral control problem for an autonomous road sweeper, a heading-error-based first order linear active disturbance rejective controller (HFO-LADRC) is proposed in this paper. To eliminate the lateral error and the heading error at the same time, a new model, called the heading-error-based model, is proposed for lateral motion, and the Lyapunov function was employed to explore the convergence ability of the heading error and lateral error. Since the heading-error-based model is first order, the ADRC is designed as first order and linear, and each module of the HFO-LADRC has been devised in detail. To ensure solution accuracy, the fourth order Runge–Kutta method was adopted as the differential system solver, and a typical ring scenario and a double lane-changing scenario were designed referencing the standard. Considering the obvious influence, wheelbase uncertainty, steering ratio uncertainty and Gaussian white noise disturbance were taken into account for the tests. The results illustrate that, in the case of both wheelbase uncertainty and steer ratio uncertainty, the HFO-LADRC has strong robustness and stability compared with a typical pure pursuit controller and classical SO-LADRC.

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“…By developing three fuzzy controllers, the dynamic errors in the process of obstacle avoidance can be reduced, and the control effect was pretty good. Amer et al [20] proposed an adaptive controller for trajectory tracking control of autonomous armored vehicles. Based on the establishment of the knowledge base, the controller realized effective control of different trajectories and speeds through a fuzzy monitoring system.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Control Algorithm for Autonomous Vehicle Considering Cornering Characteristics

et al. 2020

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Trajectory tracking control is a key technology in the research and development of autonomous vehicles. With the aim of addressing problems such as low control accuracy and poor real-time performance, which can occur easily when an autonomous vehicle avoids obstacles, this research focuses on the trajectory tracking control algorithm for autonomous vehicle considering cornering characteristics. First, the vehicle dynamics model and tire model are established through appropriate simplification. Then, based on the basic principle of model predictive control, a linear time-varying model predictive controller (LTV MPC) that considers the cornering characteristics is designed and optimized. Finally, using CarSim and MATLAB/Simulink software, a joint simulation model is established and the trajectory tracking performance of the controlled vehicle under different vehicle speeds and road adhesion conditions are tested through simulation experiments in combination with the double-shift line reference trajectory. The simulation results show the LTV MPC controller that considers cornering characteristics has good self-adaptability under complicated and severe working conditions, and no cases, such as car sideslip or track departure, were observed. Compared with other controllers and algorithms, the designed trajectory tracking controller has remarkable comprehensive performance, exhibits superior robustness and anti-interference ability, and significant improvements in the trajectory tracking control accuracy and real-time performance. The proposed control algorithm is of great importance in improving the tracking stability and driving safety of autonomous vehicles under complex extreme conditions and conducive to the further development and improvement of the technological level of intelligent vehicle driving assistance. INDEX TERMS Driving assistance, autonomous vehicle, model predictive control, cornering characteristics, trajectory tracking control.

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Adaptive modified Stanley controller with fuzzy supervisory system for trajectory tracking of an autonomous armoured vehicle

Cited by 56 publications

References 35 publications

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper

Trajectory Tracking Control Algorithm for Autonomous Vehicle Considering Cornering Characteristics

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