Path Tracking Control of an Autonomous Tractor Using Improved Stanley Controller Optimized with Multiple-Population Genetic Algorithm

Wang, Liang; Zhai, Zhiqiang; Zhu, Zhen; Mao, Enrong

doi:10.3390/act11010022

Cited by 18 publications

(7 citation statements)

References 20 publications

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“…Considering that GA is an adaptive heuristic search technique inspired by the evolutionary ideas of natural selection and genetics [34,35], this section constructs a new control method that combines MPC and GA. Compared with traditional MPC, it is no need to convert the nonlinear model to a linear format or implement the solver of convex quadratic programming.…”

Section: Control Methods Based On Mpc-gamentioning

confidence: 99%

Path Tracking Method Based on Model Predictive Control and Genetic Algorithm for Autonomous Vehicle

Wang¹,

Chen²,

Yang

et al. 2022

Mathematical Problems in Engineering

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Model predictive control (MPC) is often used for controlling the autonomous vehicle tracking the target path. But to apply MPC schemes, the nonlinear model of vehicle kinematics needs to be approximately converted to a linear format, and the path tracking problem has to be converted into certain formats in order to implement the solver of convex quadratic programming. To solve these issues, a control strategy combining MPC and genetic algorithm (GA) is put forward. The nonlinear predictive model is adopted to predict the future movement of a controlled vehicle. The objective function is established according to the future movement and target path. Instead of using a convex quadratic programming solver, GA is applied to solve the optimization problem. The proposed MPC-GA method can handle the arbitrary nonlinear problem and make the objective function more comprehensible and flexible. This method is applied in solving the path tracking problem of an autonomous vehicle. Both simulations and on-field tests are conducted. The results validate the efficiency of the proposed MPC-GA path tracking method in comparison with traditional methods. With the MPC-GA controller, the automatic driving on the park road is basically realized. The control strategy can be considered as an alternative method to solve the path tracking problem for an autonomous vehicle.

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Section: Control Methods Based On Mpc-gamentioning

confidence: 99%

Path Tracking Method Based on Model Predictive Control and Genetic Algorithm for Autonomous Vehicle

Wang¹,

Chen²,

Yang

et al. 2022

Mathematical Problems in Engineering

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“…The most prevalent path-tracking algorithms are pure pursuit [28], Stanley [29], Least Quadratic Regulator (LQR) [30], and Model Predictive Control (MPC) [31][32][33].…”

Section: Related Workmentioning

confidence: 99%

Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land

et al. 2022

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Navigating the AGV over the curve path is a difficult problem in all types of navigation (landmark, behavior, vision, and GPS). A single path tracking algorithm is required to navigate the AGV in a mixed environment that includes indoor, on-road, and agricultural terrain. In this paper, two types of proposed methods are presented. First, the curvature information from the generated trajectory (path) data is extracted. Second, the improved curve-aware MPC (C-MPC) algorithm navigates AGV in a mixed environment. The results of the real-time experiments demonstrated that the proposed curve finding algorithm successfully extracted curves from all types of terrain (indoor, on-road, and agricultural-land) path data with low type 1 (percentage of the unidentified curve) and type 2 (extra waypoints added to identified curve) errors, and eliminated path noise (hand-drawn line error over map). The AGV was navigated using C-MPC, and the real-time and simulation results reveal that the proposed path tracking technique for the mixed environment (indoor, on-road, agricultural-land, and agricultural-land with slippery error) successfully navigated the AGV and had a lower RMSE lateral and longitudinal error than the existing path tracking algorithm.

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“…The mutation probability and the crossover probability determine the system's global and local search capabilities. When they are larger, the recombined individuals will have a high probability of appearing and converge quickly; but at the same time, the replacement of the old and the new is too fast, making some superior individuals may be eliminated prematurely [38][39][40].…”

Section: Determine the Fitness Functionmentioning

confidence: 99%

Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System

Qin

Zhang²,

Zhao³

et al. 2022

Processes

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This paper proposes a dynamic high-type control (DHTC) method based on an interval type-2 fuzzy logic controller (IT2FLC), which is used in the photoelectric tracking system to improve the steady-state accuracy and response speed. Adding integrators to the traditional multi-loop feedback control loop can increase the system type, thereby speeding up the response speed and improving the steady-state accuracy, but there is a risk of integral saturation. Switching the type dynamically according to the system state can avoid integral saturation while retaining the advantages of the high-type. Fuzzy logic control (FLC) can dynamically change the output value according to the input change and has the advantages of fast response speed and strong ability to handle uncertainties. Therefore, in this paper, the FLC is introduced into the high-type control system, and the output of the FLC is used as the gain of the integrator to control the on-off to achieve the goal of dynamic switching type, which is successfully verified in the experiment. IT2FLC introduces a three-dimensional membership function, which further improves the FLC’s ability to handle uncertainties. From the experimental results, compared with T1FLC, IT2FLC’s ability to handle uncertainties is significantly improved. In addition, in order to speed up the calculation speed of IT2FLC, this paper proposes an improved type-reduction algorithm, which is called weighted-trapezoidal Nie-Tan (WTNT). Compared with the traditional type-reduction algorithm, WTNT has faster calculation speed and better steady-state accuracy, and has been successfully applied to real-time control systems, which has good engineering application value. Finally, in order to reduce the interference of human factors and improve the automation level of the system, a multi-population genetic algorithm (MPGA) is used to iteratively optimize the parameters of the FLC, which improves the output accuracy. On the experimental platform of the flexible fast steering mirror (FFSM), the control effects of the traditional controller, T1FLC and IT2FLC are compared, which proves that the IT2FLC-DHTC system has a faster response performance, higher steady-state accuracy, and stronger ability to handle uncertainties.

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Path Tracking Control of an Autonomous Tractor Using Improved Stanley Controller Optimized with Multiple-Population Genetic Algorithm

Cited by 18 publications

References 20 publications

Path Tracking Method Based on Model Predictive Control and Genetic Algorithm for Autonomous Vehicle

Path Tracking Method Based on Model Predictive Control and Genetic Algorithm for Autonomous Vehicle

Curve-Aware Model Predictive Control (C-MPC) Trajectory Tracking for Automated Guided Vehicle (AGV) over On-Road, In-Door, and Agricultural-Land

Dynamic High-Type Interval Type-2 Fuzzy Logic Control for Photoelectric Tracking System

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