Fuzzy logic-based Steering Controller for an Autonomous Head -Feed Combine Harvester

Jeon, Chan Woo; Kim, Hak-Jin; Han, Xiong; Kim, Jung Hun

doi:10.13031/aim.201700927

Cited by 6 publications

(6 citation statements)

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“…Joint Eqs. (6) and (12) can obtain the complete dynamics equation, and the writing matrix form is as follows:…”

Section: Dynamic Modelmentioning

confidence: 99%

“…Chen et al [10] and Li et al [11] use adaptive sliding mode control theory to control wheeled vehicles. Other scholars also have used other methods, such as fuzzy logic-based controllers [12], adaptive neural-fuzzy inference controllers [13] and slip-compensating control strategies [14]. In the methods commonly used for trajectory tracking control, PID control has poor control effect on nonlinear systems and structurally uncertain systems due to fixed control parameters.…”

Section: Introductionmentioning

confidence: 99%

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Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control

Zhou¹,

Wang²,

Sun³

et al. 2019

SV-JME

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This paper proposes a model predictive control (MPC) algorithm for trajectory tracking of vehicles. Using MPC can reduce tracking errors and random disturbances in complex environments in time. According to the linear kinematics model of the vehicle, a kinematics trajectory tracking controller and an electromechanical coupling dynamics trajectory tracking controller are designed. The drive system of the electrically driven tracked vehicle is non-linear, and an electromagnetic system and mechanical system interact with each other. Taking the electromechanical coupling characteristics into consideration can ensure the matching of the electromechanical performance and the stability of the system during the trajectory tracking control. To verify the algorithm, kinematic simulations and dynamic simulations are performed. The simulation results show that the algorithm has good tracking ability. In addition, a set of test devices is designed to confirm the performance of the trajectory-tracking control algorithm in a real environment. Vision recognition is used to obtain vehicle deviation, and the Kalman filter is used to reduce signal interference. The result shows that the algorithm can meet trajectory tracking requirements.

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“…Joint Eqs. (6) and (12) can obtain the complete dynamics equation, and the writing matrix form is as follows:…”

Section: Dynamic Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control

Zhou¹,

Wang²,

Sun³

et al. 2019

SV-JME

View full text Add to dashboard Cite

show abstract

“…Chen et al (2009) and Wai and Lin (2013) applied adaptive SMC theory on wheeled vehicles. Other approaches such as fuzzy logic-based controllers (Jeon et al, 2017), adaptive neuro-fuzzy inference controllers (Dai et al, 2018), and sliding compensation control strategies (Endo et al, 2007) can also be found in the research field of path tracking.…”

Section: Introductionmentioning

confidence: 99%

Research on unmanned electric shovel autonomous driving path tracking control based on improved pure tracking and fuzzy control

Wang

et al. 2023

Journal of Field Robotics

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This paper proposes a path tracking control method combining pure tracking algorithms and self‐adaptive fuzzy control for autonomous driving of an unmanned electric shovel. An improved pure tracking controller was designed based on the kinematic model of heavy crawler taking both the value of deviation and its variation as inputs with the crawler speed on each side as output. The proposed controller and MPC algorithm were simulated using MATLAB for comparison. The results show that the proposed controller has more anthropomorphic characteristics than the MPC method. To verify the actual control effect of the controller, experiments were carried out using a prototype electric shovel for different working conditions. The experimental results proved that the controller is able to meet the control requirements for unmanned electric shovel path tracking.

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“…Like the PID controller, it does not require a model and is a kind of model-free control method. Many researchers have designed fuzzy controllers for tractors [13][14][15][16][17][18][19][20][21][22][23]. Z. Liu et al [13] first obtained the position and attitude of a tractor based on machine vision, then designed a fuzzy controller with lateral and heading deviation as inputs and a desired steering angle as output, and finally used a PID controller to track the desired angle to realize motion control of the tractor.…”

Section: Introduction 1path Tracking Control For a Tractormentioning

confidence: 99%

Path Tracking Control of a Tractor on a Sloping Road with Steering Compensation

Ou,

Fu,

Tang

et al. 2023

Agriculture

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Agricultural tractors are subject to lateral forces when traveling on slopes, making it difficult to accurately follow a set course. In this paper, a steering compensation method is first proposed based on the force analysis of a tractor traveling on slopes, which compensates the steering angle according to the friction force and gravity force imposed on the tractor. Further, when traveling on slopes, acceleration and a load applied to the tractor are usually time-varying. To address this problem, this paper proposes a steering compensator that can automatically adjust a compensation coefficient, as well as a design for a model predictive controller with the steering compensator for a tractor. Simulation results show that under different traveling speeds, turning radius, and slope angles, the steering compensator allows the tractor to travel more smoothly, i.e., the distance between the actual traveling route and the reference route fluctuates within a smaller range. Further, during straight-line traveling, the static error can be effectively reduced, i.e., the distance between the actual traveling route and the reference route is closer to zero. Overall, the steering compensator enables the tractor to track the reference route more accurately.

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Fuzzy logic-based Steering Controller for an Autonomous Head -Feed Combine Harvester

Cited by 6 publications

References 0 publications

Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control

Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control

Research on unmanned electric shovel autonomous driving path tracking control based on improved pure tracking and fuzzy control

Path Tracking Control of a Tractor on a Sloping Road with Steering Compensation

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