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Path tracking plays a critical role in autonomous driving for autonomous ground vehicle (AGV). However, AGV faces challenges in accurate tracking and chatter reduction due to external disturbances, making it difficult to meet the tracking performance requirements. Currently, sliding mode control (SMC) and disturbances observer are primarily employed for disturbance estimation. However, ensuring finite‐time robust control remains a significant challenge. To ensure rapid convergence of tracking errors and effective disturbance rejection, this paper proposed a novel non‐singular fast terminal sliding mode (NFTSM) control scheme based on finite‐time disturbance observation (FDO). First, a novel NFTSM controller based on AGV dynamic model is developed to achieve fast convergence of tracking errors. Then, to mitigate disturbances effects and suppress chatter, an innovative FDO method is employed. Finally, based on FDO, the NFTSM‐FDO establishes a control scheme that enhances disturbances suppression and accelerates convergence. The simulation and experimental results demonstrate the innovation of the proposed method. Compared with other SMC methods, the results validate the effectiveness and advantages of the proposed approach, exhibiting fast convergence and superior tracking performance. [ABSTRACT FROM AUTHOR]

Pure pursuit tracking algorithms are a popular control method in the field of autonomous navigation, where the selection of a look-ahead point plays a crucial role in tracking performance. However, the computation of the look-ahead point involves issues that are challenging to describe precisely using mathematics. To enhance the tracking precision of vehicles on curved trajectories, we propose an improved optimal look-ahead point path tracking algorithm. This algorithm primarily seeks the optimal look-ahead point by considering both longitudinal look-ahead distance and lateral position offset. To begin, we employ the Deep Deterministic Policy Gradient (DDPG) algorithm to train vehicles to determine the optimal longitudinal look-ahead distance under various constant curvature and velocity conditions. Subsequently, by utilizing the optimal longitudinal look-ahead distance and the front-wheel steering angle, we construct a lateral deviation search region. Finally, we use an evaluation function to search for the optimal look-ahead point within this region. Simulation tests demonstrate that the proposed algorithm significantly improves tracking accuracy under varying curvature trajectory conditions. [ABSTRACT FROM AUTHOR]

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Aiming at the highly nonlinear and coupled longitudinal and transverse motion control problems of intelligent vehicle path tracking control, a new method of preview MPC path tracking control is proposed by combining visual preview model and model predictive control theory. The method first locally linearizes the nonlinear prediction model and transforms the MPC optimization solution problem into a quadratic programming problem. The longitudinal vehicle speed and the preview distance are then treated as known in each control time domain, an exponential model is applied to characterize the longitudinal vehicle speed, and a transverse angular velocity-center of mass lateral deflection ( r - β ) phase plane plot is applied to characterize the vehicle stability. Finally, proportional integral differential stability controller is designed to improve vehicle stability. Simulation and test results show that: Compared with the MPC tracking control method, the preview MPC control method proposed in this paper can optimize the vehicle tracking performance on different adhesion coefficient road surfaces and improve the tracking accuracy, and this enhancement effect is more obvious at high speeds. [ABSTRACT FROM AUTHOR]

The traditional Model Predictive Control (MPC) algorithm, grounded in precise mathematical models, faces challenges attributed to uncertainties in vehicle model parameters and modeling errors. These limitations result in suboptimal accuracy of the path tracking controller, particularly in complex driving environments. To address this issue, a tube-based model predictive control strategy (Tube MPC) is proposed. Initially, nominal dynamic modeling of intelligent vehicles is conducted, followed by linearized derivations. Leveraging the resulting linearized nominal model, the MPC cost function is solved to derive the control law for the nominal system. Subsequently, an error model between the actual and nominal systems is established, integrating various target constraints for path tracking and formulating a robust objective function. Utilizing the linear matrix inequality optimization method, a state feedback-assisted control law is derived to mitigate steady-state lateral position error. This approach ensures a more precise alignment between the actual and nominal states, ultimately enhancing tracking accuracy. Finally, experimental validations are conducted in the Carsim/Simulink simulation environment, encompassing diverse vehicle speed conditions and driving scenarios to assess the proposed controller against traditional Model Predictive Control. The experimental results demonstrate that, while ensuring vehicle stability, this control method exhibits superior tracking accuracy. [ABSTRACT FROM AUTHOR]

This paper introduces an enhanced APF method to address challenges in automatic lane changing and collision avoidance for autonomous vehicles, targeting issues of infeasible target points, local optimization, inadequate safety margins, and instability when using DLQR. By integrating a distance adjustment factor, this research aims to rectify traditional APF limitations. A safety distance model and a sub-target virtual potential field are established to facilitate collision-free path generation for autonomous vehicles. A path tracking system is designed, combining feed-forward control with DLQR. Linearization and discretization of the vehicle's dynamic state space model, with constraint variables set to minimize control-command costs, aligns with DLQR objectives. The aim is precise steering angle determination for path tracking, negating lateral errors due to external disturbances. A Simulink–CarSim co-simulation platform is utilized for obstacle and speed scenarios, validating the autonomous vehicle's dynamic hazard avoidance, lane changing, and overtaking capabilities. The refined APF method enhances path safety, smoothness, and stability. Experimental data across three speeds reveal reasonable steering angle and lateral deflection angle variations. The controller ensures stable reference path tracking at 40, 50, and 60 km/h around various obstacles, verifying the controller's effectiveness and driving stability. Comparative analysis of visual trajectories pre-optimization and post-optimization highlights improvements. Vehicle roll and sideslip angle peaks, roll-angle fluctuation, and front/rear wheel steering vertical support forces are compared with traditional LQR, validating the optimized controller's enhancement of vehicle performance. Simulation results using MATLAB/Simulink and CarSim demonstrate that the optimized controller reduces steering angles by 5 to 10°, decreases sideslip angles by 3 to 5°, and increases vertical support forces from 1000 to 1450 N, showcasing our algorithm's superior obstacle avoidance and lane-changing capabilities under dynamic conditions. [ABSTRACT FROM AUTHOR]

This paper introduces an adaptive path-tracking control algorithm for autonomous mobility based on recursive least squares (RLS) with external conditions and covariance self-tuning. The advancement and commercialization of autonomous driving necessitate universal path-tracking control technologies. In this study, we propose a path-tracking control algorithm that does not rely on vehicle parameters and leverages RLS with self-tuning mechanisms for external conditions and covariance. We designed an integrated error for effective path tracking that combines the lateral preview distance and yaw angle errors. The controller design employs a first-order derivative error dynamics model with the coefficients of the error dynamics estimated through the RLS using a forgetting factor. To ensure stability, we applied the Lyapunov direct method with injection terms and finite convergence conditions. Each regression process incorporates external conditions, and the self-tuning of the injection terms utilizes residuals. The performance of the proposed control algorithm was evaluated using MATLAB®/Simulink® and CarMaker under various path-tracking scenarios. The evaluation demonstrated that the algorithm effectively controlled the front steering angle for autonomous path tracking without vehicle-specific parameters. This controller is expected to provide a versatile and robust path-tracking solution in diverse autonomous driving applications. [ABSTRACT FROM AUTHOR]

We propose a compound control framework to improve the path tracking accuracy of a four-wheel independent steering and driving (4WISD) vehicle in complex environments. The framework consists of a deep reinforcement learning (DRL)-based auxiliary controller and a dual-layer controller. Samples in the 4WISD vehicle control framework have the issues of skewness and sparsity, which makes it difficult for the DRL to converge. We propose a group intelligent experience replay (GER) mechanism that non-dominantly sorts the samples in the experience buffer, which facilitates within-group and between-group collaboration to achieve a balance between exploration and exploitation. To address the generalization problem in the complex nonlinear dynamics of 4WISD vehicles, we propose an actor-critic architecture based on the method of two-stream information bottleneck (TIB). The TIB method is used to remove redundant information and extract high-dimensional features from the samples, thereby reducing generalization errors. To alleviate the overfitting of DRL to known data caused by IB, the reverse information bottleneck (RIB) alters the optimization objective of IB, preserving the discriminative features that are highly correlated with actions and improving the generalization ability of DRL. The proposed method significantly improves the convergence and generalization capabilities of DRL, while effectively enhancing the path tracking accuracy of 4WISD vehicles in high-speed, large-curvature, and complex environments. [ABSTRACT FROM AUTHOR]

The following article presents a high-level overview of how Model Predictive Control (MPC) is leveraged in passenger vehicles and their subsystems for improved performance. This overview presents the fundamental concepts of MPC algorithms and their common variants. After building some understanding of MPC methods, the paper discusses state-of-the-art examples of how MPC methods are leveraged to perform low- to high-level tasks within a typical passenger vehicle. This review also aims to provide the reader with intuition in formulating MPC systems based on the strengths and weaknesses of the different formulations of MPC. The paper also highlights active areas of research and development. [ABSTRACT FROM AUTHOR]

To improve the accuracy, real-time and stability of intelligent vehicle path tracking control algorithms, a variable Step Model Predictive Control method (VMPC) for path tracking based on Model Predictive Method (MPC) is proposed. A vehicle dynamics model considering path tracking was constructed, and a VMPC controller was designed based on the model. To address cumulative model error, the proposed control method employs a zero-order holder-based short-step discretization prediction model in the front part of the prediction interval and a first-order holder-based long-step discretization prediction model in the back part. Carsim/Simulink co-simulations were conducted to compare the performance of the proposed VMPC controller with that of a traditional MPC controller on double-lane roads and highways. The simulation results indicate that the proposed VMPC controller exhibits superior control precision, smoothness, real-time performance, and dynamic stability. The proposed method decreases 56.6% for the lateral error, 52.4% for the heading error, 28.5% for the sideslip angle, and 45.7% for the average solution time at most when compared to a standard MPC. Experiments were performed on a drive-by-wire integrated chassis platform, which confirmed that the proposed VMPC controller achieves desired tracking control accuracy for variable curvature paths in engineering applications. [ABSTRACT FROM AUTHOR]

Autonomous driving technology for agricultural machinery can maximise crop yield, reduce labour costs, and alleviate labour intensity. In response to the current low degree of automation and low tracking accuracy of driving paths in agricultural equipment, this research proposes an unmanned agricultural machinery operating system based on an improved fuzzy adaptive PD control algorithm. Firstly, mechanical kinematic models and fuzzy adaptive control algorithms are introduced to achieve autonomous driving, and parameter settings and speed adjustments are made while considering errors. Secondly, in the autonomous driving operation system, taking a certain rice machine as an example, perception information, trajectory design, dynamic control, operation supervision, and remote control design are carried out. The experimental results show that the improved fuzzy algorithm exhibits smaller deviation results in driving path tracking, with an average error between the actual path and the expected path of less than 0.001 m. In different testing scenarios, compared with the actual control results, the maximum deviation of the control system platform in straight sections is less than 2.8 m, which is more stable. More than 95% of the lateral deviation results in the road sections are within 0.11 m. And the tracking distance error of the proposed method in the straight and curved segments is relatively small, far smaller than other comparative algorithms. The unmanned agricultural machinery operation system proposed in this study can significantly improve the efficiency and accuracy of agricultural machinery work, promote the development of intelligent and modern agricultural machinery, and provide reference value and important contributions to social and economic development as well as the progress and promotion of related technologies. [ABSTRACT FROM AUTHOR]

In order to increase the accuracy of the path tracking, an improved model predictive control (IMPC) is proposed for autonomous vehicle under road conditions of large curvature, which can enhance the performances of the driving stability and safety. The controller design is implemented in four steps. First, the curvature of road ahead is derived and applied to determine the longitudinal velocity. Thus, the longitudinal velocity is not assumed to be constant, which is the salient feature of the proposed control. Second, the kinematic model of vehicle is established by the Ackermann steering principle. Third, the predictive model is constructed by linearization and discretization of the kinematic model. Fourth, the longitudinal velocity and the front steering angle are imposed on hard constraints, and the constrained objective function is designed and composed of the position deviation and the control increment. Then, we can obtain the optimal results of the longitudinal velocity and the front steering angle. Furthermore, experiment and simulation on the path tracking of an autonomous vehicle are presented. The results show that the proposed control can realize excellent tracking performance under the road conditions of large curvature. [ABSTRACT FROM AUTHOR]

This paper proposes a lateral-stability-oriented path-tracking controller for four-wheel independent drive (4WID) autonomous vehicles. The proposed controller aims to maintain vehicle stability under extreme conditions while minimizing lateral deviation. Firstly, a tiered control framework comprising upper-level and lower-level controllers is introduced. The upper-level controller is a lateral stability path-tracking controller that incorporates tire dynamic characteristics, developed using model predictive control (MPC) theory. This controller dynamically updates the tire lateral force constraints in real time to account for variations in tire dynamics under extreme conditions. Additionally, it enhances lateral stability and reduces path-tracking errors by applying additional yaw torque based on minimum tire utilization. The lower-level controllers execute the required steering angles and yaw moments through the appropriate component equipment and torque distribution. The joint simulation results from CarSim and MATLAB/Simulink show that, compared to the traditional MPC controller with unstable sideslip, this controller can maintain vehicle lateral stability under extreme conditions. Compared to the MPC controller, which only considers lateral force constraints, this controller can significantly reduce lateral tracking errors, with an average yaw rate reduction of 31.62% and an average sideslip angle reduction of 40.21%. [ABSTRACT FROM AUTHOR]

There are various types of fruits and vegetables that need to be planted on ridges. In order to allow for seedlings with a certain row space and seedling space, the ridge transplanter should be able to track along the ridge. Therefore, an ultrasonic ridge-tracking method and system were developed to let the ridge transplanter track the ridge accurately. The ultrasonic ridge-tracking method mainly contains a limiter sliding window filtering algorithm and a fuzzy look-ahead distance decision model. The limiter sliding window filtering algorithm was proposed to filter the abnormal measuring results to avoid disoperation of the steering mechanism. Moreover, the fuzzy look-ahead distance decision model was proposed to determine the optimal look-ahead distance in order to obtain a desirable tracking performance. Additionally, a comparison experiment of the proposed ultrasonic ridge-tracking method and the universal pure pursuit method was conducted. The experimental results show that the greatest mean absolute errors of the lateral deviations of the ultrasonic ridge-tracking method and universal pure pursuit were 10.56 mm and 13.11 mm. The greatest maximum absolute errors of the lateral deviations of the ultrasonic ridge-tracking method and universal pure pursuit were 18.87 mm and 23.23 mm. In addition, the greatest root mean square error of the lateral deviation of the ultrasonic ridge-tracking method and the universal pure pursuit method were 13.52 mm and 15.66 mm. According to the ridge-tracking performance of the proposed ultrasonic ridge-tracking method, it can be used in practical transplanting conditions. Moreover, in other fields, robots or intelligent machinery can also apply the proposed ultrasonic ridge-tracking method to track objects similar to ridges. [ABSTRACT FROM AUTHOR]

Abstract Targeting the lateral motion control problem in the intelligent vehicle autopilot structural system, this paper proposes a feedforward + predictive LQR algorithm for lateral motion control based on Genetic Algorithm (GA) parameter optimisation and PID steering angle compensation. Firstly, based on the vehicle dynamics tracking error model, the intelligent vehicle LQR lateral motion controller as well as the feedforward controller are designed, and upon which the predictive controller is added to eliminate the system lag.Subsequently, exploiting the advantage that the PID algorithm is not model-based, a PID steering angle compensation controller that can directly control and correct the lateral error is designed. Second, a LQR controller based on path tracking deviation is designed by using the parameter rectification method of genetic algorithm (GA), which optimizes the control parameters of the lateral motion controller and improves the adaptivity of the control accuracy. Finally, Based on the Carsim-Simulink co-simulation platform, the simulation validation and analysis of double lane change (DLC) test and circular condition test (CCT) are carried out, and the results indicate that compared with the other two LQR controllers, the optimised controllers improved more than 50% in lateral error and heading error control, and the vehicle sideslip angle and vehicle yaw rate are in the range of −0.05° to 0.05° and − 0.15 rad/s to 0.10 rad/s, and it showed improved performance in tracking accuracy and satisfied vehicle stability constrains.

Car-like robots are front wheel steering robots with a structure similar to that of unmanned vehicles and are widely used in manufacturing, warehousing, and other industries because of their advantages, such as simple structure and high load-bearing capacity. The path tracking control of car-like robots, characterized by a small range of system constraints and a low degree of component standardization, is garnering increasing widespread attention. Several studies have been conducted on this topic; however, these studies have not considered the correlation between active speed adjustment and path tracking. Existing control systems set the longitudinal speed to a low value to ensure high path-tracking control accuracy. Meanwhile, among active speed control strategies for other mobile equipment, multilayer model predictive control (MMPC), based on model predictive control (MPC), has the advantage of high compatibility with error sources. However, existing MMPC systems adopt linear MPC (LMPC) as the bottom path tracking control algorithm. Typical LMPC design methods are unable to take into account the reference path information in front of car-like robots and are not accurate enough at high longitudinal speeds; therefore, the longitudinal speeds of existing MMPC systems are still low. To address these problems, an MMPC-based framework for active speed adjustment and path-tracking control is introduced. The framework combines a feedforward MPC (FMPC) bottom path-tracking control algorithm with high accuracy and real-time performance and a nonlinear MPC (NMPC) top-speed adjustment algorithm with high long-term predictive accuracy. This new system aims to enhance car-like robots’ path tracking capabilities while actively adjusting their speed. The effectiveness of the proposed MMPC system is validated through joint simulations using MATLAB and CarSim. Results show that the proposed system achieves high-accuracy path tracking at high average traveling speeds, recording a maximum displacement error of 0.1838 m and a heading error of 0.1350 rad at an average traveling speed of 4.2859 m·s−1. Compared with constant-speed FMPC, NMPC systems, and the existing MMPC system, the proposed system demonstrated higher accuracy at higher longitudinal speeds. Under the same working conditions, the error of the FMPC system is dispersed, and the proposed MMPC system can reduce the maximum displacement error by 46.29% and 62.22% compared with the existing NMPC and MMPC systems. When higher accuracy of path tracking can be guaranteed, the average traveling speed of the proposed MMPC system is higher, and this system can increase the average traveling speed by 43.06% and 317.48% compared with existing FMPC and MMPC systems with smaller errors, approaching the performance index of the NMPC system.

Abstract In this paper, a robust control method is introduced for autonomous vehicle control in different scenarios. Dual controllers have been used in this method to ensure high performance and low errors during the vehicle’s trip. The new control system is called Model Predictive and Stanley based controller (MPS), which is an integration of a model predictive controller and a Stanley controller. Each of these two controllers has its drawbacks and weaknesses. The proposed method tries to overcome these points and come up with a high-performance control system. This hybrid way of combining two of the famous controllers has the benefit of using the best part of each one and trying to enhance the other part. The MPS is tested for both path-following and vehicle control in different scenarios and on both straight and curved roads. This controller has shown high performance and flexibility to deal with different scenarios of autonomous driving. The results are compared to previous types of controllers, and the proposed system outperformed these types.

This paper presents the implementation of the Pure Pursuit control algorithm for path tracking on an agricultural pesticide spraying drone. Precise path tracking ensures accurate pesticide coverage, maximizing crop yield and minimizing environmental impact. Conventional position control architectures used by most agricultural drones can lead to inconsistent pesticide distribution due to variability in drone speed. Position control also causes deceleration and acceleration at corners, resulting in over-spraying in these areas. This lack of uniform spray distribution challenges efficient and sustainable agriculture. The Pure Pursuit algorithm is favored for its simplicity and effectiveness in autonomous navigation. The software architecture, including the flight control stack and ROS2-based PX4 simulation architecture, demonstrates the drone’s accurate trajectory following capabilities. Simulation tests evaluated the path tracking accuracy and overall performance of the system. Comparative results show the Pure Pursuit controller’s superiority over standard position controllers in terms of accuracy, robustness, and adaptability. Additionally, this paper introduces an innovative Coverage Path Planning (CPP) strategy based on grid decomposition. Integrated with the Pure Pursuit control mechanism, this CPP strategy ensures precise path tracking and maximizes coverage uniformity, further enhancing the effectiveness and sustainability of agricultural spraying operations.

The traditional pure pursuit controller is often difficult to deal with the control delay brought by the sensor signal delay, actuator response lag and other factors, as well as the lack of active anti-disturbance ability under the intervention of internal and external disturbances. At the same time, the preview distance is used as the denominator of the control law and it gradually will decrease when tracking the target point near the end, resulting in sudden changes in the input angle value and along with the unstable steering wheel, which affects the driving stability and the riding experience of the passengers. A path tracking controller based on improved pure pursuit was proposed. Firstly, the base model of the pure pursuit controller was constructed and a road preview model combining the planned path and the planned speed information was established to improve the lagging effect of the pure pursuit control. Then, the unknown disturbances generated by the internal and external environment of the system were considered, a nonlinear expanded state observer (NESO) was designed to estimate and compensate the disturbances in real time to enhance the anti-disturbance capability of the pure pursuit controller. Furthermore, a steering stability adjustment model was developed to improve the sudden change of steering angle when it's approaching the end point. Finally, the effectiveness of the proposed controller was tested on a software-in-the-loop dynamics simulation platform with different initial heading and initial deviation conditions. Further, the proposed controller was verified in a closed-loop test on a real-world vehicle platform, and the experimental results show that improved pure pursuit algorithm has good tracking accuracy and steering smoothness. [ABSTRACT FROM AUTHOR]

This paper introduces a novel fuzzy logic switched model predictive control (MPC) algorithm for articulated steering vehicles, addressing significant path tracking challenges due to varying road conditions and vehicle speeds. Traditional single-model and parameter-based controllers struggle with tracking errors and computational inefficiencies under diverse operational conditions. Therefore, a kinematics-based MPC algorithm is first developed, showing strong real-time performance but encountering accuracy issues on low-adhesion surfaces and at high speeds. Then, a 4-DOF dynamics-based MPC algorithm is designed to enhance tracking accuracy and control stability. The proposed solution is a switched MPC strategy, integrating a fuzzy control system that dynamically switches between kinematics-based and dynamics-based MPC algorithms based on error, solution time, and heading angle indicators. Subsequently, simulation tests are conducted using SIMULINK and ADAMS to verify the performance of the proposed algorithm. The results confirm that this fuzzy-based MPC algorithm can effectively mitigate the drawbacks of single-model approaches, ensuring precise, stable, and efficient path tracking across diverse adhesion road conditions. [ABSTRACT FROM AUTHOR]

In this paper, an optimized adaptive human-machine collaborative torque control methodology is developed for a class of autonomous vehicle systems with unknown disturbances by employing a reinforcement learning technology with identifier-critic-actor architecture. The criticism method is proposed to assess the performance of the control and to give feedback to the actor. Furthermore, a human-vehicle-road model with steering torque as input is constructed to describe the vehicle's dynamic response and the driver's maneuvering behavior. The stability of the control system is analyzed by means of the Lyapunov theory. Finally, the effectiveness of the control methodology is illustrated by means of a numerical example. [ABSTRACT FROM AUTHOR]

In response to the problem of low optimization efficiency and low tracking accuracy in vehicle path tracking, a comprehensive optimization method is established based on the 3-DOF vehicle motion model. The outer layer adopts the adaptive particle swarm optimization (APSO) method for parameter optimization, and improves the adaptive inertia weight and adaptive particle exploration rate to improve the convergence efficiency and global search ability of the population. The inner layer adopts the segmented Gaussian pseudospectral method (GPM) to optimize the vehicle motion trajectory, and sets continuity constraints to ensure the continuity of the state and control variables at the segmentation points. The inner optimization results are fed back to the outer layer as a reference for the population updating fitness, achieving double-layer iterative optimization. The simulation results show that the proposed APSO-GPM optimization method can effectively solve the vehicle path tracking problem, with a high solving efficiency and stronger global optimization ability. [ABSTRACT FROM AUTHOR]

Aiming at the problem of multiple constraints and low solving efficiency in the process of vehicle path tracking, an improved hp-adaptive Radau pseudospectral method (I-hp-ARPM) which uses a double-layer optimization iteration strategy and the residual of differential algebraic constraints at sampling points with a Gaussian distribution as the error evaluation criterion is proposed. Firstly, a four-DOF vehicle motion model is established. Secondly, on the basis of establishing algebraic differential constraints and path constraints and satisfying the optimization objective function, the I-hp-ARPM is used to transform the optimal control problem (OCP) into a general nonlinear programming problem for solution. Finally, the effectiveness of the proposed method is verified compared with the traditional hp-adaptive pseudospectral method. The simulation results and the virtual test show that there are peak values at 3.5 s and 4.8 s, as well as 6 s, for both the steering wheel angle and the sideslip angle with the condition of μ = 0.8. And also, there are peak values at the times of 3.5 s and 5.5 s, as well as 7.5 s, with the condition of μ = 0.4. This indicates the vehicle can track the reference path well with the control of the proposed algorithm. Both the initial and final constraints, as well as the path constraint, meet the requirements. The proposed method can generate the optimal trajectory that meets various constraint requirements. This method provides a design basis for path tracking of autonomous vehicles and has significance in engineering. [ABSTRACT FROM AUTHOR]

The path tracking control of electric mining articulated vehicles (EMAVs), critical equipment commonly used for mining and transportation in underground mines, is a research topic that has received much attention. The path tracking control of EMAVs is subject to several system constraints, including articulation angle and articulation angular velocity. In light of this, many researchers have initiated studies based on model predictive control (MPC). The principal design schemes for existing MPC methods encompass linear MPC (LMPC) utilizing a single reference point, so named the single reference point LMPC (SRP-LMPC), and nonlinear MPC (NMPC). However, NMPC exhibits suboptimal real-time performance, while SRP-LMPC demonstrates inferior accuracy. To simultaneously improve the accuracy and real-time performance of the path tracking control of EMAV, based on the SRP-LMPC, a path tracking control method for EMAV based on nonlinear compensated multiple reference points LMPC (MRP-LMPC) is proposed. The simulation results demonstrate that MRP-LMPC simultaneously exhibits a commendable degree of accuracy and real-time performance. In all simulation results, the displacement error amplitude and heading error amplitude of MRP-LMPC do not exceed 0.2675 m and 0.1108 rad, respectively. Additionally, the maximum solution time in each control period is 5.9580 ms. The accuracy of MRP-LMPC is comparable to that of NMPC. However, the maximum solution time of MRP-LMPC can be reduced by over 27.81% relative to that of NMPC. Furthermore, the accuracy of MRP-LMPC is significantly superior to that of SRP-LMPC. The maximum displacement and heading error amplitude can be reduced by 0.3075 m and 0.1003 rad, respectively, representing a reduction of 65.51% and 73.59% in the middle speed and above scenario. [ABSTRACT FROM AUTHOR]

In this paper, a robust control method is introduced for autonomous vehicle control in different scenarios. Dual controllers have been used in this method to ensure high performance and low errors during the vehicle’s trip. The new control system is called Model Predictive and Stanley based controller (MPS), which is an integration of a model predictive controller and a Stanley controller. Each of these two controllers has its drawbacks and weaknesses. The proposed method tries to overcome these points and come up with a high-performance control system. This hybrid way of combining two of the famous controllers has the benefit of using the best part of each one and trying to enhance the other part. The MPS is tested for both path-following and vehicle control in different scenarios and on both straight and curved roads. This controller has shown high performance and flexibility to deal with different scenarios of autonomous driving. The results are compared to previous types of controllers, and the proposed system outperformed these types. [ABSTRACT FROM AUTHOR]

This work investigates the use of a fuzzy logic controller (FLC) for two-wheeled differential drive mobile robot trajectory tracking control. Due to the inherent complexity associated with tuning the membership functions of an FLC, this work employs a particle swarm optimization algorithm to optimize the parameters of these functions. In order to automate and reduce the number of rule bases, the genetic algorithm is also employed for this study. The effectiveness of the proposed approach is validated through MATLAB simulations involving diverse path tracking scenarios. The performance of the FLC is compared against established controllers, including minimum norm solution, closed-loop inverse kinematics, and Jacobian transpose-based controllers. The results demonstrate that the FLC offers accurate trajectory tracking with reduced root mean square error and controller effort. An experimental, hardware-based investigation is also performed for further verification of the proposed system. In addition, the simulation is conducted for various paths in the presence of noise in order to assess the proposed controller's robustness. The proposed method is resilient against noise and disturbances, according to the simulation outcomes. [ABSTRACT FROM AUTHOR]

This paper proposes an event-triggered model predictive-preview control strategy for autonomous vehicle trajectory tracking. First, the dynamic equation is established based on the preview road curvature and the vehicle's 2 degree-of-freedom relationship. A model predictive tracking controller is designed by predicting the system dynamics in the future. Second, to reduce the computational burden of the controller, the triggering conditions are designed according to the system's stability and feasibility. The proposed event-triggered model predictive-preview control strategy not only makes the autonomous vehicles maintain tracking accuracy but also reduces the number of online optimization. Then, asymptotically stable of closed-loop systems are proved by using Lyapunov's theorem. Finally, simulation experiments show that the proposed strategy has some advantages over traditional model predictive control in terms of improving vehicle tracking accuracy and reducing algorithm complexity. [ABSTRACT FROM AUTHOR]

This article investigates the path-following control problem of an autonomous ground vehicle (AGV) with unknown external disturbances and input deadzones. Neural networks are used to estimate unknown external disturbances, dead zones, and nonlinear functions. The minimum learning parameter scheme is employed to adjust the neural network to reduce the computational load. A backstepping control is proposed to facilitate the tracking of the target path. The steady-state path-following error is decreased by adding an integral error term to the backstepping controller. Command filtering is employed to address the explosion of the complexity issue of the conventional backstepping approach, and the filtering error is compensated via an auxiliary signal. Lyapunov stability study indicates that the AGV closed-loop system is bounded by the proposed control with reasonable accuracy. At last, simulations are given to demonstrate the potential of the proposed scheme in path-following control.Article Highlights: An integral backstepping control with neural network is proposed for path tracking of an autonomous ground vehicle with input deadzones and external disturbances. A command filter is used to estimate the derivative of the virtual controller to avoid the problem of “explosion of the complexity”. The computational burden of the controller is lessened by using a single parameter to adjust the neural network instead of the whole weight vector. [ABSTRACT FROM AUTHOR]

In warehouses with vast quantities of heavy goods, heavy-duty forklift Automated Guided Vehicles (AGVs) play a key role in facilitating efficient warehouse automation. Due to their large load capacity and high inertia, heavy-duty forklift AGVs struggle to automatically navigate optimized routes. Additionally, rapid acceleration and deceleration can pose safety hazards. This paper proposes a velocity-adaptive model predictive control (MPC)-based path tracking method for heavy-duty forklift AGVs. The movement of heavy-duty forklift-type AGVs is categorized into straight-line and curve-turning motions, corresponding to the straight and curved sections of the reference path, respectively. These sections are segmented based on their curvature. The best driving speeds for straight and curved sections were 1.5 m/s and 0.3 m/s, respectively, while the optimal acceleration rates were 0.2 m/s2 for acceleration and −0.2 m/s2 for deceleration in straight paths and 0.3 m/s2 for acceleration with −0.15 m/s2 for deceleration in curves. Moreover, preferred sampling times, prediction domain, and control domain were determined through simulations at various speeds. Four path tracking methods, including pure tracking, Linear Quadratic Regulator (LQR), MPC, and the velocity-adaptive MPC, were simulated and evaluated under straight-line, turning, and complex double lane change conditions. Field experiments conducted in a warehouse environment demonstrated the effectiveness of the proposed path tracking method. Findings have implications for advancing path tracking control in narrow aisles. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To improve the problems of poor trajectory specification, unsecured safety, and unsmooth paths planned by the artificial potential field (APF) in the high-speed overtaking state of intelligent vehicles. The lane-changing gravitational potential field was designed to guide the smart vehicle to change lanes to ensure the normality of the lane-changing trajectory. An obstacle vehicle repulsive potential field with an anisotropic distribution structure was constructed to improve the safety of lane changing, and the road centerline repulsive potential field with a steering notch and design the smoothing cost function was established to optimize the planned trajectory points to make the planned path smoother. The path tracking controller based on linear quadratic regulator (LQR) was designed to track the planned path. Through the joint simulation of Simulink and Carsim, the results show that the improved APF algorithm can plan a more reasonable path, and the tracking error of the designed LQR path tracking controller tends to be close to zero, which can better control the vehicle to complete the overtaking lane change. [ABSTRACT FROM AUTHOR]

Previous control methods developed by our research team cannot satisfy the high accuracy requirements of unmanned surface vessel (USV) path-tracking during bathymetric mapping because of the excessive overshoot and slow convergence speed. For this reason, this study developed an adaptive fuzzy integral-derivative line-of-sight (AFIDLOS) method for USV path-tracking control. Integral and derivative terms were added to counteract the effect of the sideslip angle with which the USV could be quickly guided to converge to the planned path for bathymetric mapping. To obtain high accuracy of the look-ahead distance, a fuzzy control method was proposed. The proposed method was verified using simulations and outdoor experiments. The results demonstrate that the AFIDLOS method can reduce the overshoot by 79.85%, shorten the settling time by 55.32% in simulation experiments, reduce the average cross-track error by 10.91% and can ensure a 30% overlap of neighboring strips of bathymetric LiDAR outdoor mapping when compared with the traditional guidance law. [ABSTRACT FROM AUTHOR]

To improve the anti-disturbance capability of controller in automatic parking system and optimize the execution effect of control law, the closed-loop layered controller of front wheel angle was designed. The non-smooth control law based on the fal function was designed with driving distance as non-time reference to output the target front wheel angle. The front wheel angle observer was designed based on the Ackerman steering model, and the fuzzy sliding mode controller was used to implement closed-loop lateral control. The Carsim/Simulink co-simulation was built to verify the feasibility, tracking effect and robustness of the designed controller by simulating typical parking scenarios. A real vehicle test platform was used for testing. The results show that the closed-loop layered controller of front wheel angle can quickly and accurately track the target path, improve the lateral control accuracy of the parking system and ensure the tracking control effect under unknown steering nonlinear disturbances. [ABSTRACT FROM AUTHOR]

This paper presents the implementation of the Pure Pursuit control algorithm for path tracking on an agricultural pesticide spraying drone. Precise path tracking ensures accurate pesticide coverage, maximizing crop yield and minimizing environmental impact. Conventional position control architectures used by most agricultural drones can lead to inconsistent pesticide distribution due to variability in drone speed. Position control also causes deceleration and acceleration at corners, resulting in over-spraying in these areas. This lack of uniform spray distribution challenges efficient and sustainable agriculture. The Pure Pursuit algorithm is favored for its simplicity and effectiveness in autonomous navigation. The software architecture, including the flight control stack and ROS2-based PX4 simulation architecture, demonstrates the drone's accurate trajectory following capabilities. Simulation tests evaluated the path tracking accuracy and overall performance of the system. Comparative results show the Pure Pursuit controller's superiority over standard position controllers in terms of accuracy, robustness, and adaptability. Additionally, this paper introduces an innovative Coverage Path Planning (CPP) strategy based on grid decomposition. Integrated with the Pure Pursuit control mechanism, this CPP strategy ensures precise path tracking and maximizes coverage uniformity, further enhancing the effectiveness and sustainability of agricultural spraying operations. [ABSTRACT FROM AUTHOR]

Pure pursuit algorithm is commonly used in path tracking control of autonomous vehicle for its high real-time performance. Due to the problem of "taking shortcuts," traditional pure pursuit algorithms usually have the problem of low path tracking accuracy in curved road scenarios. To address the issue, a path tracking control method based on improved pure pursuit algorithm is proposed. This method builds upon traditional pure pursuit theory and dynamically adjusts the look-ahead distance based on vehicle speed and road curvature radius information, allowing it to adapt to different road scenarios. This effectively addresses the problem of large path tracking errors in curved road scenarios. Furthermore, a fuzzy feedback control is employed to compensate for control variable and enhance tracking accuracy across various scenarios. Simulations and real-world experiments demonstrate that the proposed method significantly improves path tracking accuracy compared to traditional pure pursuit methods, particularly in curved road scenarios. The maximum lateral deviation is reduced by over 50%, realizing the precise tracking of autonomous vehicle on the park roads. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Dalian University of Technology / Dalian Ligong Daxue Xuebao is the property of Journal of Dalian University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Signal delay, execution deviation and reference model mismatch are common faults in autonomous vehicles, resulting in reduced path tracking control performance. To improve the path tracking performance of autonomous vehicles under fault conditions, this paper proposes a hybrid fault-tolerant (HFT) path tracking control approach, in which passive fault-tolerant path tracking control and fault observer-based hybrid active fault-tolerant path tracking control are introduced. The probable usual errors in path tracking are first examined and analyzed, together with the vehicle kinematic model and a typical autonomous driving system. Then, a slide-mode-based passive fault-tolerant path tracking controller is designed and integrated with a fault observer to create a hybrid active fault-tolerant controller. Finally, simulations and experiments are conducted, and the results demonstrate that our proposal is effective. After convergence of the fault observations, the lateral tracking error is less than 0.2m and the root mean square of the tracking error is 44% smaller than the LQR approach. [ABSTRACT FROM AUTHOR]

For the three-dimensional path tracking control problem of fixed-wing unmanned aerial vehicles, an inner and outer loop controller based on globally stable integral sliding mode S-plane model is proposed in this paper. The outer loop is controlled by the globally stable integral sliding mode, and the inner loop is controlled by the S-plane. Firstly, the globally stable integral sliding control law is designed for the outer loop, and the stability of the control law is proved using the Lyapunov theory. Then the S-plane controller is designed for the instruction signal of the inner loop. Due to the complexity of derivation in the S-plane controller, a second-order differentiator is introduced. The simulation results show that the proposed controller can track the ideal path accurately, which has good control performance and anti-interference performance. [ABSTRACT FROM AUTHOR]

With the rapid development of autonomous driving technology, estimating and controlling key vehicle state parameters under complex road conditions have become critical challenges. This study combines Unscented Kalman Filtering (UKF) and Sliding Mode Control (SMC) methods to propose an integrated control model for achieving more efficient control. First, a three-degrees-of-freedom vehicle dynamics model based on the Dugoff tire model is constructed to accurately estimate key vehicle state parameters. Next, UKF is used to estimate road friction coefficients and key vehicle state parameters, and its performance is compared with Extended Kalman Filtering (EKF) under various conditions. The results show the superiority of UKF in identifying road friction coefficients. Based on SMC theory, a sliding surface is designed, and the functional relationship between state variables and control variables is derived to establish the corresponding control model. Joint simulations using Carsim and Simulink under different conditions validate the real-time performance and effectiveness of the designed UKF-SMC integrated control strategy in the presence of external disturbances and system uncertainties. Simulation results indicate that this strategy effectively enhances the overall performance and safety of autonomous vehicles, providing an accurate real-time solution capable of handling complex and variable road conditions. The proposed UKF-SMC integrated control strategy not only proves its theoretical superiority but also demonstrates promising practical applications in simulation experiments. This study provides reliable technical support for the development of autonomous driving technology under complex road conditions. [ABSTRACT FROM AUTHOR]

Tracking performance and stability of path tracking is crucial for unmanned vehicles' navigational tasks. Researches on vehicle path tracking controllers primarily rely on dynamic models. In contrast, there are less designs and researches that focus on path tracking controller based on kinematic model. This scarcity may stem from the perceived inadequacy in the accuracy of vehicle kinematic models. This research introduces a novel method for modifying the traditional vehicle kinematic model by incorporating a front wheel steering angle modification function to enhance tracking performance of path tracking controllers based on kinematic models. In terms of control strategy selection, the study opted for nonlinear model predictive control with terminal cost. A controller which is founded on the modified model was used on a simulated vehicle on CarSim-Simulink platform to assess the tracking performance. The simulation was designed with a double-shift line reference trajectory and the initial speeds of the simulated vehicle are 5 m/s and 10 m/s, respectively. The results of the simulations indicate that employing a controller based on the modified model could reduce the peak tracking error by 76.45 % and 43.06 %, and the root mean square of the tracking error was reduced by 73.29 % and 36.06 %, respectively.

Car-like robots are mobile robots commonly used in manufacturing and warehousing. This type of robot has a mechanical structure similar to that of an unmanned vehicle, which uses the front wheels as steering structures. However, this type of robot has characteristics that significantly influence path tracking control relative to unmanned vehicles, such as a larger magnitude of reference path curvature, a smaller range of system constraints, and a lower degree of part standardization. Consequently, several research efforts dedicated to path tracking for car-like robots have emerged. Among the path-tracking control methods for car-like robots, model predictive control (MPC) has a tremendous advantage in dealing with system constraints. However, the existing nonlinear model predictive control (Nonlinear MPC, NMPC) has inferior real-time performance, and the linear model predictive control (LMPC) has poor accuracy. Therefore, a path-tracking control method for car-like robots with high accuracy and superior real-time performance needs to be developed. Because the reason for the low accuracy of LMPC in paths with significant curvature changes is that the response of LMPC is not timely after the curvature change, the idea of combining LMPC and feed-forward information is adopted. The basis of path-tracking controller for car-like robots is the LMPC. The reference front wheel angle at the reference path point in front of the car-like robot is obtained as a feed-forward signal through an inverse kinematic model. A feed-forward optimization objective function that conforms to the LMPC architecture is designed, and a feed-forward model predictive control (FMPC) is proposed by combining the feed-forward optimization objective function with the LMPC. The FMPC is tested by joint simulation using MATLAB and CarSim. The FMPC has high accuracy, the absolute value of the displacement error in all of the simulation results does not exceed 0.1110 m, and the absolute value of the heading error does not exceed 0.1177 rad. The accuracy of the FMPC is comparable to that of the NMPC under the same conditions, and the errors of LMPC, feed-forward control, and Stanley control are dispersed under these conditions. The FMPC also has superior real-time performance, and the solving time in each control period does not exceed 4.31 ms. Under the same conditions, the FMPC is comparable to the LMPC in terms of real-time performance and can reduce the maximum value of the solving time in each control cycle by 80.68% and the average value by 65.14% compared with the NMPC. The FMPC can also ensure that the control variables are within the system constraints and are less affected by positioning errors.

ObjectiveThe traditional predictive control approach usually employs a fixed time horizon and often overlooks the impact of changes in curvature and road bends. This oversight leads to subpar tracking performance and inadequate adaptability of robots for navigating curves and paths. Although extending the time horizon of the standard fixed time horizon model predictive control (MPC) can improve curve path tracking accuracy, it comes with high computational costs, making it impractical in situations with restricted computing resources. Consequently, an adaptive time horizon MPC controller was developed to meet the requirements of complex tasks such as autonomous mowing.MethodsInitially, it was crucial to establish a kinematic model for the mowing robot, which required employing Taylor linearization and Euler method discretization techniques to ensure accurate path tracking. The prediction equation for the error model was derived after conducting a comprehensive analysis of the robot's kinematics model employed in mowing. Second, the size of the previewing area was determined by utilizing the speed data and reference path information gathered from the mowing robot. The region located a certain distance ahead of the robot's current position, was identified to as the preview region, enabling a more accurate prediction of the robot's future traveling conditions. Calculations for both the curve factor and curve change factor were carried out within this preview region. The curvature factor represented the initial curvature of the path, while the curvature change factor indicated the extent of curvature variation in this region. These two variables were then fed into a fuzzy controller, which adjusted the prediction time horizon of the MPC. The integration enabled the mowing robot to promptly adjust to changes in the path's curvature, thereby improving its accuracy in tracking the desired trajectory. Additionally, a novel technique for triggering MPC execution was developed to reduce computational load and improve real-time performance. This approach ensured that MPC activation occurred only when needed, rather than at every time step, resulting in reduced computational expenses especially during periods of smooth robot motion where unnecessary computation overhead could be minimized. By meeting kinematic and dynamic constraints, the optimization algorithm successfully identified an optimal control sequence, ultimately enhancing stability and reliability of the control system. Consequently, these set of control algorithms facilitated precise path tracking while considering both kinematic and dynamic limitations in complex environments.Results and DiscussionThe adaptive time-horizon MPC controller effectively limited the maximum absolute heading error and maximum absolute lateral error to within 0.13 rad and 11 cm, respectively, surpassing the performance of the MPC controller in the control group. Moreover, compared to both the first and fourth groups, the adaptive time-horizon MPC controller achieved a remarkable reduction of 75.39% and 57.83% in mean values for lateral error and heading error, respectively (38.38% and 31.84%, respectively). Additionally, it demonstrated superior tracking accuracy as evidenced by its significantly smaller absolute standard deviation of lateral error (0.025 6 m) and course error (0.025 5 rad), outperforming all four fixed time-horizon MPC controllers tested in the study. Furthermore, this adaptive approach ensured precise tracking and control capabilities for the mowing robot while maintaining a remarkably low average solution time of only 0.004 9 s, notably faster than that observed with other control data sets-reducing computational load by approximately 10.9 ms compared to maximum time-horizon MPC.ConclusionsThe experimental results demonstrated that the adaptive time-horizon MPC tracking approach effectively addressed the trade-off between control accuracy and computational complexity encountered in fixed time-horizon MPC. By dynamically adjusting the time horizon length the and performing MPC calculations based on individual events, this approach can more effectively handle scenarios with restricted computational resources, ensuring superior control precision and stability. Furthermore, it achieves a balance between control precision and real-time performance in curve route tracking for mowing robots, offering a more practical and reliable solution for their practical application.

Substation secondary equipment is monitored through inspection robots, providing an important means to enhance power equipment automation and intelligent management, thereby ensuring the safe operation of power engineering equipment. In this paper, a Mecanum wheeled omnidirectional mobile robot is developed for automatically inspecting secondary equipment in substations. It possesses autonomous navigation, positioning, and identification capabilities, significantly enhancing inspection efficiency and the accuracy of protection plate state identification. The Mecanum wheels enable the inspection robot to move flexibly and adjust its attitude within narrow working environments. Meanwhile, a multi-track lifting platform facilitates image acquisition and state identification of the secondary equipment pressure plate, covering a height range of 350-1 800 mm. The robot employs the lidar-based simultaneous localization and mapping (SLAM) method for autonomous positioning and navigation, supplemented by a vision-based path extraction and tracking algorithm for precise position correction at set points. Moreover, a color recognition-based image arrangement and state recognition method is proposed to accurately identify and assess the connection state of the secondary equipment protection plate. Experimental results demonstrate that the substation secondary equipment inspection robot, utilizing Mecanum wheels, achieves successful autonomous navigation and precise positioning, with maximum deflection angles and distances during path tracking processes being ±3° and ±8 mm, respectively. Additionally, the plate recognition method, combining machine vision and color recognition, achieves an outstanding recognition accuracy rate exceeding 95.80%, thereby elevating the level of robot automation in inspection operations.

In view of the task requirements of accurate recovery of autonomous undersea vehicles(AUVs), a model-free adaptive control based on the active disturbance rejection control(ADRC-MFAC) algorithm was proposed from the perspective of model-free control, so as to improve the insufficient path tracking accuracy caused by model uncertainty and vulnerability to environmental interference in AUV motion. According to the characteristics of the second-order model system and line-of-sight guidance, the MFAC was improved by redesigning the control input criterion function, solving the problem that MFAC was only applicable to the self-balancing system. A tracking differentiator was introduced to smooth the desired signal, and a linear extended state observer was designed by considering the influence of unknown compound interference. The estimated disturbance was compensated for in the controller. The stability of the controller was verified, and system robustness was improved. Under the same interference, the proposed control scheme could improve the anti-interference ability and control precision by 42.37% and 45%, compared with the traditional proportional-integral-differential controller. The result shows that ADRC-MFAC can significantly improve the anti-interference performance of AUVs and enhance path tracking accuracy.

Path planning is one of the most fundamental problems that must be solved before a robot can navigate and explore autonomously. Path planning needs to be integrated with path tracking to be applied to autonomous robots. This makes path tracking also important for autonomous robot navigation which cannot be separated from path planning. There are two path planning methods, the first is search-based method, the second is sampling-based method. Both have their own advantages, but the popular and commonly used sampling-based algorithm due to its fast convergence is preferred in path planning. The RRT* algorithm was developed. This improvement initiated a major civilization in sampling-based algorithms, namely parent node selection and rewiring in RRT. Although there has been an improvement in optimality, RRT* still doesn't provide the distance optimality value as expected, due to its character that is still adopted from RRT. The resulting path is still suboptimal and not smooth (jagged). On the other side, Path tracking has several methods, however, these path tracking methods are difficult to apply to autonomous robots and need to be adapted to the robot used. Based on the description above, there are still problems with path planning, namely paths that are still less than optimal and convergence that is still slow. This research will add a way to shorten the distance in the RRT* algorithm with the triangular inequality method. Meanwhile, for path tracking, we will apply the pose-to-pose method, which follows the waypoint that has been made by path planning.

Track skid and slip often occur, when the tracking chassis work in soft soil and uneven field. The accuracy of curved path tracking can inevitably deteriorate in automatic navigation. In this work, path tracking was designed to consider the track skid and slip in field conditions. Firstly, the coefficient of turning radius correction was derived to describe the skid and slip characteristics using the kinematic model of the tracked chassis. Then, the lateral and heading deviations were determined by the look-ahead model. Finally, a three-input fuzzy controller was established with the controller inputs of lateral deviation, heading deviation and turning radius correction coefficient. A dual-motor tracked chassis was designed to validate the model. The control hardware included two brushless DC motors, a motor driver, an STM32 microcontroller, and two photoelectric rotary encoders. In navigation, the position, heading and angular velocity of the chassis were measured by a high-precision RTK-GNSS and an MTi-30 inertial sensor. The measurement values were sent to the upper-level computer using a serial port. The path tracking was carried out to calculate the control signal, and then sent into the lower-level microcontroller. The tracking of the reference path was realized by the motor driver of the lower-level microcontroller on both sides. A field experiment was conducted on automatic navigation to verify the performance of a three-input fuzzy controller. The field was selected with a lot of weeds, where the soil was wet and sticky with a moisture content of 22.4%. There was the low flatness with some pits, bulges and hardened soil blocks that scattered throughout the whole field. The speed of the chassis was set to 0.6 m/s in navigation. The look-ahead distance proportional coefficient of the look-ahead model was set to 1.5. An initial lateral deviation of about 1000 cm was set for the correction. The results showed that the average response time of the control system was 6.79 s, while the average distance was 4.08 m. The tracking test of the U-shaped path was performed on the three-input fuzzy controller. The maximum absolute deviation, average absolute deviation and standard deviation of the straight-line and curve tracking section were 12.0 and 21.3 cm, 3.6 and 8.6 cm, as well as 4.4 and 8.4 cm, respectively. The curve tracking with the three-input fuzzy controller was compared with the conventional one, in order to further determine the path tracking accuracy under track skid and slip. The maximum absolute deviation, average absolute deviation and standard deviation of the conventional and three-input fuzzy controller were 31.1 and 22.4 cm, 8.3 and 7.4 cm, as well as 10.2 and 9.1 cm, respectively. Compared with the conventional, the three-input fuzzy controller was improved by 27.95%, 10.84 % and 10.78 %, respectively, in the maximum absolute deviation, average absolute deviation and standard deviation. The better control performance of the navigation system was achieved in the three-input fuzzy controller, indicating the reduced influence of track skid and slip. This finding can also provide a strong reference for the high-precision navigation of tracked chassis in soft soil environments in the field. [ABSTRACT FROM AUTHOR]