Path Tracking Control Strategy Research Articles

Path tracking and energy efficiency coordination control strategy for skid-steering unmanned ground vehicle

The skid steering unmanned ground vehicle (SUGV) plays an important role in extremely harsh environments. Improving the autonomous control capability and energy efficiency of SUGV is urgently needed. This article presents a skid steering-based path tracking control strategy. In the upper controller, an improved model-free sliding mode controller (APMS) is used to calculate the yaw moment for tracking control. On the lower controller, the Snow Ablation Optimizer (SAO) is used to distribute the output torque of the drive motors, taking longitudinal force, yaw moment and energy consumption into account. Finally, the designed controller is validated through simulation under different operating conditions. The results show that the proposed coordination controller achieves good control performance, increases energy efficiency and at the same time ensures tracking accuracy.

Dynamic modeling and learning based path tracking control for ROV-based deep-sea mining vehicle

Track slippage and body sinking of the tracked mining vehicle in the traditional deep-sea mining system are the critical issues for operating stability. To solve this bottleneck problem, a novel ROV-based deep-sea mining system is proposed in this study, in which a remotely-operated vehicle (ROV) towering a sledge-shaped mining robot (MRT) named ROV-based Deep-sea Mining Vehicle (ROVDMV) is instead of the traditional tracked Deep-sea mining vehicle. The design of the ROVDMV can fundamentally overcome the bottleneck problem. However, the complex marine environment and multi-rigid-body design of the ROVDMV pose new challenges for its path-tracking control. Firstly, the dynamic model of the ROVDMV considering the ROV at a fixed depth is established based on the bicycle model, which is mainly used as the control object in the numerical simulation. Secondly, a learning-based path-tracking control strategy is proposed for the path-tracking control of the ROVDMV. In the control strategy, a novel nonparametric learning (NPL) method is introduced to learn the uncertain nonlinear dynamics considering the external disturbances and parametric uncertainty. The NPL method is proven to provide bounded estimated error. Besides, the enhanced NPL method can save approximately 33 % of the computation time, and the average computation time for its optimization control problem is only 12.47 ms. Finally, the numerical results show that the NPL method can learn nonlinear dynamics accurately, and the proposed strategy has proven to be effective.

Finite-Time Line-of-Sight Guidance-Based Path-Following Control for a Wire-Driven Robot Fish.

This paper presents an adaptive line-of-sight (LOS) guidance method, incorporating a finite-time sideslip angle observer to achieve precise planar path tracking of a bionic robotic fish driven by LOS. First, an adaptive LOS guidance method based on real-time cross-track error is presented. To mitigate the adverse effects of the sideslip angle on tracking performance, a finite-time observer (FTO) based on finite-time convergence theory is employed to observe the time-varying sideslip angle and correct the target yaw. Subsequently, classical proportional-integral-derivative (PID) controllers are utilized to achieve yaw tracking, followed by static and dynamic yaw angle experiments for evaluation. Finally, the yaw-tracking-based path-tracking control strategy is applied to the robotic fish, whose motion is generated by an improved central pattern generator (CPG) and equipped with a six-axis inertial measurement unit for real-time swimming direction. Quantitative comparisons in tank experiments validate the effectiveness of the proposed method.

A Fast-Computing Path Tracking Control Strategy for Autonomous Multiaxle Electric Vehicle Considering Safety and Stability

A Fast-Computing Path Tracking Control Strategy for Autonomous Multiaxle Electric Vehicle Considering Safety and Stability

Intelligent Vehicle Path Tracking and Stability Cooperative Control Strategy Based on Stable Domain

Intelligent Vehicle Path Tracking and Stability Cooperative Control Strategy Based on Stable Domain

Research on a Path Tracking Control Strategy for Autonomous Vehicles Based on State Parameter Identification

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.

Adaptive path tracking control for autonomous vehicles with sideslip effects and unknown input delays

This paper presents an adaptive path-tracking control of autonomous ground vehicles with sideslip effects and time-varying input delays. To handle the problem of unknown time-varying input delays, the vehicle kinematic model is transformed into a novel chain model, and a delay-based auxiliary integral term is introduced for the estimation of the unknown terms caused by time-varying delays. To avoid modeling errors caused by existing linear approximation methods, a new adaptive path-tracking control strategy is proposed, such that the optimal tracking performance is achieved by appropriately adjusting certain design parameters. In addition, the inherent complexity explosion issue is avoided with the aid of a boundary estimation strategy. All signals of the closed-loop systems are guaranteed to be bounded. Simulation results illustrate the effectiveness of the method proposed.

Personalized Path-Tracking Approach Based on Reference Vector Field for Four-Wheel Driving and Steering Wire-Controlled Chassis

It is essential and forward-thinking to investigate the personalized use of four-wheel driving and steering wire-controlled unmanned chassis. This paper introduces a personalized path-tracking approach designed to adapt the vehicle’s control system to human-like characteristics, enhancing the fit and maximizing the potential of the chassis’ multi-directional driving and steering capabilities. By modifying the classic vehicle motion controller design, this approach aligns with individual driving habits, significantly improving upon traditional path-tracking control methods that rely solely on reference vector fields. First, the classic reference vector field’s logic was expanded upon, and it is shown that a personalized upgrade is feasible. Then, driving behavior data from multiple drivers were collected using a driving simulator. The fuzzy c-means clustering method was used to categorize drivers based on typical states that match vehicle path-tracking performance. Additionally, the random forest algorithm was used as the method for recognizing driving style. Subsequently, a personalized path-tracking control strategy based on the reference vector field was developed and a distributed execution architecture for four-wheel driving and steering wire-controlled unmanned chassis was established. Finally, the proposed personalized path-tracking approach was validated using a driving simulator. The results of the experimental tests demonstrated that the personalized path-tracking control approach not only fits well with various driving styles but also delivers high accuracy in driving style identification, making it highly suitable for application in four-wheel driving and steering wire-controlled chassis.

Research on Optimization of Intelligent Driving Vehicle Path Tracking Control Strategy Based on Backpropagation Neural Network

To enhance path tracking precision in intelligent vehicles, this study proposes a lateral–longitudinal control strategy optimized with a Backpropagation (BP) neural network. The strategy employs the BP neural network to dynamically adjust prediction and control time-domain parameters within an established Model Predictive Control (MPC) framework, effectively computing real-time front-wheel steering angles for lateral control. Simultaneously, it integrates an incremental Proportional–Integral–Derivative (PID) approach with a meticulously designed acceleration–deceleration strategy for accurate and stable longitudinal speed tracking. The strategy’s efficiency and superior performance are validated through a comprehensive CarSim(2020)/Simulink(2020b) simulation, demonstrating that the proposed controller adeptly modulates control parameters to adapt to various road adhesion coefficients and vehicle speeds. This adaptability significantly improves tracking and driving dynamics, thereby enhancing accuracy, safety, stability, and real-time responsiveness in the intelligent vehicle tracking control system.

An identical path tracking control strategy of the tractor-trailer wheeled mobile robot with an off-axle hitching based on a passive steering angle

For a tractor-trailer wheeled mobile robot (TTWMR, for short) with an off-axle hitching, it is challenging for both tractor and trailer to track a desired path due to the insufficient steering capability of the trailer. As such, a passive steering angle is first introduced, which allows the trailer to follow the same path as the leading tractor. In this way, a given motion task can be reduced to the accurate path tracing problem for the tractor. By considering such a passive steering angle, the motion speed relation between two vehicles of the TTWMR with an off-axle hitching is then derived, based on which its kinematics and dynamics equations are established. Thereafter, a proportion integration feedback controller is then developed by combining the curvature tracing method and the directed Lyapunov method, so that both vehicles can move along an identical path. Finally, simulation results indicate the effectiveness and robustness of the resulting control strategy.

Integrated control of path tracking and handling stability for autonomous ground vehicles with four-wheel steering

Accurate modelling of an autonomous ground vehicle (AGV) is challenging due to strong nonlinearity, model perturbations and external disturbances. This necessitates the design of path tracking controllers with robustness. In this paper, an integrated control strategy for path tracking and handling stability of AGVs is proposed, leveraging a four-wheel steering (4WS) system. To address the path tracking problem, a fuzzy logic-based steering strategy is developed, which enables parameter self-adjustment based on displacement error. Additionally, a reference model is employed to transform the path tracking problem into a reference signal tracking problem, ensuring the handling stability of vehicles. A sliding mode controller is designed to track the reference signal, utilizing an extended state observer to estimate and compensate for unmodeled dynamics and unknown external disturbances in real-time. Simulation results demonstrate the effectiveness of the proposed strategy in accurately tracking the desired path while maintaining the lateral stability of the vehicle. Furthermore, the strategy exhibits strong robustness against uncertainties and disturbances.

Intelligent vehicle path tracking control strategy considering data-driven dynamic stable region constraints

The path tracking controller can easily reduce the tracking error, but often exceed the limitations of vehicle stability. In this paper, an intelligent vehicle path tracking control strategy considering data-driven dynamic stable region constraints is proposed. Firstly, based on the two-degree-of-freedom (DOF) vehicle model and nonlinear tire model, the vehicle sideslip angle-sideslip angular velocity ([Formula: see text]) phase plane is established. Then, the stable region dataset is made considering the influence of vehicle speed, adhesion coefficient, and front wheel angle. To get the vehicle driving stable region, a back propagation neural network (BP-NN) regression model is trained offline. Subsequently, a path tracking control strategy based on adaptive-model predictive control (MPC) is designed, which considers the vehicle dynamic stable region constraints with the BP-NN predicting online. Finally, model-in-the-loop (MIL) and driving simulator is designed to test the control strategy, which indicates that it has a better performance compared with the linear quadratic regulator (LQR) path tracking controller.

Research on Path Tracking and Yaw Stability Coordination Control Strategy for Four-Wheel Independent Drive Electric Trucks

Achieving accurate path tracking and vehicle stability control for four-wheel independent drive electric trucks under complex driving conditions, such as high speed and low adhesion, remains a major challenge in current research. Poor coordination control may cause the vehicle to deviate from its intended path and become unstable. To address this issue, this article proposes a coordinated control strategy consisting of a three-layer control framework. In the upper layer controller design, establish a linear quadratic regulator (LQR) path tracking controller to ensure precise steering control by eliminating steady-state errors through feedforward control. The middle layer controller utilizes the fractional order sliding mode control (FOSMC) yaw moment controller to calculate the additional yaw moment based on the steering angle of the upper input, utilizing the error of yaw rate and sideslip angle as the state variable. To collectively optimize the control system, establish a coordinated optimization objective function and utilize the hybrid genetic-particle swarm optimization algorithm (GA-PSO) to optimize the weight coefficient of LQR and sliding mode parameters of FOSMC, effectively improving the performance of the controller. In the lower layer torque distribution controller, use the quadratic programming method to achieve real-time optimal torque distribution based on tire utilization, which improves vehicle stability control. Through simulations conducted under four different working conditions, the proposed control scheme demonstrates a 15.54% to 23.17% improvement in tracking performance and a 10.83% to 23.88% optimization in vehicle driving stability compared to other control methods. This scheme provides a theoretical reference for path tracking and stability control in four-wheel independent drive electric trucks.

Intelligent vehicle obstacle avoidance path-tracking control based on adaptive model predictive control

Abstract. In order to solve the problems of low path-tracking accuracy, poor safety, and stability of intelligent vehicles with variable speeds and obstacles on the road, a double-layer adaptive model predictive controller (MPC) is designed. A vehicle point mass model is used in an obstacle avoidance planning controller, and the safety collision distance model is established according to the distance relationship between the vehicle and the obstacle to improve the driving safety of the vehicle. The design of the path-tracking controller is based on the three-degrees-of-freedom dynamics model. According to the relationship between the predictive horizon and vehicle speed in the MPC algorithm, an adaptive path-tracking control strategy which can update the prediction horizon in real time is proposed to improve the accuracy of vehicle path tracking. To increase the vehicle stability, a sideslip angle and an acceleration control variable are added to the vehicle dynamics model as soft constraint conditions. The proposed method is simulated based on a CarSim and MATLAB/Simulink co-simulation platform. The simulation results show that the maximum lateral path deviation and the maximum centroid sideslip angle of the designed controller are 0.13 m and 0.4∘, respectively. Compared with the traditional MPC, the adaptive MPC maximum lateral path deviation and the maximum centroid sideslip angle are reduced by 0.51 m and 1.57∘, respectively, which proves the effectiveness of the proposed method.

Path Tracking Control of Intelligent Vehicles Considering Multi-Nonlinear Characteristics for Dual-Motor Autonomous Steering System

In the path tracking control of intelligent vehicles, the traditional linear control method is prone to high tracking errors for uncertain parameters of the steering transmission system and road conditions. Therefore, considering the mechanical friction in the dual-motor autonomous steering system and the nonlinearity of tires, this paper proposes a path tracking control strategy of intelligent vehicles for the dual-motor autonomous steering system that considers nonlinear characteristics. First, a dual-motor autonomous steering system considering mechanical friction and the variation of tire cornering stiffness under different tire–road friction coefficients was established based on the structure of an autonomous steering system. Second, a tire–road friction coefficient estimator was designed based on a PSO-LSTM neural network. The tire cornering stiffness under different tire–road friction coefficients was estimated through the recursive least-square algorithm. Then, the control strategy of the dual-motor autonomous steering system was designed by combining the LQR path tracking controller with the adaptive sliding mode control strategy based on field-oriented control. Here, mechanical friction and the variation of tire cornering stiffness were considered. Finally, simulation and HiL tests validated the method proposed in this paper. The results show that the proposed control strategy significantly improves the tracking accuracy and performance of the dual-motor autonomous steering system for intelligent vehicles.

Research on parallel parking path planning and tracking control of unmanned vehicles

Aiming at the problems of discontinuous curvature of the planned path, difficulty in finding the optimal reference path and high control accuracy requirements in tracking control for unmanned vehicles in the process of parallel parking, an optimal reference path planning method based on arc line and multi-objective optimization function and a path tracking control strategy based on nonlinear model predictive control are proposed. Firstly, the upper and lower boundaries of the exercisable area are obtained by the tangent of the arc and the straight line, and the multi-objective optimization function is designed to obtain the optimal arc-line combination, and the optimal reference path is obtained by polynomial curve fitting. Secondly, a path-following controller is designed using nonlinear model predictive control. Finally, a comparative analysis is carried out in MATLAB/Simulink and Carsim with the controller based on pure tracking design. The results show that the optimal reference path curvature obtained by the proposed method is continuous without sudden change, and the designed tracking controller has better tracking accuracy.

NMPC-Based Path Tracking Control Strategy for Autonomous Vehicles With Stable Limit Handling

A novel nonlinear model predictive control (NMPC) strategy for path tracking of autonomous vehicles with stable limit handling is proposed in this article. To improve the path tracking performance under complex driving maneuvers, a tire lateral force model, which treats road adhesion coefficient and vertical load as variable parameters, is discussed first. It has the advantage that the dynamics model can be applied under different driving conditions. In addition, the stability properties of the dynamics system with the front tire lateral force as the virtual control input are analyzed. The global asymptotical stability with zero input and local input-to-state stability with nonzero input of the dynamics system are proved respectively. To perform path tracking within the handling limits, both constraints of front tire lateral force and rear tire slip angle are included in the NMPC controller. The constraint of front tire lateral force is considered as peak limits related to load transfer and adhesion coefficient. And for the rear tire slip angle, a combining constraint scheme is proposed, where the indirect constraint is implemented by a penalty term that predicts the saturation of rear tire lateral force, while the direct constraint is implemented by a dead-zone exponential penalty function. The Continuation/Generalized Minimum Residual (C/GMRES) algorithm is applied to improve the computation efficiency of the NMPC. The performance of the controller is evaluated in simulations and hardware-in-the-loop (HIL) tests, and the results show that the controller is able to track the reference path with high precision in real-time.

Path tracking control strategy for off-road 4WS4WD vehicle based on robust model predictive control

With the increasing requirements for vehicle adaptability and maneuverability in various road environments, four-wheel-steering four-wheel-drive (4WS4WD) vehicles have attracted wider attention. This paper presents a robust model predictive control-based strategy for the path tracking of 4WS4WD vehicles considering external disturbances. The strategy combines model predictive control (MPC) and control allocation under an upper–lower structure. The main objective of the present work is to improve the robustness and stability of path tracking by developing an MPC algorithm in the upper layer. The controller design considers general disturbances caused by allocation errors and sudden disturbances caused by an outer force in the offset model. Based on the offset model, a robust MPC control law is obtained by converting the robustness constraints into a linear matrix inequality. The control law is mathematically demonstrated to be stable in multidisturbed conditions via the Lyapunov stability theorem. Through comparison with a similar control algorithm of path tracking and applying it on different uneven ground conditions, the proposed robust algorithm is found to effectively overcome disturbances on the system.

Adaptive Sliding Mode Path Tracking Control of Unmanned Rice Transplanter

To decrease the impact of uncertainty disturbance such as sideslip from the field environment on the path tracking control accuracy of an unmanned rice transplanter, a path tracking method for an autonomous rice transplanter based on an adaptive sliding mode variable structure control was proposed. A radial basis function (RBF) neural network, which can precisely approximate arbitrary nonlinear function, was used for parameter auto-tuning on-line. The sliding surface was built by a combination of parameter auto-tuning and the power approach law, and thereafter an adaptive sliding controller was designed. Based on theoretical and simulation analysis, the performance of the proposed method was evaluated by field tests. After the appropriate hardware modification, the high-speed transplanter FLW 2ZG-6DM was adapted as a test platform in this study. The contribution of this study is providing an adaptive sliding mode path tracking control strategy in the face of the uncertainty influenced by the changeable slippery paddy soil environment in the actual operation process of the unmanned transplanter. The experimental results demonstrated that: compared to traditional sliding control methods, the maximum lateral deviation was degraded from 17.5 cm to 9.3 cm and the average of absolute lateral deviation was degraded from 9.1 cm to 3.2 cm. The maximum heading deviation was dropped from 46.7° to 3.1°, and the average absolute heading deviation from 10.7° to 1.3°. The proposed control method not only alleviated the system chattering caused by uncertain terms and environmental interference but also improved the path tracking performance of the autonomous rice transplanter. The results show that the designed control system provided good stability and reliability under the actual rice field conditions.

Research on Automatic Driving Path Tracking Control of Open-Pit Mine Transportation Vehicles with Delay Compensation

The transportation environment of the open-pit mine is complex, the steering actuator of the mine vehicle has a large delay and poor response accuracy, and there are a lot of bumpy roads, large undulating ramps, and narrow-area curves in the mining area. These road sections seriously reduce the tracking accuracy of the mine vehicle path. Tracking control presents great challenges. Therefore, this study first conducts a simulation comparison study on commonly used path tracking methods such as pure pursuit control, Stanley control, and model predictive control (MPC), and then designs a path tracking control strategy for automatic driving of open-pit mine transportation vehicles based on the MPC algorithm. Finally, the proposed control strategy was verified through actual mining vehicle tests. The results showed that the maximum lateral deviation obtained by the MPC-based path tracking control strategy was reduced from 0.55 m to 0.08 m under the C-shaped reference path compared with the traditional method. Under the S-shaped reference path, the lateral deviation is reduced from 0.4 m to 0.16 m.