Path Tracking Control Research Articles

Dynamic Event-Triggered and Fast Natural Logarithmic Sliding Mode Path Tracking Control for Unmanned Ground Vehicles With the Experiment Validation

Dynamic Event-Triggered and Fast Natural Logarithmic Sliding Mode Path Tracking Control for Unmanned Ground Vehicles With the Experiment Validation

Interval Type-2 Fuzzy Path Tracking Control for Autonomous Ground Vehicles Under Switched Triggered and Sensor Attacks

Interval Type-2 Fuzzy Path Tracking Control for Autonomous Ground Vehicles Under Switched Triggered and Sensor Attacks

A Practical Design Approach for Complex Path Tracking Control of a Tailless Fixed-Wing Unmanned Aerial Vehicle With a Single Pair of Elevons

A Practical Design Approach for Complex Path Tracking Control of a Tailless Fixed-Wing Unmanned Aerial Vehicle With a Single Pair of Elevons

Genetic-Algorithm-Assisted Self-Scheduled Multidelay PIR Control: Experiments in a Car-Like Vehicle System.

The path-tracking control of an intelligent vehicle always suffers from the high-frequency measurement noises. To confront this issue, this work puts forward a novel delayed output-feedback implementation of proportional-integral-derivation (PID) control, which is called multidelay proportional-integral-retarded (PIR) control. The mathematical model of the vehicle system is represented in the form of a linear parameter-varying (LPV) system, which uses the car position as the scheduling variable for regulation. On this basis, the multidelay PIR controller is designed such that the tracking errors gradually converge to zero with the aid of the proportional and integral actions, and the harmful high-frequency measurement noises are attenuated by the retarded term consisting of a few delayed proportional actions. To tune the PIR controller parameters, linear matrix inequalities (LMIs), derived by applying Taylor's expansion to the retarded term, are used to compute the convex subcontroller gains. Then, the self-scheduled tracking controller is formulated as the weighted sum of convex subcontrollers, and the weight functions scheduled by the current position are adaptive to the different operational conditions. Experiments in real time using a laboratory car-like vehicle are employed to assess the performance of the proposed controller.

Adaptive Learning-Based Path-Tracking Control for Unknown Vehicle Systems Under Performance Optimization

Adaptive Learning-Based Path-Tracking Control for Unknown Vehicle Systems Under Performance Optimization

Path Planning and Trajectory Tracking for Autonomous Obstacle Avoidance in Automated Guided Vehicles at Automated Terminals

During the operation of automated guided vehicles (AGVs) at automated terminals, the occurrence of conflicts and deadlocks will undoubtedly increase the ineffective waiting time of AGVs, so there is an urgent need for path planning and tracking control schemes for autonomous obstacle avoidance in AGVs. An innovative AGV autonomous obstacle avoidance path planning and trajectory tracking control scheme is proposed, effectively considering static and dynamic obstacles. This involves establishing three potential fields that reflect the influences of obstacles, lane lines, and velocities. These potential fields are incorporated into an optimized model predictive control (MPC) cost function, leveraging artificial potential fields to ensure effective obstacle avoidance. To enhance this system’s capability, a fuzzy logic system is designed to dynamically adjust the weight coefficients of the hybrid artificial potential field model predictive controller, strengthening the autonomous obstacle avoidance capabilities of the AGVs. The tracking control scheme includes a fuzzy linear quadratic regulator based on a fuzzy logic system, a dynamics model as a lateral controller, and a PI controller as a longitudinal tracker to track the pre-set trajectory and speed autonomously. Multi-scenario simulation tests demonstrate the effectiveness and rationality of our autonomous obstacle-avoidance control scheme.

Adaptive coordinated control strategy for autonomous vehicles based on four-wheel steering

To address the challenges of coordinating path tracking, maneuverability, and stability control in real-time during actual driving of autonomous vehicles, this paper proposes an adaptive coordinated control strategy for four-wheel steering and four-wheel drive autonomous vehicles based on the model predictive control algorithm. First, an approach utilizing a recursive least square method with an adaptive forgetting factor is proposed to estimate and update the cornering stiffness of the prediction model in real-time, aiming to enhance the control accuracy of the controller. Second, the phase plane method is employed to divide the vehicle stability domain, incorporating indicators based on maneuverability and lateral stability to facilitate effective vehicle stability control. Next, the control priority is designed, and the weight of the objective function is adaptively adjusted to achieve the overall coordination between path tracking and handling stability control. Finally, co-simulation of Carsim and MATLAB/Simulink demonstrates that under high-speed and large curvature conditions, the proposed control strategy can further enhance the overall performance of the vehicle. On high-adhesion roads, compared to the general fixed weight strategies, the proposed control strategy has improved path tracking accuracy by 50.57% and 18.41%, respectively. Additionally, on low-adhesion roads, the proposed control strategy still maintains precise trajectory control, with path tracking accuracy improvements of 47.04% and 5.8%, while further enhancing the vehicle’s maneuverability and stability.

Nonsingleton Gaussian type-3 fuzzy system with fractional order NTSMC for path tracking of autonomous cars

Path-tracking and lane-keeping tasks are critical to guarantee safety and navigation performance considerations for deploying autonomous cars. This paper presents a novel control framework for the path-tracking control of high-speed autonomous cars with structured uncertainties. This study introduces a nonlinear adaptive control system based on a fractional-order terminal sliding mode system while incorporating a novel Gaussian Nonsingleton type-3 fuzzy system (FOTSM-NT3FS). Therefore, the proposed controller is independent of the information about the ego vehicle’s dynamic information, and instead, the dynamics are approximated through a developed NT3FLS. The developed control system exhibits robustness to measurement errors and faulty sensors, because the inputs to the NT3FS are uncertain. In order to guarantee the boundedness of the adaptation parameters, the σ−mod approach is employed. The Lyapunov stability theorem and Barbalat’s lemma are used to ensure the uniform ultimate boundedness of the closed loop system and the convergence of tracking errors to the origin in finite time. High-fidelity co-simulations with CarSim and MATLAB are performed to verify the effectiveness of the proposed control scheme and are also compared to other reported methods in the literature. Based on the obtained results, the schemed controller exhibits competitive effectiveness in path-tracking tasks and strong efficiency under various road conditions, parametric uncertainties, and unknown disturbances.

Path-Tracking Ability of the ASV on Different Adhesion Coefficient Roads Based on Slide Mode Control

A vehicle equipped with an articulated steering system is often used in a particular area, such as a mine or the construction field. These sealing or semi-sealing fields make autonomous driving by an ASV (Articulated Steering Vehicle) easier than in passenger cars. Path-tracking is essential for an autonomous vehicle. To improve the path-tracking ability of an ASV, a path-tracking model is established based on dynamic theory. Then, the slide-mode predictive-control (SMPC) strategy is initiated to design the path-tracking controller. The improvement in the path-tracking ability on roads with different adhesion coefficients is validated via the HIL (Hardware in Loop) platform. According to the simulation in HIL with the provided strategy, the path-tracking error during the typical condition decreased by 21.73~93.84%, demonstrating the SMC controller can be used to improve the path-tracking performance of an ASV.

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.

Robust Tube-MPC Based Steering and Braking Control for Path Tracking at High-Speed Driving

Robust Tube-MPC Based Steering and Braking Control for Path Tracking at High-Speed Driving

Optimal Path Tracking Control Based on Online Modeling for Autonomous Vehicle With Completely Unknown Parameters

Optimal Path Tracking Control Based on Online Modeling for Autonomous Vehicle With Completely Unknown Parameters

Multiagent-Reinforcement-Learning-Based Stable Path Tracking Control for a Bionic Robotic Fish With Reaction Wheel

The path tracking of the robotic fish is a hotspot with its high maneuverability and environmental friendliness. However, the periodic oscillation generated by bionic fish-like propulsion mode may lead to unstable control. To this end, this paper proposes a novel framework involving a newly-designed platform and multi-agent reinforcement learning (MARL) method. Firstly, a bionic robotic fish equipped with a reaction wheel is developed to enhance the stability. Secondly, a MARL-based control framework is proposed for the cooperative control of tail-beating and reaction wheel. Correspondingly, a hierarchical training method including initial training and iterative training is designed to deal with the control coupling and frequency difference between two agents. Finally, extensive simulations and experiments indicate that the developed robotic fish and proposed MARL-based control framework can effectively improve the accuracy and stability of path tracking. Remarkably, the head-shaking is reduced about 40%. It provides a promising reference for the stability optimization and cooperative control of bionic swimming robots featuring oscillatory motions.

Event-Triggered Robust Path Tracking Control Considering Roll Stability Under Network-Induced Delays for Autonomous Vehicles

Event-Triggered Robust Path Tracking Control Considering Roll Stability Under Network-Induced Delays for Autonomous Vehicles

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

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

АВТОНОМНІ НАДВОДНІ ДОПОМІЖНІ СУДНА: ПЕРЕВАГИ ТА ПРОБЛЕМИ

With the continuous progress of modern science and technology and the increasing requirements for marine transport in various fields, the intelligence and automation of ships has become a general trend. Autonomous unmanned surface vessel (UASV) control generally includes UASV trajectory planning, path tracking control, and autonomous collision avoidance control. Due to their low cost, small size, fast action, reconnaissance capabilities and other advantages, BPNS play a very important role in everyday life, emergency response and scientific research. UASV, as a rule, consist of a platform and a payload system. But in the whole process of UASV navigation, autonomous mooring is also an important part. And there are fewer studies on the automatic mooring algorithm of UASV. The advanced technology of autonomous mooring of BPNS can effectively reduce the cost of human, material and financial resources, while reducing the accident rate in a reasonable and safe manner. Therefore, it is very important to comprehensively promote the development of autonomous navigation and mooring technology of UASV. Keywords: Autonomous unmanned surface vessel (UASV), autonomous mooring.

Path Tracking Control for Autonomous Truck with Dual Modular Chassis

Path Tracking Control for Autonomous Truck with Dual Modular Chassis

Human–Machine Shared Steering Control Under High-Speed Emergency Obstacle Avoidance Scenarios

The allocation of controlling and driving authority is an important and difficult aspect of human–machine shared steering control (SSC). This paper addresses the SSC problem in high-speed emergency obstacle avoidance scenarios. A parallel SSC framework containing a dynamic driving authority allocation model and a path tracking controller is developed, where the human driver and controller can control the vehicle simultaneously. First, fuzzy logic is adopted in the SSC framework to actively adjust the driving authority between the human driver and the controller. The driver steering state and path tracking error are considered to reduce the negative impact of a driver’s mis-operation and weaken any human–machine conflict. Subsequently, the path tracking controller in the proposed SSC system is designed based on a nonlinear vehicle lateral model to improve the accuracy of the controller, particularly when the vehicle is facing large lateral acceleration. To address the nonlinear control problem, the Udwadia–Kalaba approach is employed and the Lyapunov stability of the controller is proved. Finally, the effectiveness of the proposed SSC system is proved through simulation results, which show that the vehicle has excellent path tracking performance in high-speed obstacle avoidance scenarios. In addition, the system can resolve the human–machine conflict problem.

Comparative Study on Coordinated Control of Path Tracking and Vehicle Stability for Autonomous Vehicles on Low-Friction Roads

This paper presents a comparative study on coordinated control of path tracking and vehicle stability for autonomous vehicles on low-friction roads. Generally, a path-tracking controller designed on high-friction roads cannot provide good performance under low-friction conditions. To cope with the problem, a coordinated control between path tracking and vehicle stability has been proposed to date. In this paper, three types of coordinated controllers are classified according to the controller structure. As an actuator, front-wheel steering, four-wheel steering, and four-wheel independent braking and driving are adopted. A common feature of these types of controllers is that front steering and yaw moment control are adopted as control inputs. To convert the yaw moment control into tire forces generated by combinations of multiple actuators, a control allocation method is applied. For each type, a controller is designed and simulated using vehicle simulation software. From the simulation results, a performance comparison among those controller types is carried out. Through comparison, it is shown that there are small differences among those types of controllers in terms of path tracking.

Tuning path tracking controllers for autonomous cars using reinforcement learning.

This article proposes an adaptable path tracking control system, based on reinforcement learning (RL), for autonomous cars. A four-parameter controller shapes the behaviour of the vehicle to navigate lane changes and roundabouts. The tuning of the tracker uses an 'educated' Q-Learning algorithm to minimize the lateral and steering trajectory errors, this being a key contribution of this article. The CARLA (CAR Learning to Act) simulator was used both for training and testing. The results show the vehicle is able to adapt its behaviour to the different types of reference trajectories, navigating safely with low tracking errors. The use of a robot operating system (ROS) bridge between CARLA and the tracker (i) results in a realistic system, and (ii) simplifies the replacement of CARLA by a real vehicle, as in a hardware-in-the-loop system. Another contribution of this article is the framework for the dependability of the overall architecture based on stability results of non-smooth systems, presented at the end of this article.