대잠전용 무인수상로봇의 자율운항시스템 설계를 위한 LQR 기반 경로추종 알고리즘 연구

Aiming to address the tracking accuracy and anti-rollover problem of the unmanned mining truck path tracking process under the complex unstructured road conditions in mining areas, a coordinated control strategy for path tracking and anti-rollover based on topology theory is proposed. Moreover, optimal equilibrium weights are assigned to path tracking control and anti-rollover control to ensure that the path tracking accuracy of the mining vehicle can be effectively improved in a safe and stable driving state. Regarding the path tracking problem, a lateral preview error model is established, and a path tracking controller is designed using LQR (linear quadratic regulator) control theory. In the design of the anti-rollover controller, the effects of understeer and trip-type rollover on the stability of the vehicle are taken into account, and the ideal transverse swing angular velocity and trip-type rollover evaluation index are introduced for controller design, which reduce the effects of the curves and roadway excitation on the mining truck and improve the rollover motion. Based on a joint simulation using Trucksim and Simulink and the construction of a hardware-in-the-loop simulation platform for verification, the single control strategy and coordinated control strategy are compared and analyzed. The final simulation results show that the tracking error, yaw velocity, and center of mass side deviation angle are optimized by 45%, 32.5%, and 20%, respectively. Therefore, the Extension theory-based coordinated controller satisfies the complex road conditions in the mining area and improves the tracking accuracy to the maximum extent while ensuring the safety and smoothness of vehicle driving and exhibiting good adaptability and robustness.

This paper presents a method to design a path tracking controller with a constraint on tire slip angles under low-friction road conditions. On a low-friction road surface, a lateral tire force is easily saturated and decreases as a tire slip angle increases by a large steering angle. Under this situation, a path tracking controller cannot achieve its maximum performance. To cope with this problem, it is necessary to limit tire slip angles to a value where the maximum lateral tire force is achieved. The most commonly used controllers for path tracking, linear quadratic regulator (LQR) and model predictive control (MPC), are adopted as a controller design methodology. The control inputs of LQR and MPC are front and rear steering angles and control yaw moment, which have been widely used for path tracking. The constraint derived from tire slip angles is imposed on the steering angles of LQR and MPC. To fully verify the performance of the path tracking controller with the constraint on tire slip angles, a simulation is conducted on vehicle simulation software. From the simulation results, it is shown that the path tracking controller with the constraint on tire slip angles presented in this paper is quite effective for path tracking on low-friction road surface.

This paper presents a method for designing a static output feedback integrated path tracking controller for autonomous vehicles. For path tracking, state–space model-based control methods, such as linear quadratic regulator, H∞ control, sliding mode control, and model predictive control, have been selected as controller design methodologies. However, these methods adopt full-state feedback. Among the state variables, the lateral velocity, or the side-slip angle, is hard to measure in real vehicles. To cope with this problem, it is desirable to use a state estimator or static output feedback (SOF) control. In this paper, an SOF control is selected as the controller structure. To design the SOF controller, a linear quadratic optimal control and sliding mode control are adopted as controller design methodologies. Front wheel steering (FWS), rear wheel steering (RWS), four-wheel steering (4WS), four-wheel independent braking (4WIB), and driving (4WID) are adopted as actuators for path tracking and integrated as several actuator configurations. For better performance, a lookahead or preview function is introduced into the state–space model built for path tracking. To verify the performance of the SOF path tracking controller, simulations are conducted on vehicle simulation software. From the simulation results, it is shown that the SOF path tracking controller presented in this paper is effective for path tracking with limited sensor outputs.

In order to realize the path following control of the robot in the complex agricultural environment, an intelligent path following control method based on fuzzy PD (proportional derivative) is proposed. Firstly, in order to realize the task of path tracking and automatic steering control, the automatic navigation controller and the automatic steering method based on fuzzy PD control are constructed. The method of driving the robot hardware platform based on plant protection unmanned, developed automatic navigation control system based on laser radar, angle sensor, then according to the structure and control principle of automatic navigation control system, the robot kinematics model, two wheel vehicle control model, pure path tracking control method, finally, has carried on the the design of the tracking control principle and fuzzy PD control method is verified by MATLAB/simulink simulation platform. The results show that the maximum tracking error is only 5cm, and the average tracking error is 1.2cm when the robot tracks with 1.5m/s. The simulation and experimental results show that the control system can effectively control the robot walking on a predetermined path.

<abstract> <bold>Abstract. </bold>This paper focuses on path tracking and vibration reducing velocity control inside strawberry field furrows. A path tracking controller is developed which maintains the robot chassis in the center of the furrow and parallel to the furrow walls by minimizing the heading error between the robot’s velocity vector and a look ahead point placed at a constant distance ahead of the robot in the center of the furrow. This type of control is based on a method known as pure pursuit. The velocity controller developed implements a finite state machine which controls the velocity based on the vibration energy. When the vibration is higher or lower than different vibration thresholds, the velocity will be changed accordingly. The results of the experiments demonstrate that by reducing velocity when the robot enters an uneven terrain, the vibration energy of the robot stays within the range that it was in when the robot was on the even terrain. Furthermore, the results indicate that the path tracking controller was able to maintain a lateral error of less than 2 centimeters from the center of a 50 centimeter wide furrow and heading error of less than 3 degrees. Future experiments will include determining the threshold a fragile crop can experience without bruising damage so that a maximum velocity that will maintain the integrity of the crop can be determined.

Differential-steering based path tracking control and energy-saving torque distribution strategy of 6WID unmanned ground vehicle

This paper proposes a novel adaptive path tracking and stability control strategy for autonomous vehicles based on intelligent tires technology that is introduced to estimate the tire-road contact states based on the in-tire sensor signals. Firstly, a tire finite element model was established based on ABAQUS software. On this basis, the Spearman correlation analysis method was used to acquire the optimal sensitive positions of the in-tire acceleration sensors. The feature values of the inputs of the tire-road contact states estimation model were determined through the PLS-VIP analysis method. Then, the tire slip angle and lateral force estimation models whose inputs are the in-tire sensor signals was constructed using the LSTM neural network. Subsequently, an adaptive MPC path tracking control strategy in which the tire cornering stiffness is calculated in real-time by the intelligent tires model was designed. Finally, an adaptive NFTSM lateral stability control strategy which uses the tire lateral force estimated by the intelligent tires model was designed to determine the direct yaw moment, and an efficient and concise algorithm was then designed to achieve torque distribution. The simulation results indicate that the proposed control strategy provides better path tracking accuracy and stability control effect in emergency lane change and high-speed evasive maneuvers under different road conditions.

This paper presents a method to design a path tracking controller with four-wheel independent braking (4WIB), drive (4WID) and steering (4WIS) systems equipped in in-wheel motor-driven electric vehicles (IWM-EVs). Generally, it is difficult to calculate the steering angles of 4WIS and the braking/traction torques of 4WIB/4WID for path tracking control. Moreover, there have been limitations of an error dynamics-based path tracking controller, which requires assumptions on a target path. To cope with these problems, the path tracking problem on a target path is converted into the yaw rate tracking one with a reference yaw rate in this paper. Two methods are adopted for the purpose of calculating a reference yaw rate. The first is to use a pure pursuit method, which generates a steering angle for path tracking. From the steering angle, a reference yaw rate is calculated. The second is to derive a reference yaw rate from a target path and a current vehicle position. For yaw rate tracking, direct yaw moment control is adopted to generate a control yaw moment. A control allocation method is adopted to distribute a control yaw moment into tire forces, generated by 4WIS, 4WID and 4WIB. Several actuator combinations are represented by various sets of virtual weights in the control allocation method. A simulation with a vehicle simulation program, CarSim^&#x00AE;, shows that the proposed path tracking controller is effective in enhancing the path tracking performance with 4WIS, 4WID and 4WIB. From the simulation, effects of actuator combinations on path tracking performance are analyzed.

With the advancements in autonomous driving technology, artificial intelligence and computer science have facilitated the autonomous operation of machinery. Industrial production efficiency is enhanced by this autonomy in construction machinery. Furthermore, personal safety for machine operators is also improved. Among these advancements, path tracking control emerges as a critical component for the automation of construction machinery. It primarily focuses on the lateral and longitudinal control of the machinery, enabling stable tracking of a predefined path even under challenging operational conditions. To enhance the accuracy and robustness of path tracking, this paper proposes a mixed linear quadratic regulator (LQR) controller specifically designed for autonomous tracked vehicles. This approach uses the LQR control algorithm to suppress the noise generated by complex construction environments and accurately track the desired path. The effectiveness and practicality of this method are validated through simulation experiments.

This paper addresses the problem of three-dimensional (3-D) waypoint tracking for a biomimetic underwater vehicle (BUV) propelled by undulatory fins: RobCutt-II. Based on the specific mechanical design and control system configuration, the RobCutt-II can perform diversified locomotion patterns, especially submerging or surfacing vertically in the water. For practical underwater operating procedures, a selective switching control for 3-D waypoint tracking is proposed. This control scheme contains a depth controller, a waypoint tracking controller, and a selector. When tracking a series of given 3-D waypoints, the RobCutt-II can switch between two closed-loop locomotion patterns, i.e., the depth control pattern and the waypoint tracking pattern. Simulations and a comparative experimental study demonstrate the feasibility and effectiveness of the proposed switching control scheme.

Modelling and control strategies in path tracking control for autonomous tracked vehicles: A review of state of the art and challenges

Due to the uncertainty of vehicle model parameters, modeling errors and external disturbances, the performance of path tracking control system is poor, especially under high velocity and large curvature extreme conditions. To address this issue, this paper presents a novel gain-scheduled robust control strategy based on linear parameter varying system with model predictive control (MPC) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> . Firstly, fully considering the influence of the time-varying characteristics of vehicle velocity and tire cornering stiffness on the path tracking system model, a novel linear parameter varying system model is built for path tracking control of autonomous vehicle. Then, a path tracking robust controller is designed based on gain-scheduled approach, and the linear matrix inequality (LMI) is applied to solve optimization problem, in which MPC and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> robust control theory are applied to the controller design process. Finally, the simulation experiments have verified that the proposed novel robust control strategy can improve the path tracking accuracy and ensure the vehicle lateral and roll stability, especially under high velocity and large curvature extreme conditions. Meanwhile, the proposed robust controller shows superiority to suppress the parameters uncertainty, modeling error and external disturbance.

As a new carrier to carry out intelligent task, the unmanned ground vehicle (UGV) is an important component of the intelligent transportation system (ITS) in the future. Most of traditional UGV path tracking control methods are deployed in the edge-side (on-board computing platform) with restricted data, which severely limits the improvement of UGVs’ path tracking performance. Therefore, this paper proposes a novel cloud-edge combined control system with MPC parameter optimization based on cloud brain control center (CBCC) applied in future ITS, which consists of edge-side control module and cloud-side optimization module. The proposed control system can optimize control parameters iteratively in CBCC by making the utmost of big data generated by UGVs to markedly enhance the control effectiveness. In CBCC, the path tracking performance is quantitatively evaluated from the aspects of path tracking accuracy, vehicle stability, and control stability. Also, an optimization algorithm is established by using SVR theory. Based on this, the cloud-side optimization module optimizes the control parameters of the edge-side control module by collecting and processing the big data generated by UGVs while driving. Besides, the designed edge-side control module is based on MPC algorithm and innovatively introduces a compensator which obviously reduces the error caused by the inaccuracy of the prediction model. To verify the effectiveness of the proposed cloud-edge combined control system, numerous simulation experiments are carried out. The results shows that the proposed cloud-edge combined control system can improve UGV’s path tracking performance and have strong robustness at different speeds.

Solving the path-tracking problem of vehicles is a key part of realizing intelligent transportation. The four-wheel- independent-steering (4WIS) electric vehicle (EV) with steer-by-wire (SBW) has the advantages of good controllability, low speed maneuverability and high speed stability, which is beneficial to improving the path tracking performance. In this paper, a novel 4WIS electric vehicle is proposed and the path tracking control research was carried out on it by using the adaptive fuzzy PID algorithm. Firstly, the four-wheel steering strategy was analyzed, and the geometric relationship between the front and rear steering angle when the vehicle's centroid angle is zero was derived. Furthermore, based on the principle of adaptive fuzzy PID control and the single-step heading prediction algorithm, a path-tracking controller was designed for the self-built 4WIS EV. A Simulink/Carsim co-simulation platform was built, on which the simulation was carried out at different condition, and the simulation results show that the designed adaptive fuzzy PID controller has good path-tracking performance and strong robustness. Finally, the serpentine trajectory and the double lane change (DLC) trajectory are taken as the reference trajectory, and the reliability and robustness of the designed adaptive fuzzy PID control algorithm were verified on the open road. The results show that the adaptive fuzzy PID control algorithm has good tracking performance and is far superior to pure PID control.

With the growing demand for safe obstacle avoidance and precise trajectory tracking in the autonomous navigation of unmanned surface vessels (USVs), this paper investigates an adaptive differential evolution approach for integrated path planning and tracking control. In the path planning stage, an elite archive mechanism is first incorporated into the mutation process, and the scaling factor F and crossover rate CR are adaptively adjusted to enhance population diversity and global search capability. Then, the International Regulations for Preventing Collisions at Sea (COLREGs) are embedded into the algorithmic framework to reinforce collision avoidance performance in complex encounter scenarios. A multi-objective fitness function combining six performance criteria is subsequently constructed to evaluate individual path points, thereby identifying high-quality solutions that ensure both safe navigation and route efficiency. In the tracking control stage, the optimally generated reference trajectory is then employed as the input command for the vessel’s motion control subsystem. A fuzzy logic system is introduced to approximate unknown nonlinear dynamics, and an adaptive fuzzy logic controller is designed to guarantee accurate tracking of the planned path. Finally, simulation tests are used to show the algorithm’s efficiency and usefulness.