Intelligent vehicle path tracking control based on hybrid controller

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 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.

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

This paper presents an improved fixed-time control strategy for path tracking of autonomous gun-launched coaxial-rotor micro aerial vehicles (MAVs) operating under unknown aerodynamic forces. The proposed algorithm combines a filtered fixed-time hierarchical backstepping (FTHB) method with a fixed-time disturbance observer (FDO). Using a coordinate transformation, the complex dynamics of the MAV are decoupled into fully actuated and underactuated subsystems. The novelty of this work lies in two key contributions: (1) the design of a fixed-time hierarchical backstepping controller for the nominal system, ensuring fixed-time stability, with fixed-time filters employed to estimate virtual control inputs and thereby reduce the complexity of the backstepping design, and (2) the integration of fixed-time observers to estimate unknown disturbances in fixed time, effectively addressing the challenges posed by aerodynamic uncertainties affecting the entire system. The effectiveness of this control strategy is rigorously demonstrated through fixed-time stability analyses. A comprehensive comparative assessment, covering both qualitative and quantitative evaluations, is carried out through extensive simulations comparing the proposed controller with a fixed-time controller, a finite-time controller, and a disturbance observer-based controller. This evaluation clearly underscores the robustness and superior performance of the proposed control strategy.

Autonomous path tracking control design for a comercial quadcopter

The path tracking control problem of robot manipulators carrying unknown payloads is studied on the basis of the payload-dependent robot model and its structural properties. The nonlinear payload-dependent dynamic model of a robot manipulator is derived, and several structure properties of this model are investigated. On the basis of these properties, a path tracking control strategy is presented. It is shown that the presented strategy not only compensates for the effects of couplings, nonlinearities, and payload uncertainty but also guarantees tracking convergence.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this study, to improve the accuracy of path tracking in intelligent vehicles, we propose an intelligent vehicle path-tracking control method based on improved model predictive control (MPC) combined with hybrid proportional-integral-derivative (PID) control theory. In the lateral control, a constraint on the side deflection of the front wheel is added based on traditional MPC and a relaxation factor is introduced to improve the stability of vehicle control for the driving stability. In longitudinal control, a hybrid PID controller is designed for different road conditions to improve the accuracy of control of vehicle speed. We present the results of a co-simulation using Carim and MATLAB/Simulink and a test with a sample vehicle, which show that the proposed path tracking controller can greatly improve the path tracking accuracy and stability of an intelligent vehicle. The model-based prediction, rolling optimization solution, feedback control, and the addition of a constraint on the side deflection of the front wheel as well as a relaxation factor can ensure the lateral driving stability of an intelligent vehicle. The proposed approach achieved a lateral error of less than 1&#x0025;, and the yaw angle was controlled within 4&#x00B0;. The longitudinal speed control based on hybrid PID controller can improve the response speed of the system and meet the real-time requirements of vehicle driving.

Since the curvature of urban roads varies greatly, path tracking in this scenario faces the problem that it is difficult to simultaneously guarantee high accuracy and high efficiency. According to previous work on path tracking, nonlinear model predictive control (NMPC) has good accuracy when the curvature of the reference path varies. But when it is hoped to further improve the efficiency, the accuracy of NMPC-based path tracking control is still difficult to guarantee. So, we combined fuzzy control and NMPC to design a path tracking controller, which can adjust the speed to ensure high accuracy and high efficiency. Then, we verified the proposed controller through the joint simulation of MATLAB/Simulink and CarSim. When the simulation environment is consistent, the maximum absolute values of the displacement error and the heading error of the proposed controller are 80.4 % and 46.6 % smaller than those of the NMPC controller. The proposed controller is robust to positioning error, and it can operate normally when the positioning error is not greater than 0.04 m. Besides, the real-time performance of the proposed controller can be ensured by reducing the control horizon. The maximum calculation time in the simulation only accounts for 28 % of the control period.

In this paper, a nonlinear model predictive control (NMPC) scheme for path tracking of autonomous vehicles using discrete previewed points in the inertial coordinate is presented. The control objective is to improve the tracking accuracy under small lateral acceleration in the scenario of low speed and narrow space. The path tracking problem is formulated as a nonlinear model predictive control model, in which the vertical distance between the vehicle position and the tangent of previewed point is adopted to evaluate the tracking error. The discrete previewed points are generated from the path points independently, which does not rely on the approximate path functions. The iterative initial values of the optimization model are appropriately selected by combining with the Stanley method to accelerate optimization computations. A simulation comparison between the proposed NMPC controller and a linear model predictive control (LMPC) controller is conducted through Carsim-Matlab/Simulink co-simulations. Simulation results show that the proposed controller exhibits better tracking accuracy than the LMPC controller under small lateral acceleration, especially when the path curvature is large and continuously changing.

Path tracking is an important part of the research of intelligent vehicle driving systems. This paper proposes a path tracking method based on model predictive control (MPC). Firstly, the two-degree-of-freedom vehicle model is established, and the incremental model of the design controller is further obtained. Secondly, taking into account the road safety constraints and actuator constraints associated with the shape of the vehicle, a constrained MPC method is employed; The front wheel angle is obtained by rolling optimization, and vehicle path tracking control is realized. Finally, the effectiveness of the verification scheme is verified by the vehicle dynamics software veDYNA and compared with the non-incremental model. The simulation results show that the incremental model has better anti-interference.

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.

The coupling and nonlinearity of vehicle dynamics present considerable challenges to path tracking of autonomous vehicles. In this paper, a necessary condition is derived to decouple the translational motion from yawing motion based on the time-scale separation. Consequently, the translational motion is regulated over an extended control horizon to generate a human-like tracking trajectory. The yawing motion is regulated based on a high-fidelity control model. In addition, model predictive path integral (MPPI) is developed to mitigate the computational burden of nonlinear motion planning through sampling-based optimization. A predictive output regulator is developed to solve the underactuated problem in the 2-DOF lateral dynamics with only 1-DOF of control input. Simulation results show that the proposed method enhances computing efficiency and reduces the lateral jerk by an average of 50% with only one set of parameters.

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.

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.

<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.