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Aiming to promote the control performance of traditional fixed-parameter model predictive control (MPC), a new longitudinal and lateral coupling path tracking control method for the autonomous vehicle is proposed. First, a dynamic model of the autonomous vehicle, which can describe the longitudinal and lateral motion states, is established. Secondly, both the tire side-slip angle and centroid side-slip angle constraints are introduced in the path tracking controller design, which improves the accuracy of the prediction model at high speed. Furthermore, the key parameters of the proposed path tracking controller are optimized. The simulation and experimental results show that compared with the traditional MPC tracking control method, the lateral error and directional error of the proposed optimized path tracking control method under high speed decrease by 19.17 % and 11.89 %, respectively, which improves the path tracking performance significantly. [ABSTRACT FROM AUTHOR]

Braking control of urban rail vehicles with multiple carriages is critical to ensure the safe operation of urban rails. However, existing decentralized braking control methods lead to the inconsistent speed and excessive coupler force among carriages, which compromises the operation safety of urban rails. To address this issue, a cooperative braking strategy is proposed for urban rail vehicles based on Koopman model predictive control method. First, a cyber‐physical model is established, where the physical layer characterizes the dynamic model of multiple carriages using the Koopman operator. The cyber layer represents the communication topology of carriages with graph theory. Second, a cooperative braking controller is designed with the distributed model predictive control method. An optimisation problem is formulated to minimize the speed inconsistency and relative displacement difference among carriages. Third, extensive simulations under various scenarios, including normal and extreme operating conditions, are conducted to verify the effectiveness of the proposed method. Simulation results show that, compared with the classical decentralized method, the proposed braking method reduces the average relative displacement by 78% while reducing the speed error by 29%. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of path following and dynamic obstacle avoidance for an underwater biomimetic vehicle-manipulator system (UBVMS). Firstly, the general kinematic and dynamic models of underwater vehicles are presented; then, a nonlinear model predictive control (NMPC) scheme is employed to track a reference path and collision avoidance simultaneously. Moreover, to minimize the tracking error and for a higher degree of robustness, improved extended state observers are used to estimate model uncertainties and disturbances to be fed into the NMPC prediction model. On top of this, the proposed method also considers the uncertainty of the state estimator, while combining a priori estimation of the Kalman filter to reasonably predict the position of dynamic obstacles during short periods. Finally, simulations and experimental results are carried out to assess the validity of the proposed method in case of disturbances and constraints. [ABSTRACT FROM AUTHOR]

In order to improve the path following performance and solve the stability problems of autonomous distributed drive electric buses, this paper proposed a path following and lateral-yaw-roll stability integrated control method. Firstly, to cope with the varying speed during different maneuvers and roll motion of autonomous distributed drive electric buses, a nonlinear 4-DOF vehicle dynamic model which includes rolling and longitudinal movement is established. Secondly, a hierarchical control method is built, in the upper layer, a model predictive controller (MPC) is designed to generate the control inputs, which is obtained through an optimization problem including path following error and vehicle stability condition, especially the roll stability for buses, and for the lower layer, the torque of each drive wheel is distributed through solving an optimization problem about drive requirement and tire utilization. Finally, the validation of the proposed method is carried out through TruckSim-Simulink co-simulation, the results illustrate that both following accuracy and vehicle stability are obtained more effectively than the popular linear MPC method, especially roll stability is guaranteed under extreme condition. [ABSTRACT FROM AUTHOR]

• Dynamic model of the tracked vehicle considered this type of forces which is traction, hydrodynamic, centrifugal, lateral friction, longitudinal resistive, bulldozing, and compaction force. • Path tracking is vital in autonomous vehicles since it is designed to deliver appropriate steering, throttle, and brake input to control the vehicle's direction and speed along a predefined path. • Model Predictive Control (MPC) may handle system limits and future predictions. • The objective of a path tracking controller is to eliminate any variation in vehicle direction from the specified path. This paper provides a review of path tracking strategies used in autonomous vehicle control design. Several elements of modelling process and path tracking control are examined, including the vehicle model implemented, the path tracking control algorithms used, and the criteria for evaluating the controller's performance. Path tracking control is classified into several forms based on its methodology and linearity. Vehicle models are grouped into numerous types based on the linearity and the intended behaviour to be observed. This study explores each of these strategy in terms of the applicability and disadvantages/advantages. The main challenges in the field of path tracking control are defined and future research directions are offered based on the critical reviews. Based on the entire review, a model-based controller based on a linear vehicle model and assessed with hardware-in-the-loop (HIL) is suggested. This review is aimed to serve as a starting point for determining which controllers to use in path tracking control development for an autonomous tracked vehicle. [ABSTRACT FROM AUTHOR]

In complex driving scenarios, automated vehicles should behave reasonably and respond adaptively with high computational efficiency. In this paper, a computational efficient motion planning method is proposed, which considers traffic interaction and accelerates calculation. Firstly, the behavior is decided by connecting the points on the unequally divided road segments and lane centerlines, which simplifies the decision-making process in both space and time span. Secondly, as the dynamic vehicle model with changeable longitudinal velocity is considered in the trajectory generation module, the C/GMRES algorithm is used to accelerate the calculation of trajectory generation and realize on-line solving in nonlinear model predictive control. Meanwhile, the motion of other traffic participants is more accurately predicted based on the driver's intention and kinematics vehicle model, which enables the host vehicle to obtain a more reasonable behavior and trajectory. The simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A path following control strategy for a four-wheel-independent-drive electrical vehicle (4WID-EV) based on backstepping and model predictive control is presented, which can ensure the accuracy of path following and maintain vehicle stability simultaneously. Firstly, a 2-DOF vehicle dynamic model and a path following error model are built and the desired yaw rate is obtained through backstepping. Then, a model predictive controller is adopted to track the desired yaw rate and obtain the optimal front wheel steering and external yaw moment. Meanwhile, an optimal torque distribution algorithm is carried out to allocate it to each tire. Finally, the effectiveness and superiority of the strategy is validated via CarSim–Simulink joint simulation. Results show that the strategy has higher following accuracy, smaller sideslip angle, and better yaw rate tracking. [ABSTRACT FROM AUTHOR]

This paper presents a path-following and collision avoidance system for autonomous surface vehicles based on nonlinear model predictive control. The proposed strategy is capable of following a desired path while maintaining a commanded velocity, whereas it can diverge from the references to safely perform maneuvers to elude unexpected obstacles. A nonlinear dynamic model of the vehicle is applied to predict the vehicle states into a finite horizon. Furthermore, a LiDAR sensor located at the front of the boat is employed for local object detection. Here, the high-performance optimal control framework, acados, is used to solve the optimization problem onboard. Real-time numerical simulations and field experiments demonstrate the effectiveness of the proposed approach against multiple buoys. • An NMPC-based path-following and collision avoidance system for ASVs is proposed. • 3D LiDAR is applied to detect obstacles for evasion. • An efficient implementation is achieved using the optimal control framework, acados. • Simulations and physical experiments show the effectiveness of the approach. [ABSTRACT FROM AUTHOR]