Search

To ensure that a liquid tank semi-trailer has good yaw stability and path-following performance on low-adhesion roads under turning conditions, a multi-object PID differential braking-control method is proposed, which takes the tractor yaw rate, semi-trailer yaw rate, and articulation angle as the control parameters. According to the principle of equivalence, the trammel pendulum (TP) model is used to simulate the liquid sloshing effect in the liquid tank, and the Fluent software is used to identify the key parameters of the trammel pendulum model and verify the correctness of its simulation effect. Then, a co-simulation model is built based on TruckSim and MATLAB/Simulink. Based on the simplified six degrees of freedom model and the co-simulation model of a liquid tank semi-trailer, a multi-object PID differential braking-control method is designed, and the vehicle state responses with and without control are compared when it is turning on a low-adhesion road. The simulation results show that the proposed multi-object PID differential braking-control method can effectively improve the yaw stability and path-following performance of the liquid tank semi-trailer when turning on a low-adhesion road.

In this paper, a novel path-following and obstacle avoidance control method is given for nonholonomic wheeled mobile robots (NWMRs), based on deep reinforcement learning. The model for path-following is investigated first, and then applied to the proposed reinforcement learning control strategy. The proposed control method can achieve path-following control through interacting with the environment of the set path. The path-following control method is mainly based on the design of the state and reward function in the training of the reinforcement learning. For extra obstacle avoidance problems in following, the state and reward function is redesigned by utilizing both distance and directional perspective aspects, and a minimum representative value is proposed to deal with the occurrence of multiple obstacles in the path-following environment. Through the reinforcement learning algorithm deep deterministic policy gradient (DDPG), the NWMR can gradually achieve the path it is required to follow and avoid the obstacles in simulation experiments, and the effectiveness of the proposed algorithm is verified.

This article studies the design, modeling, and implementation of a path-following algorithm as a guidance, navigation, and control (GNC) architecture for an autonomous underwater vehicle (AUV). First, a mathematical model is developed based on nonlinear equations of motion and parameter estimation techniques, including the model validation based on field test data. Then, the guidance system incorporates a line-of-sight (LOS) algorithm with a combination of position PID controllers. The GNC architecture includes a modular and multi-layer approach with an LOS-based, path-following algorithm in the AUV platform. Furthermore, the navigation used in the path-following algorithm is developed based on a predefined coverage area. Finally, this study addresses simulation and field test control scenarios to verify the developed GNC architecture.

The paper addresses path planning and path-following problems in an unknown complex environment for an underactuated autonomous underwater vehicle (AUV). The AUV is required to follow a given reference path represented as a sequence of smoothly joined lines and arcs, bypassing obstacles encountered on the path. A two-level control system is proposed with an upper level for event-driven path planning and a lower level for path-following. A discrete event system is designed to identify situations that require planning a new path. An improved waypoint guidance algorithm and a Dubins curves based algorithm are proposed to build paths that allow the AUV to avoid collision with obstacles and to return to the reference path respectively. Both algorithms generate paths that meet the minimum turning radius constraint. A robust parameter-varying controller is designed using sublinear vector Lyapunov functions to solve the path-following problem. The performance of the developed event-based control system is demonstrated in three different simulation scenarios: with a sharp-edged obstacle, with a U-shaped obstacle, and with densely scattered obstacles. The proposed scheme does not require significant computing resources and allows for easy implementation on board.