scholarly journals Trajectory Tracking Control for Intelligent Vehicles Based on Cut-In Behavior Prediction

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2932
Author(s):  
Chongpu Chen ◽  
Jianhua Guo ◽  
Chong Guo ◽  
Xiaohan Li ◽  
Chaoyi Chen
Keyword(s):  
Trajectory Planning ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
Control Process ◽  
Intelligent Vehicles ◽  
Behavior Prediction ◽  
Trajectory Tracking Control ◽  
Distance Model ◽  
The Subject

For intelligent vehicles, trajectory tracking control is of vital importance. However, due to the cut-in possibility of adjacent vehicles, trajectory planning of intelligent vehicles is challenging. Therefore, this paper proposes a trajectory tracking control method based on cut-in behavior prediction. A method of cut-in intention recognition is adopted to judge the possibility of adjacent vehicle and the driver preview model is used to predict the trajectory of the cut-in vehicle. The three driving scenarios are divided to manage trajectory planning under different cut-in behaviors. At the same time, the safety distance model is established as the basis for scene conversion. Taking the predicted trajectory of the cut-in vehicle as a reference, the model predictive control (MPC) method is used to plan and control the driving trajectory of the subject vehicle, so as to realize the coordinated control of the subject vehicle and the cut-in vehicle. Finally, the simulation shows that the subject vehicle can effectively recognize the cut-in intention of the adjacent vehicle and predict its trajectory. Facing with the cut-in vehicle, the subject vehicle can take appropriate control actions in advance to ensure the safety. Finally, a smoother coordinate control process is obtained between the subject vehicle and the cut-in vehicle.

Robust finite-time trajectory tracking control for a space manipulator with parametric uncertainties and external disturbances

2021 ◽  
pp. 095441002110147
Author(s):  
Qijia Yao
Keyword(s):  
Trajectory Tracking ◽  
Finite Time ◽  
Tracking Control ◽  
Disturbance Observer ◽  
Control Method ◽  
Parametric Uncertainties ◽  
External Disturbances ◽  
Trajectory Tracking Control ◽  
Space Manipulator ◽  
Tracking Controller

Space manipulator is considered as one of the most promising technologies for future space activities owing to its important role in various on-orbit serving missions. In this study, a robust finite-time tracking control method is proposed for the rapid and accurate trajectory tracking control of an attitude-controlled free-flying space manipulator in the presence of parametric uncertainties and external disturbances. First, a baseline finite-time tracking controller is designed to track the desired position of the space manipulator based on the homogeneous method. Then, a finite-time disturbance observer is designed to accurately estimate the lumped uncertainties. Finally, a robust finite-time tracking controller is developed by integrating the baseline finite-time tracking controller with the finite-time disturbance observer. Rigorous theoretical analysis for the global finite-time stability of the whole closed-loop system is provided. The proposed robust finite-time tracking controller has a relatively simple structure and can guarantee the position and velocity tracking errors converge to zero in finite time even subject to lumped uncertainties. To the best of the authors’ knowledge, there are really limited existing controllers can achieve such excellent performance under the same conditions. Numerical simulations illustrate the effectiveness and superiority of the proposed control method.

Dynamic trajectory-tracking control method of robotic transcranial magnetic stimulation with end-effector gravity compensation based on force sensors

2018 ◽  
Vol 45 (6) ◽  
pp. 722-731
Author(s):  
ZeCai Lin ◽  
Wang Xin ◽  
Jian Yang ◽  
Zhang QingPei ◽  
Lu ZongJie
Keyword(s):  
Neural Networks ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Magnetic Stimulation ◽  
Control Method ◽  
Gravity Compensation ◽  
Trajectory Tracking Control ◽  
End Effector ◽  
Content Type ◽  
Dynamic Trajectory

Purpose This paper aims to propose a dynamic trajectory-tracking control method for robotic transcranial magnetic stimulation (TMS), based on force sensors, which follows the dynamic movement of the patient’s head during treatment. Design/methodology/approach First, end-effector gravity compensation methods based on kinematics and back-propagation (BP) neural networks are presented and compared. Second, a dynamic trajectory-tracking method is tested using force/position hybrid control. Finally, an adaptive proportional-derivative (PD) controller is adopted to make pose corrections. All the methods are designed for robotic TMS systems. Findings The gravity compensation method, based on BP neural networks for end-effectors, is proposed due to the different zero drifts in different sensors’ postures, modeling errors in the kinematics and the effects of other uncertain factors on the accuracy of gravity compensation. Results indicate that accuracy is improved using this method and the computing load is significantly reduced. The pose correction of the robotic manipulator can be achieved using an adaptive PD hybrid force/position controller. Originality/value A BP neural network-based gravity compensation method is developed and compared with traditional kinematic methods. The adaptive PD control strategy is designed to make the necessary pose corrections more effectively. The proposed methods are verified on a robotic TMS system. Experimental results indicate that the system is effective and flexible for the dynamic trajectory-tracking control of manipulator applications.

Dynamic modeling and trajectory tracking control method of segmented linkage cable-driven hyper-redundant robot

2020 ◽  
Vol 101 (1) ◽  
pp. 233-253
Author(s):  
Jianqing Peng ◽  
Wenfu Xu ◽  
Taiwei Yang ◽  
Zhonghua Hu ◽  
Bin Liang
Keyword(s):  
Dynamic Modeling ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
Trajectory Tracking Control ◽  
Redundant Robot

Group Control of Mobile Robots for More Efficient Searches – Verification of Semi-Autonomous Trajectory Tracking Motions in Irregular Ground Environment –

2018 ◽  
Vol 30 (6) ◽  
pp. 980-990
Author(s):  
Yoshikazu Ohtsubo ◽  
Morihito Matsuyama ◽  
◽  
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
Potential Method ◽  
Gravity Potential ◽  
Trajectory Tracking Control ◽  
Robot Operating System ◽  
Uneven Terrain ◽  
Disaster Area ◽  
Autonomous Search

After the occurrence of a disaster, it is critical to perform rapid and accurate searching operations in the large disaster area. It is efficient to perform such operations using multiple mobile exploration robots. Accordingly, we focus on cooperative cruising in a disaster environment and propose the trajectory tracking control method for a semi-autonomous search robot. We apply a robot operating system (ROS) to execute the trajectory tracking control using two mobile exploration robots. In this paper, we describe the trajectory tracking control using gravity potential method and the results of a cooperative cruising experiment in an uneven terrain environment.

Robust Trajectory Tracking Control of Autonomous Quad-Rotor UAV Based on Double Loop Frame

2011 ◽  
Vol 467-469 ◽  
pp. 1421-1426
Author(s):  
Zhi Cheng Hou ◽  
X. Gong ◽  
Y. Bai ◽  
Y.T. Tian ◽  
Q. Sun
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
External Disturbance ◽  
Double Loop ◽  
Trajectory Tracking Control ◽  
Stability Augmentation ◽  
Aerial Vehicle ◽  
Simulation Results ◽  
Control Quantity

This paper deals with the under-actuated characteristic of a quad-rotor unmanned aerial vehicle (UAV). By designing the double loop configuration, the autonomous trajectory tracking is realized. The model uncertainty, external disturbance and the senor noise are also taken into consideration. Then the controller is put forward in the inner loop. An optimal stability augmentation control (SAC) method is used to stabilize the horizon position and keep it away from oscillation. By calculating the nonlinear decouple map, control quantity is converted to the speeds of the four rotors. At last some simulation results and the prototype implementation prove that the control method is effective.

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