scholarly journals Trajectory tracking method based on the circulation of feasible path planning

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252542
Author(s):  
Yi Yu ◽  
Peng Han
Keyword(s):  
Path Planning ◽  
Trajectory Tracking ◽  
Control Method ◽  
Tracking Error ◽  
Unmanned Vehicles ◽  
Path Generation ◽  
Trajectory Tracking Control ◽  
Core System ◽  
Tracking Method ◽  
Feasible Path

The control method is the central point of the unmanned vehicles. As the core system to guarantee the properties of self-decision and trajectory tracking of the unmanned vehicles, a new kind of trajectory tracking method based on the circulation of feasible path planning for the unmanned vehicles are proposed in this article which considered the dynamics and kinematics characteristics of vehicles. The multi-trace-points cooperative trajectory tracking control strategy on the basis of the circulation of feasible path generation method is proposed and the lateral controller is designed for trajectory tracking. The process of feasible path generation is conducted once the tracking error exceeded. A simulation platform of the trajectory tracking simulation of unmanned vehicles is built considering the mechanical properties of system elements and the mechanical characteristics. Finally, the proposed trajectory tracking method is verified. The tracking error would be reduced to make sure the vehicles move along the pre-set virtual track.

Real-Time Path-Generation and Path-Following Using an Interoperable Multi-Agent Framework

2018 ◽  
Vol 06 (04) ◽  
pp. 231-250 ◽  
Author(s):  
Willson Amalraj Arokiasami ◽  
Prahlad Vadakkepat ◽  
Kay Chen Tan ◽  
Dipti Srinivasan
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Collision Avoidance ◽  
Tracking Error ◽  
Unmanned Vehicles ◽  
Path Generation ◽  
Ground Vehicles ◽  
Agent Architectures ◽  
Time Path ◽  
Multi Agent

Autonomous unmanned vehicles are preferable in patrolling, surveillance and, search and rescue missions. Multi-agent architectures are commonly used for autonomous control of unmanned vehicles. Existing multi-robot architectures for unmanned aerial and ground robots are generally mission and platform oriented. Collision avoidance, path-planning and tracking are some of the fundamental requirements for autonomous operation of unmanned robots. Though aerial and ground vehicles operate differently, the algorithms for obstacle avoidance, path-planning and path-tracking can be generalized. Service Oriented Interoperable Framework for Robot Autonomy (SOIFRA) proposed in this work is an interoperable multi-agent framework focused on generalizing platform independent algorithms for unmanned aerial and ground vehicles. SOIFRA is behavior-based, modular and interoperable across unmanned aerial and ground vehicles. SOIFRA provides collision avoidance, and, path-planning and tracking behaviors for unmanned aerial and ground vehicles. Vector Directed Path-Generation and Tracking (VDPGT), a vector-based algorithm for real-time path-generation and tracking, is proposed in this work. VDPGT dynamically adopts the shortest path to the destination while minimizing the tracking error. Collision avoidance is performed utilizing Hough transform, Canny contour, Lucas–Kanade sparse optical flow algorithm and expansion of object-based time-to-contact estimation. Simulation and experimental results from Turtlebot and AR Drone show that VDPGT can dynamically generate and track paths, and SOIFRA is interoperable across multiple robotic platforms.

scholarly journals Path Planning and Trajectory Tracking Strategy of Autonomous Vehicles

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Peng Han ◽  
Bingyu Zhang
Keyword(s):  
Control System ◽  
Trajectory Tracking ◽  
Control Strategy ◽  
Public Transportation ◽  
Tracking Error ◽  
Transportation System ◽  
Path Generation ◽  
Articulated Vehicle ◽  
Public Transportation System ◽  
Feasible Path

With the development of global urbanization and the construction of regional urbanization, residents around urban cities are increasingly making demands on urban public transportation system. A new kind of modern public transportation vehicle named Multi-Articulated Guided Vehicle based on Virtual Track (MAAV-VT) with the advantages of beautiful, smart energy conservation and environmental protection is proposed in this paper, which aims at optimizing the public transportation system between and within urban areas. Therefore, concentrating on the general design and control strategy, the main contents of this paper are as follows. At first, the design concepts and key technologies of MAAV-VT are introduced. It is the fusion of urban rail transit operation mode and advanced automotive technologies, which have the characteristics of 100% low-floor, medium to high velocity, medium to big capacity, and low construction cost. Then, as the core subsystem, to guarantee the properties of self-guiding and trajectory tracking of the new vehicle, this paper is focused on the control system based on the dynamics and kinematics model of the whole multi-articulated vehicle. The multi-trace-points cooperative trajectory tracking control strategy on the basis of the circulation of feasible path generation method is proposed and the lateral controller is designed for trajectory tracking. The process of feasible path generation is conducted once the tracking error exceeded. A simulation platform is built considering the mechanical properties of each vehicle element and the characteristic of articulated mechanism. Finally, the function of control system is validated. The tracking error of each vehicle elements would be reduced to make sure the whole multi-articulated vehicle moves along the preset virtual track.

scholarly journals Prescribed performance adaptive fault-tolerant trajectory tracking control for an ocean bottom flying node

2019 ◽  
Vol 16 (3) ◽  
pp. 172988141984194 ◽  
Author(s):  
Hongde Qin ◽  
Zheyuan Wu ◽  
Yanchao Sun ◽  
Yushan Sun
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
Tracking Error ◽  
Approximation Error ◽  
Ocean Current ◽  
Ocean Bottom ◽  
Model Uncertainties ◽  
Trajectory Tracking Control ◽  
Prescribed Performance

The ocean bottom flying node is a novel autonomous underwater vehicle that explores the oil and gas resources in deep water. Thousands of the ocean bottom flying nodes track different predefined trajectories arriving at target points in a small ocean area, respectively. A class of prescribed performance adaptive trajectory tracking control method is investigated for the ocean bottom flying node trajectory tracking problem with ocean current disturbances, model uncertainties as well as thruster faults. Based on a predefined performance function and an error transformation, the ocean bottom flying node trajectory tracking error is restricted to prespecified bounds to ensure a desired transient and steady response. Radial basis function neural network is used to approximate the general uncertainty caused by ocean current disturbances, model uncertainties, and thruster faults. Further, the upper bound of approximation error is estimated by an adaptive law. Using the adaptive laws, we propose a prescribed performance adaptive trajectory tracking controller. The simulation examples on an ocean bottom flying node system show that the proposed control scheme can compensate for the effect of the general uncertainty while obtaining the fast transient process and expected trajectory tracking accuracy.

scholarly journals Fuzzy swarm trajectory tracking control of unmanned aerial vehicle

2020 ◽  
Vol 7 (4) ◽  
pp. 435-447 ◽  
Author(s):  
Boumediene Selma ◽  
Samira Chouraqui ◽  
Hassane Abouaïssa
Keyword(s):  
Trajectory Tracking ◽  
Degrees Of Freedom ◽  
Fuzzy Inference ◽  
Control Method ◽  
Tracking Error ◽  
Vertical Plane ◽  
Pso Algorithm ◽  
Trajectory Tracking Control ◽  
Inference System ◽  
Quadrotor Uav

Abstract Accurate and precise trajectory tracking is crucial for unmanned aerial vehicles (UAVs) to operate in disturbed environments. This paper presents a novel tracking hybrid controller for a quadrotor UAV that combines the robust adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) algorithm. The ANFIS-PSO controller is implemented to govern the behavior of three degrees of freedom quadrotor UAV. The ANFIS controller allows controlling the movement of UAV to track a given trajectory in a 2D vertical plane. The PSO algorithm provides an automatic adjustment of the ANFIS parameters to reduce tracking error and improve the quality of the controller. The results showed perfect behavior for the control law to control a UAV trajectory tracking task. To show the effectiveness of the intelligent controller, simulation results are given to confirm the advantages of the proposed control method, compared with ANFIS and PID control methods.

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

scholarly journals Trajectory Tracking Control for WMRs with the Time-Varying Longitudinal Slippage Based on a New Adaptive SMC Method

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Zhi Li ◽  
Bo You ◽  
Liang Ding ◽  
Haibo Gao ◽  
Fengxiang Huang
Keyword(s):  
Trajectory Tracking ◽  
Control Method ◽  
Sliding Mode ◽  
Kinematic Model ◽  
Time Varying ◽  
Wheeled Mobile Robots ◽  
Trajectory Tracking Control ◽  
Complete Control ◽  
Loop Control ◽  
Adaptive Smc

Wheeled mobile robots (WMRs) in real complex environments such as on extraterrestrial planets are confronted with uncertain external disturbances and strong coupling of wheel-ground interactions while tracking commanded trajectories. Methods based on sliding mode control (SMC) are popular approaches for these situations. Traditional SMC has some potential problems, such as slow convergence, poor robustness, and excessive output chattering. In this paper, a kinematic-based feed-forward control model is designed for WMRs with longitudinal slippage and applied to the closed-loop control system for active compensation of time-varying slip rates. And a new adaptive SMC method is proposed to guide a WMR in trajectory tracking missions based on the kinematic model of a general WMR. This method combines the adaptive control method and a fast double-power reaching law with the SMC method. A complete control loop with active slip compensation and adaptive SMC is thus established. Simulation results show that the proposed method can greatly suppress chattering and improve the robustness of trajectory tracking. The feasibility of the proposed method in the real world is demonstrated by experiments with a skid-steered WMR on the loose-soil terrain.

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