Top PDF Trajectory tracking control strategy using co-reference for rear-steered vehicle

Trajectory tracking control strategy using co-reference for rear-steered vehicle

Trajectory tracking control strategy using co-reference for rear-steered vehicle

Nopparat Seemuang, King Mongkut's University of Technology North Bangkok, Thailand Ying Chih Lai, Feng Chia University, Taiwan. Yi-Wei Chen, Air Force Institute of Technology, Taiwan A[r]

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A Distance-Reduction Trajectory Tracking Control Algorithm for a Rear-Steered AGV

A Distance-Reduction Trajectory Tracking Control Algorithm for a Rear-Steered AGV

This paper presents a Lyapunov-based switched trajectory tracking control design for a rear-steered automated guided AGV (AGV). Given a moving reference whose position and orientation have to be tracked by the AGV, the main objective of the controller is to reduce AGV’s distance from the reference while adjusting its orientation. The distance reduction issue is important, especially in huge warehouses operating a group of AGVs, since the rate of AGV-to-reference distance reduction contributes to the possibility of AGV-to-AGV collision. A set of control algorithms is proposed to handle large AGV’s orientation. Simulations that show the performance of the proposed method is presented.
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Trajectory tracking control of agricultural vehicles based on disturbance test

Trajectory tracking control of agricultural vehicles based on disturbance test

The kinematics analysis of agricultural vehicles is carried out and the kinematics model shown in Figure 3 is established in the plane coordinate system. The front wheel of the model turns and rear wheel drive. In the process of work, the steering wheel and the driving wheel adjust the angle and speed by adjusting the voltage. In the whole kinematics analysis, the agricultural vehicle is regarded as a rigid body running on horizontal plane. In order to determine the agricultural vehicle’s position and attitude in the whole trajectory, the navigation coordinate is set up. The center point of the test car’s rear axle was selected as a reference point to define the position and position information of the car. Pose information is defined as (x, y, φ). In which, x, y are the axial coordinates of the vehicle’s rear axle (m); φ is the heading angle (rad); δf is the front wheel steering angle (rad); v is the center speed of the rear axle (m/s); v f is the front axle center speed (m/s);
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Trajectory Tracking of Autonomous Vehicle with the Fusion of DYC and Longitudinal–Lateral Control

Trajectory Tracking of Autonomous Vehicle with the Fusion of DYC and Longitudinal–Lateral Control

The current research of autonomous vehicle motion control mainly focuses on trajectory tracking and velocity track- ing. However, numerous studies deal with trajectory tracking and velocity tracking separately, and the yaw stability is seldom considered during trajectory tracking. In this research, a combination of the longitudinal–lateral control method with the yaw stability in the trajectory tracking for autonomous vehicles is studied. Based on the vehicle dynamics, considering the longitudinal and lateral motion of the vehicle, the velocity tracking and trajectory tracking problems can be attributed to the longitudinal and lateral control. A sliding mode variable structure control method is used in the longitudinal control. The total driving force is obtained from the velocity error in order to carry out velocity tracking. A linear time-varying model predictive control method is used in the lateral control to predict the required front wheel angle for trajectory tracking. Furthermore, a combined control framework is established to control the longitudinal and lateral motions and improve the reliability of the longitudinal and lateral direction control. On this basis, the driving force of a tire is allocated reasonably by using the direct yaw moment control, which ensures good yaw stability of the vehicle when tracking the trajectory. Simulation results indicate that the proposed control strategy is good in tracking the reference velocity and trajectory and improves the performance of the stability of the vehicle. Keywords: Autonomous vehicle, Trajectory tracking, Direct yaw moment control (DYC), Model predictive control (MPC), Longitudinal–lateral control
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Vehicle Tracking System Using GPS Tracking Technology

Vehicle Tracking System Using GPS Tracking Technology

In present days all the human beings are well known about his own time because time is precious than gold.All the persons are use a vehicle and mobile phones for his personal use. We are going to use that personal need into to use an emergency warn messaging system. In our real time clock also used to see the exact world clock time. In this wrist watch contains flexi force sensor, MEMS Accelerometer, GPS Transceiver. Sensor to detect the pressure of the human skin.MEMS Accelerometer is used to accelerates the small mechanical movement is convert to electrical signal. Sensor is used to convert the force into a digital signal. IEEE 802.15.4 protocol is used to transmit these kind of data in wireless medium. The controller also used to monitor the process of sensor and MEMS Accelerometer. To find the location using GPS Transceiver and send a warning message using GSM Technology.[1]
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Nonlinear Trajectory Tracking Control for Marine Vessels with Additive Uncertainties

Nonlinear Trajectory Tracking Control for Marine Vessels with Additive Uncertainties

A new control law for trajectory tracking in marine vessels under uncertainties was presented. To deal with the uncertainties, a new term has been incorpo- rated into the methodology presented in Serrano et al. [19]. This new approach allows reducing the effect of uncertainties in the tracking error. To tune the con- troller, the Monte Carlo experiment was used, and a cost function that depends on tracking errors was minimized. The proposed controllers are easy to im- plement, making them suitable for implementation in low-profile processors.

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Adaptive Trajectory Tracking Control of a High Altitude Unmanned Airship

Adaptive Trajectory Tracking Control of a High Altitude Unmanned Airship

Abstract—Nonlinear dynamic model of a high-altitude unmanned airship, expressed by generalized coordinate, was built. A nonlinear compensation was introduced into the control loop to linearize and decouple the nonlinear system globally. In view of the imprecisely known inertia parameters of the airship, an adaptive law was proposed based on the feedback linearization to realize asymptotic tracking of any continuous time-varying desired trajectory from an arbitrary initial condition. The stability of the closed-loop control system was proved via the use of Lyapunov stability theory. Finally, numerical simulation results demonstrate the validity and effectiveness of the proposed adaptive control law.
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Optimal Trajectory Tracking Control for a Wheeled Mobile Robot Using Fractional Order PID Controller

Optimal Trajectory Tracking Control for a Wheeled Mobile Robot Using Fractional Order PID Controller

This paper present an optimal Fractional Order PID (FOPID) controller based on Particle Swarm Optimization (PSO) for controlling the trajectory tracking of Wheeled Mobile Robot(WMR).The issue of trajectory tracking with given a desired reference velocity is minimized to get the distance and deviation angle equal to zero, to realize the objective of trajectory tracking a two FOPID controllers are used for velocity control and azimuth control to implement the trajectory tracking control. A path planning and path tracking methodologies are used to give different desired tracking trajectories. PSO algorithm is using to find the optimal parameters of FOPID controllers. The kinematic and dynamic models of wheeled mobile robot for desired trajectory tracking with PSO algorithm are simulated in Simulink-Matlab. Simulation results show that the optimal FOPID controllers are more effective and has better dynamic performance than the conventional methods.
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Bi-directional Tracking using Trajectory Segment Analysis

Bi-directional Tracking using Trajectory Segment Analysis

Direct optimization The direct optimization ap- proaches [12, 2, 7, 4] estimate the motion parameters between two neighboring frames by minimizing a deter- ministic cost function. The direct optimization approach assumes slow motion between two frames. This kind of approach is efficient but not very robust in situations with rapid sudden motion, ambiguity, and long-time occlusion. Particle filtering Condensation [10] is the first particle fil- tering [6, 11] based algorithm introduced in visual tracking. Particle filtering approximates the posterior distribution us- ing a set of “weighted particles”. The particle filtering algo- rithm has advantages on handling sudden motion and short- time occlusion. However, it often difficult to handle am- biguity or long-time occlusion. Maccormick & Black pro- posed a “probabilistic exclusion principle” [13] to address the ambiguity problem. But their approach is limited to a special observation model for contour tracking.
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PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking

PaTAVTT: A Hardware-in-the-Loop Scaled Platform for Testing Autonomous Vehicle Trajectory Tracking

It is difficult to test real autonomous vehicle under harsh conditions. Using the hardware-in-loop scaled platform to test scaled autonomous vehicle becomes an apparent alter- native. In this paper, we use the HIL platform to test the slip of scaled autonomous passing through U-turn under high-speed condition. After comparing its result with the real vehicle testing, we have proved the feasibility of using HIL scaled platform as surrogates of the real autonomous vehicle for testing automated vehicle. Note that the proposed test platform has a very high flexibility in simulating real world traffic operations, including those with purely traditional vehicles. For example, it can be used for conducting traffic capacity analysis [28–30], Long Distance Commuter lane (Qu and Wang, 2015), traffic oscillations [31, 32], traffic safety analysis [33–35], and others. At the end of this paper, we use a simple case study of high-speed U-turn to build the tracking control function. A simplified vehicle dynamics model and a trajectory tracking algorithm have been considered to build the simulation test. The experiment results demonstrate the effectivity of HIL scaled platform.
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GPS based Advanced Vehicle Tracking and Vehicle Control System

GPS based Advanced Vehicle Tracking and Vehicle Control System

The vehicle tracking system is an electronic device that tracks the vehicle‟s location. Most of the tracking systems use GPS module to locate the vehicle‟s position [1].Many systems also combines communication components such as satellite transmitters to communicate the vehicle‟s location to a remote user [2]. Google maps are used to view the vehicle‟s location. The design of the tracking system is divided into three parts; basic design, intermediate design and an advance Design. The basic design of the vehicle tracking system consists of a GSM module, a GPS module, a MCU (ATMEL), a Relay circuit and a LCD. The user sends SMS and the system responds to the user‟s request by providing the coordinates of a location in accordance to the requirements of mobile phone users through the GPRS network. The intermediate and advance design is an improvement of the basic design. There are five features introduced in the project. SMS codes are specifically assigned to each of these features. For example, if the user sends „555‟ to the tracking system. The GSM modem will receive the SMS and transmit to the MCU unit, where the SMS code will be compared against the codes stored in the library. In this project, the code ‟555‟ is assigned to find the location of a vehicle. So, the MCU will get the location from the GPS module and reply back
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Arm Robot Manipulator Design and Control for Trajectory Tracking; a Review

Arm Robot Manipulator Design and Control for Trajectory Tracking; a Review

Kinematics modeling is required for designing the motion and trajectory of the robot without considering the dynamics aspect such as force and friction. Kinematics modeling can be tested using simulation to avoid the complexity of the real system in testing the effectiveness of the proposed method. Simulation was conducted by Ceccarelli et al. 2008 utilizing the kinematic design for a manipulator by creating an algorithm for evaluating manipulator workspace [4], Lin et al. 2014 proposed an intuitive kinematic control of a robot via interface with human motion to control a robot directly teleoperated in avoiding obstacle and finishing its task [5], Reihara 2011 analyzed and solved the kinematics problem for an AdeptThree robot arm with the application of DH convention simulated in LabView [6], and Zodey et al. 2014 analyze the kinematics of a robotics gripper by simulating the capability of hand modeling, grasp definition, grasp modeling, grasp analysis and graphic to support the presentation [10].
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Vehicle Trajectory Estimation Using Spatio-Temporal MCMC

Vehicle Trajectory Estimation Using Spatio-Temporal MCMC

a 11-dimensional state space by driver temporal command parameters. This method drastically reduces the dimension of the state space, thus improving computational e ffi ciency. 2.2. Driver Command and Vehicle Priors. The driver com- mands are the steering wheel angle, and the vehicle longitu- dinal acceleration, from which we deduce the vehicle speed through integration. The experiments presented below have been conducted on a mid-velocity curve. While traveling such a curve, a light vehicle driver’s command law is commonly modelled by a trapezoid, with steering wheel angle velocities lying between 1.5 and 4 degrees per second, and with absolute longitudinal accelerations lying between 1 m · s − 2 and 3 m · s − 2 . In order to take into account a
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Tracking Control Design for Quadrotor Unmanned Aerial Vehicle

Tracking Control Design for Quadrotor Unmanned Aerial Vehicle

The model of a quadrotor unmanned aerial vehicle (UAV) is nonlinear and dynamically unstable. A flight controller design is proposed on the basis of Lyapunov stability theory which guarantees that all the states remain and reach on the sliding surfaces. The control strategy uses sliding mode with a backstepping control to perform the position and attitude tracking control. This proposed controller is simple and effectively enhance the performance of quadrotor UAV. In order to demonstrate the robustness of the proposed control method, White Gaussian Noise and aerodynamic moment disturbances are taken into account. The performance of the nonlinear control method is evaluated by comparing the performance with developed linear quadratic regulator and existing backstepping control technique and proportional-integral-derivative from the literature. The comparative performance results demonstrate the superiority and effectiveness of the proposed control strategy for the quadrotor UAV.
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Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control

Trajectory Tracking Study of Track Vehicles Based on Model Predictive Control

The electromechanical coupling system of the electrically driven track vehicle consists of an alternating current motor and a working machine. The electromagnetic system and the mechanical system interact with each other and form a complex nonlinear system. When the electromechanical performance is not suitable, the induction motor is likely to be blocked and cannot drive the crawler device. In severe cases, it may even burn out the motor [23]. Therefore, it is necessary to perform electromechanical coupling dynamics analysis when analysing and controlling a track vehicle. In the current research, many scholars have established kinematic and dynamic models of track vehicles and applied them to motion analysis [24] and [25]. However, there are few studies combining vehicle trajectory tracking control with electromechanical coupling dynamic analysis.
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Speed Tracking Control of a Vehicle Robot Driver System Using Multiple Sliding Surface Control Schemes

Speed Tracking Control of a Vehicle Robot Driver System Using Multiple Sliding Surface Control Schemes

Abstract  To overcome the drawbacks of using a traditional  proportional‐integral‐derivative (PID) control method for a  robot driver system, such as requiring preliminary offline  learning,  big  overshoot  and  large  speed  fluctuation,  a  new  method  for  speed  tracking  of  a  robot  driver  system  based  on  sliding  mode  control  is  proposed  in  this  paper.  Firstly,  the  coordinated  control  model  of  multiple  manipulators for the robot driver is built, which achieved  coordinated  control  of  the  throttle  mechanical  leg,  clutch  mechanical leg, brake mechanical leg and shift mechanical  arm  for  the  robot  driver.  On  the  basis  of  this,  a  speed  tracking sliding mode controller for a vehicle robot driver  is designed using the method of multiple sliding surfaces  design, and the variable structure control laws of throttle  and  brake  are  designed  respectively,  which  realize  the  speed tracking of the given driving test cycle. Experimental  results  demonstrate  that  compared  with  the  PID  control  method,  the  proposed  method  can  obviously  reduce  the  overshoot  of  vehicle  speed  tracking  control  and  greatly  improve  the  accuracy  of  vehicle  speed  tracking.  The  vehicle  speed  tracking  accuracy  stays  within  a  tolerance  band  of  ±2  km/h,  which  meets  the  requirements  of 
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Flatness based tracking control of a manoeuvrable vehicle : the Car

Flatness based tracking control of a manoeuvrable vehicle : the Car

We study the tracking control of a very manoeuvrable vehicle aimed at autonomous tasks. Two models are used and are shown to be flat; this property is then used to obtain open loop controls. The study is complemented by a stabilization around the desired trajectory.

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Robust hovering and trajectory tracking control of a quadrotor helicopter using acceleration feedback and a novel disturbance observer

Robust hovering and trajectory tracking control of a quadrotor helicopter using acceleration feedback and a novel disturbance observer

In this work, robust trajectory tracking control of a quadrotor subject to external disturbances is developed using angular acceleration feedback. The hierarchical control structure is used as a control framework. Acceleration based disturbance observer integrated with PID controllers is designed for the positional dynamics of the quadrotor where linear acceleration signals provide better stiffness against the disturbance forces. For attitude control, a nested angular position, velocity and acceleration control structure is employed where PID and PI controllers are used. In order to get reliable angular position, velocity and acceleration signals, an estimation algorithm based on the cascaded structure of extended and classical Kalman filters is utilized. Furthermore, in this work, a nonlinear optimization technique is used to obtain the reference attitude angles form command signals generated from the high-level control of the hierarchical control structure. Unlike analytical method for calculating the reference attitude angles where nonsmooth and large Euler angles might be obtained, the constrained nonlinear optimization technique provides smooth and desired bounded values. Also in the analytical approach, the desired yaw angle (ψ) needs to be fixed to some value (ψ ∗ ), but in case of the proposed method, yaw angle need not be constant. The efficiency of the proposed control method is tested on a high fidelity model of the quadrotor where sensor bias and noise in measurements are also taken into account when 3-D circular helix type trajectory is considered. Results are compared with a
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Robust Backstepping Sliding Mode Control with L2-Gain Performance for Reference Input Wheel Slip Tracking of Vehicle

Robust Backstepping Sliding Mode Control with L2-Gain Performance for Reference Input Wheel Slip Tracking of Vehicle

especially in traffic jams or long-distance driving cir- cumstances. However, the wheel slip control is the basis of active safety control systems and intelligent driver assistance systems. For instance, the anti-lock brake system (ABS) regulates the slip of each wheel at its optimum value to prevent it from locking during braking, such that the shortest stopping distance is achieved and the capability of directional stability and steer-handling is maintained [4]. The electronic stability program (ESP) may produce additional yaw moment by commanding the target slip of one or two wheels to prevent vehicle from spinning and drifting out of lane [29]. Finally, the adaptive cruise control system (ACC) can follow target speed or forward ve- hicle at the desired safety headway distance by com- manding the target slip of the wheels and the target torque of the power system [16]. As a consequence, de- signing the wheel slip controller has important theo- retical and practical significance for active safety con- trol systems and intelligent driver assistance systems. In recent years, many control approaches which are robust against system uncertainty and external dis- turbance have been proposed for the wheel slip con- trol due to the modeling errors, the measurement or estimation errors, and the changing of external en- vironment conditions of the wheel dynamic system, such as sliding mode control [23], hybrid control [25] and fuzzy control [13], etc. Johansen et al. [9] established the speed-dependent nominal linearized slip model with a perturbation term as a basis for the wheel slip control, and utilized gain-scheduled LQR approach to design the gain matrices of the control- ler. Pasillas-Lépine [19] adopted wheel deceleration logic-based switching and wheel dynamic model to design the five-phase anti-lock brake algorithm, and proved the existence and stability of limit cycles by the Poincaré map. Hsu [7] proposed an intelligent exponential sliding-mode control strategy for ABS, and a functional recurrent fuzzy neural network uncertainty estimator was designed to reduce the chattering of the exponential sliding-mode control strategy by approximating and compensating the unknown nolinear term of ABS dynamics on-line. Jing et al. [8] presented a switched control strategy for the anti-lock brake system and then analyzed the stability condition of the closed-loop system by Lyapunov-based method in the Filippov frame- work. The proposed control strategy in [7-9, 19] may
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Global trajectory tracking for underactuated VTOL aerial vehicles using a cascade control paradigm

Global trajectory tracking for underactuated VTOL aerial vehicles using a cascade control paradigm

been proposed. In [16], almost-global stability results are achieved by considering geometric methods and then applied to the control of a quadrotor aerial vehicle. Backstepping control design has been proposed in [17] in order to perform aggressive maneuvers by considering the dynamics of a small-scale helicopter. A global stabilizing controller based on synergistic Lyapunov functions has appeared in [18]. In [19], [20], inner-outer loop control strategies have been employed to stabilize the dynamical model of a miniature helicopter. The proposed methodology takes into account for the feedback interconnection between the inner attitude and the outer position control loops. More recently, a survey describing feedback control design for under-actuated VTOL systems has appeared in [21].
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