Longitudinal Motion Based Lightweight Vehicle Payload Parameter Real-Time Estimations

2012 ◽  
Vol 135 (1) ◽  
Author(s):  
Xiaoyu Huang ◽  
Junmin Wang
Keyword(s):  
Real Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Location Estimation ◽  
Torque Control ◽  
Recursive Least Squares ◽  
Longitudinal Motion ◽  
Unknown Parameters ◽  
Payload Mass ◽  
Planar Location

This paper proposes a longitudinal motion based payload parameter estimator (PPE) design for four-wheel-independently driven lightweight vehicles (LWVs), whose dynamics and control are substantially affected by their payload variations due to the LWVs' significantly reduced sizes and weights. Accurate and real-time estimation of payload parameters, including payload mass and its onboard planar location, will be helpful for LWV control (particularly under challenging driving conditions) and load monitoring. The proposed estimation method consists of three steps in sequential: tire effective radius identification for undriven wheels at constant speed driving; payload mass estimation during acceleration–deceleration period; and payload planar location estimation (PPLE). The PPLE is divided into two parts: a tire nominal normal force estimator (NNFE) based on a recursive least squares algorithm using signals generated by the redundant inputs, and a parameter calculator combining these estimated nominal normal forces. The prototype LWV is a lightweight electric ground vehicle (EGV) with separable torque control of the four wheels enabled by four in-wheel motors, which allow redundant input injections in the designed maneuvers. Experimental results obtained on an EGV road test show that the proposed PPE is capable of accurately estimating payload parameters, and it is independent of other unknown parameters such as tire-road friction coefficient.

Payload Parameter Real-Time Estimation for Lightweight Vehicles

2011 ◽  
Author(s):  
Xiaoyu Huang ◽  
Junmin Wang
Keyword(s):  
Real Time ◽  
Estimation Method ◽  
Load Condition ◽  
Time Estimation ◽  
Torque Control ◽  
Recursive Least Squares ◽  
The Road ◽  
Dynamics And Control ◽  
Real Time Estimation ◽  
Tire Forces

This paper proposes a payload parameter estimation method for lightweight vehicles (LWVs), whose dynamics and control are substantially affected by their payload variations due to the LWVs’ significantly reduced sizes and weights. Accurate and real-time estimation of payload parameters, including payload mass and its onboard planar location, will be helpful for controller designs and load condition monitoring. The proposed payload parameter estimator (PPE) is divided into two parts: tire nominal normal force estimator (NNFE) based on a recursive least squares (RLS) algorithm using signals measured from LWV constant speed maneuvers, and parameter calculator based on estimated nominal normal forces. The prototype LWV is an electric ground vehicle with separable torque control of the four wheels by in-wheel motors, which allow redundant input injections in the designed maneuvers. Simulation results, based on a CarSim® model, show that the proposed PPE is capable of accurately and quickly estimating payload parameters, and is independent of the road condition as long as the tire forces are kept within their linear ranges.

scholarly journals Real-Time Estimation of Glenohumeral Joint Rotation Center With Cable-Driven Arm Exoskeleton (CAREX)—A Cable-Based Arm Exoskeleton

2013 ◽  
Vol 6 (1) ◽  
Author(s):  
Ying Mao ◽  
Xin Jin ◽  
Sunil K. Agrawal
Keyword(s):  
Real Time ◽  
Glenohumeral Joint ◽  
Kinematic Model ◽  
Estimation Method ◽  
Time Estimation ◽  
Estimation Algorithm ◽  
Joint Rotation ◽  
Rotation Center ◽  
Arm Rehabilitation ◽  
Novel Approach

In the past few years, the authors have proposed several prototypes of a Cable-driven upper ARm EXoskeleton (CAREX) for arm rehabilitation. One of the assumptions of CAREX was that the glenohumeral joint rotation center (GH-c) remains stationary in the inertial frame during motion, which leads to inaccuracy in the kinematic model and may hamper training performance. In this paper, we propose a novel approach to estimate GH-c using measurements of shoulder joint angles and cable lengths. This helps in locating the GH-c center appropriately within the kinematic model. As a result, more accurate kinematic model can be used to improve the training of human users. An estimation algorithm is presented to compute the GH-c in real-time. The algorithm was implemented on the latest prototype of CAREX. Simulations and preliminary experimental results are presented to validate the proposed GH-c estimation method.

A Method for Real-Time Estimating Pose of Large Aircraft Component in Process of Pose Adjustment Based on the Unscented Kalman Filter

2012 ◽  
Vol 442 ◽  
pp. 251-255
Author(s):  
Zheng Ying
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Unscented Kalman Filter ◽  
Kinematic Model ◽  
Estimation Method ◽  
Time Estimation ◽  
Measuring Point ◽  
Aircraft Component ◽  
Forward Kinematic ◽  
Large Aircraft

To estimate the pose of large aircraft component in pose adjustment quickly and accurately, a real-time estimation method based on Unscented Kalman filter (UKF) is proposed. Firstly, in the process of the aircraft component adjustment, a rough value of aircraft component’s pose is acquired by using forward kinematic model and the displacement of positioners on real time. Then, position of a measuring point fixed on aircraft component is obtained by a laser tracker. At last, UKF is employed to integrate the previous rough value and the measuring point position for evaluating the accurate pose of aircraft component. Numerical simulation results show that the presented method is achieved easily, calculated fast and high accurate.

A Study of Control Methods for the Braking System on Passenger Multiple Units

2004 ◽  
Author(s):  
Masanobu Nankyo
Keyword(s):  
Real Time ◽  
Control Method ◽  
Estimation Method ◽  
Time Estimation ◽  
Brake System ◽  
Friction Forces ◽  
Brake Shoe ◽  
Non Linear ◽  
Linear Elements ◽  
Real Time Estimation

As well known, the mechanical (friction, pneumatic) brake system on trains contains some non-linear elements. So it has been difficult to control the speed or acceleration of trains according to desired patterns. This paper reviews our research on the control method of the physical performance of train running such as acceleration (deceleration) by mechanical braking devices. One of our approaches is the introduction of the feedback control into the brake control system. Mathematical models of non-linear elements in the brake system and some effective methods of controller design are proposed with both simulation and experimental results. Another approach is the real time estimation of the friction forces between a brake shoe and a wheel tread. Friction has severe non-linearity; however it can not be measured easily on running trains. We propose the introduction of the onboard real-time estimation method of friction coefficients using the speed information which can be obtained easily in the existing brake system.

Real-Time Structure Shape Estimation Using Distributed Fiber Bragg Grating Sensors

2009 ◽  
Author(s):  
Hong-Il Kim ◽  
Lae-Hyong Kang ◽  
Jae-Hung Han
Keyword(s):  
Real Time ◽  
High Speed ◽  
Estimation Method ◽  
Time Estimation ◽  
Flexible Beam ◽  
Beam Model ◽  
Shape Estimation ◽  
Rotating Beam ◽  
Structural Shape ◽  
Beam Structures

One of the emerging issues in lightweight aerospace structures is the real-time estimation of the structural shape changes. In order to reconstruct the structure shape based on the measured strain data at multiple points, the displacement-strain transformation (DST) method has been used. In this study, simulation for a 1-D beam model was performed to verify the DST method. Bending displacements for various excitation conditions were successfully estimated using the simulated strain signals. Strain sensor positions were optimized by the minimization of the condition number of the DST matrix for the 1-D beam. We further expanded the shape estimation method to rotating beams. A rotating flexible beam experimental model was constructed and a numerical simulation model was also prepared. Multiplexed four FBG sensors were fabricated and attached to the rotating beam structures to measure strains at four different locations. The experimental device has an optical rotary coupler, and the sensor signals are transmitted through the optical rotary coupler. Bending displacements were estimated based on the FBG signals and compared with directly measured displacement data using photographs taken by a high-speed camera. This shows the validity of the proposed shape estimation technique based on DST matrix for rotating beam structures.

scholarly journals Online Real-Time Estimation of Response Time for Periodic Messages in Controller Area Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Dahui Gao ◽  
Qingfeng Wang ◽  
Yong Lei ◽  
Zheng Chen ◽  
Linying Shangguan
Keyword(s):  
Response Time ◽  
Real Time ◽  
Networked Control Systems ◽  
Estimation Method ◽  
Time Estimation ◽  
Hybrid Vehicles ◽  
Delay Compensation ◽  
Controller Area Networks ◽  
Additional Equipment ◽  
Real Time Estimation

The Controller Area Networks (CAN) are widely used in industrial Networked Control Systems (NCSs), such as construction machineries, hybrid vehicles, robotics, and other applications. The message response time (MRT) or communication delay is the main issue to degrade the performance of CAN-based NCSs since its exact value is time-varying and unpredictable. The online acquisition of exact MRT can be quite helpful for the delay compensation of NCSs. However, since the clocks on different nodes are asynchronous, the MRT acquisition in CAN is a challenging work. The current delay acquisition methods for asynchronous systems are not suitable for the delay compensation in CAN-based NCSs because they either increase the bus load of CAN or cannot acquire the exact MRT in real time. In this paper, we propose a novel online real-time MRT estimation method for periodic CAN messages based on the analysis of message traces on CAN bus. The proposed method can estimate the exact MRT of the received message instance in real time without increasing the bus load and can be conveniently embedded into the CAN nodes without requiring additional equipment. In order to validate the proposed method, practical experiments are carried out and the experimental results show that the proposed method can effectively estimate the exact MRT of periodic CAN messages.

Engine Torque Estimation Based on BP Neural Network

2011 ◽  
Vol 403-408 ◽  
pp. 2848-2851
Author(s):  
Kai Sheng Huang ◽  
Dong Liang Wang ◽  
Zhi Hua Lin ◽  
Xiang Rui Zeng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Estimation Method ◽  
Time Estimation ◽  
Torque Control ◽  
Steady Condition ◽  
Estimation Model ◽  
Engine Torque ◽  
Unsteady Condition ◽  
Torque Estimation

Engine torque estimation function is the base of engine torque control. This paper establishes the model for engine torque estimation respectively under steady condition and unsteady condition based on BP Neural network, and develops a new engine torque real-time estimation method. The experiment results under steady condition and unsteady condition show that the engine torque estimation model can estimate the engine output torque and the precision is remarkable.

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