scholarly journals Research on Torque Distribution of Four-Wheel Independent Drive Off-Road Vehicle Based on PRLS Road Slope Estimation

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Hongwei Ling ◽  
Bin Huang
Keyword(s):  
Real Time ◽  
Control Strategy ◽  
Time Estimation ◽  
Estimation Algorithm ◽  
Torque Distribution ◽  
Precise Control ◽  
The Road ◽  
Time Simulation ◽  
Road Gradient ◽  
Driving Torque

In view of the high difficulty in coupling of various electric vehicle parameters, intractable parameter estimation, and unreasonable distribution of vehicle driving torque, the four-wheel hub motor is applied to drive electric vehicles, which can instantly obtain the torque and speed of the hub motor and achieve precise control of the torque of each wheel. According to the vehicle longitudinal dynamics model, a progressive RLS (PRLS) algorithm for real-time estimation of vehicle mass and road gradient is proposed. Meanwhile, by means of taking the longitudinal acceleration of the vehicle and the road gradient obtained from the estimation algorithm as the parameter of the torque distribution at the front and rear axles, a dynamic compensation and distribution control strategy of the front and rear axle torques is designed. Moreover, based on hardware-in-the-loop real-time simulation and real-vehicle tests, the effectiveness of the proposed estimation algorithm and the rationality of the real-time distribution control strategy of driving torque are verified.

Truck mass estimation method based on the on-board sensor

2020 ◽  
Vol 234 (10-11) ◽  
pp. 2429-2443
Author(s):  
Beijia Wang ◽  
Hongliang Wang ◽  
Lei Wu ◽  
Liuliu Cai ◽  
Dawei Pi ◽  
...  
Keyword(s):  
Error Correction ◽  
Estimation Error ◽  
Estimation Method ◽  
Estimation Algorithm ◽  
Correction Method ◽  
Mass Estimation ◽  
The Road ◽  
Vehicle Mass ◽  
Error Correction Method ◽  
Road Gradient

Vehicle mass estimation is the key technology to improve vehicle stability. However, the existing mass estimation accuracy is easily affected by the change of road gradient, and there are few studies on the mass estimation method of the light truck. Aiming at this problem, this paper uses sensors to measure road gradient and rear suspension deformation and proposes a sensor-based vehicle mass estimation algorithm. First, factors that affect the mass estimation are analyzed, road gradient error correction method and mass estimation error correction method are established. Besides, the suspension deformation is decoupled from the road gradient. Second, the mass estimation algorithm model was established in Matlab/Simulink platform and compared with the mass estimation iterative algorithm. Finally, the road test was carried out under various conditions, the results show that the proposed mass estimation algorithm is robust, and the accuracy of the mass estimation will not be affected by the sudden change of road gradient.

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.

Real-Time Estimation of Glenohumeral Joint Rotation Center With CAREX: A Cable-Based Arm Exoskeleton

2012 ◽  
Author(s):  
Ying Mao ◽  
Xin Jin ◽  
Sunil K. Agrawal
Keyword(s):  
Real Time ◽  
Healthy Subjects ◽  
Degrees Of Freedom ◽  
Glenohumeral Joint ◽  
Kinematic Model ◽  
Time Estimation ◽  
Estimation Algorithm ◽  
Experimental Results ◽  
Joint Rotation ◽  
Rotation Center

In the past few years, the authors have proposed several prototypes of a Cable-driven upper ARm EXoskeleton (CAREX) for arm rehabilitation. The key advantages of CAREX over conventional exoskeletons are: (i) It is nearly an order of magnitude lighter. (ii) It does not have conventional links and joints, hence does not require joint axes alignment and segment lengths adjustment. (iii) It does not limit the natural degrees-of-freedom of the upper limb. (iv) The structure of the exoskeleton is novel as the cables are routed from the proximal to the distal segments of the arm. Preliminary experimental results with CAREX on a robotic arm and on healthy subjects have demonstrated the effectiveness of the exoskeleton within “assist-as-needed” training paradigm. In this paper, we propose a novel approach to estimate the glenohumeral joint rotation center (GH-c) using measurements of shoulder joint angles and cable lengths. This helps in locating the glenohumeral joint rotation 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 which controls four degrees-of-freedom of the shoulder and elbow. Preliminary experiments were performed on two healthy subjects under two different scenarios: (i) GH-c was assumed to be a fixed point and (ii) GH-c was estimated using the proposed algorithm. Experimental results are presented to compare the two scenarios.

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