scholarly journals A Steering-Following Dynamic Model with Driver’s NMS Characteristic for Human-Vehicle Shared Control

2020 ◽  
Vol 10 (7) ◽  
pp. 2626 ◽  
Author(s):  
Hanbing Wei ◽  
Yanhong Wu ◽  
Xing Chen ◽  
Jin Xu
Keyword(s):  
Dynamic Model ◽  
Unscented Kalman Filter ◽  
Autonomous Vehicle ◽  
Least Square ◽  
Experimental Results ◽  
Shared Control ◽  
Dynamic State ◽  
Dynamic Mode ◽  
Identification Algorithm ◽  
Control Authority

For investigating driver characteristic as well as control authority allocation during the process of human–vehicle shared control (HVSC) for an autonomous vehicle (AV), a HVSC dynamic mode with a driver’s neuromuscular (NMS) state parameters was proposed in this paper. It takes into account the driver’s NMS characteristics such as stretch reflection and reflex stiffness. By designing a model predictive control (MPC) controller, the vehicle’s state feedback and driver’s state are incorporated to construct the HVSC dynamic model. For the validation of the model, a field experiment was conducted. The vehicle state signals are collected by V-BOX, and the driver’s state signals are obtained with the electromyography instrument. Subsequently, the hierarchical least square (HLS) parameter identification algorithm was implemented to identify the parameters of the model based on the experimental results. Moreover, the Unscented Kalman Filter (UKF) was utilized to estimate the important NMS parameters which cannot be measured directly. The experimental results showed that the model we proposed has excellent accuracy in characterizing the vehicle’s dynamic state and estimating the driver’s NMS parameter. This paper will serve as a theoretical basis for the new control strategy allocation between human and vehicle for L3 class AVs.

Human-vehicle dynamic model with driver’s neuromuscular characteristic for shared control of autonomous vehicle

2020 ◽  
pp. 095440702097710
Author(s):  
Wei Hanbing ◽  
Wu Yanhong ◽  
Chen Xing ◽  
Xu Jin ◽  
Rahul Sharma
Keyword(s):  
Driving Simulator ◽  
Unscented Kalman Filter ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Least Square ◽  
Shared Control ◽  
Driving Safety ◽  
Dynamic State ◽  
Commercial Application ◽  
Steering Torque

Over a long period of time, the fully autonomous vehicle is far from commercial application. The concept of ‘human-vehicle shared control (HVSC)’ provides a promising solution to enhance autonomous driving safety. In order to characterize the evolution of the driver’s feature in the process of HVSC, a dynamics model of HVSC with the driver’s neuromuscular characteristic is proposed in this paper. It takes into account the driver’s neuromuscular characteristics, such as stretch reflection, feedback stiffness, etc. By designing a model predictive control (MPC) controller, the feedback of the vehicle’s state and steering torque is constructed. For validation of the model, driving simulation has been conducted in our table-based driving simulator. The vehicle state and the surface electromyography of the driver’s arm working muscle group are collected simultaneously. Subsequently, the hierarchical least square (HLS) parameter identification and unscented Kalman filter (UKF) observer is used to identify and estimate the important characteristic parameters respectively based on the experimental results. The comparisons show that the HVSC can characterize the vehicle’s dynamic state and the driver’s personalized characteristic can be identified by HLS. This paper will serve as a theoretical basis of control strategy allocation between the human and vehicle during shared control for L3 class autonomous vehicle.

A simplified dynamic model with driver’s NMS characteristic for human-vehicle shared control of autonomous vehicle

2021 ◽  
pp. 095440702110189
Author(s):  
Huateng Wu ◽  
Hanbing Wei ◽  
Zheng Liu ◽  
Jin Xu
Keyword(s):  
Dynamic Model ◽  
Autonomous Vehicles ◽  
Large Scale ◽  
Autonomous Vehicle ◽  
Lyapunov Stability Theory ◽  
Shared Control ◽  
Steering Wheel ◽  
Adaptive Genetic Algorithm ◽  
Model Parameters ◽  
Control Authority

Since the large-scale application of fully autonomous vehicles is difficult to be commercialized in the short term, human-vehicle shared control (HVSC) is a promising technique. To implement the control authority allocation and observe the driver characteristic, it is essential to develop an efficient HVSC dynamic model with the driver’s neuromuscular characteristic (NMS). To further our previous research, a simplified HVSC dynamic model is proposed in this paper. This model simplifies the non-critical NMS parameters such as muscle spindle feedback, which has no significant feedback effect while retaining essential NMS characteristics such as stretch reflection and intrinsic properties. The model consists of a model predictive controller (MPC) coupled with a driver NMS model and a 2 DOF vehicle model. The stability is proved by Lyapunov stability theory. Moreover, a field experiment was conducted for validation of the model. The V-Box is utilized to measure the vehicle’s state signals, such as steering wheel angle and pedal stroke. Subsequently, the adaptive genetic algorithm (AGA) is employed to identify the model parameters based on the experimental results. The comparison between the experiment and the model output shows that the proposed model can accurately represent the driver’s NMS characteristics and vehicle dynamic parameters. This paper will serve as a theoretical basis for the control authority allocation for L3 class autonomous vehicles.

scholarly journals Dynamic Model and Inverse Kinematic Identification of a 3-DOF Manipulator Using RLSPSO

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 416 ◽  
Author(s):  
Josias Batista ◽  
Darielson Souza ◽  
Laurinda dos Reis ◽  
Antônio Barbosa ◽  
Rui Araújo
Keyword(s):  
Dynamic Model ◽  
Inverse Kinematics ◽  
Lagrange Equation ◽  
Computational Cost ◽  
Dynamic Parameter ◽  
Search Space ◽  
Least Square ◽  
Identification Accuracy ◽  
Identification Algorithm ◽  
Recursive Least Square

This paper presents the identification of the inverse kinematics of a cylindrical manipulator using identification techniques of Least Squares (LS), Recursive Least Square (RLS), and a dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by RLS (RLSPSO). A helical trajectory in the cartesian space is used as input. The dynamic model is found through the Lagrange equation and the motion equations, which are used to calculate the torque values of each joint. The torques are calculated from the values of the inverse kinematics, identified by each algorithm and from the manipulator joint speeds and accelerations. The results obtained for the trajectories, speeds, accelerations, and torques of each joint are compared for each algorithm. The computational costs as well as the Multi-Correlation Coefficient ( R 2 ) are computed. The results demonstrated that the identification accuracy of RLSPSO is better than that of LS and PSO. This paper brings an improvement in RLS because it is a method with high complexity, so the proposed method (hybrid) aims to improve the computational cost and the results of the classic RLS.

scholarly journals Joints dynamic identification and modeling based on FRFs data

2018 ◽  
Vol 189 ◽  
pp. 01010
Author(s):  
Guanhua Dong ◽  
Wei Wu ◽  
Jianhui Zhou
Keyword(s):  
Frequency Response ◽  
Dynamic Model ◽  
Dynamic Properties ◽  
Dynamic Stiffness ◽  
Least Square Method ◽  
Least Square ◽  
Experimental Results ◽  
Response Functions ◽  
Dynamic Identification ◽  
Inconsistent Equation

The problem of joints dynamic identification and modeling is discussed in this paper. The theoretical dynamic model of joints is established by FRFs (frequency response functions) data, and formulas for identifying the joints dynamic properties is deduced. The equivalent value of dynamic stiffness is extracted by solving the inconsistent equation using the least square method. The experimental example is provided to validate the feasibility and accuracy of the proposed method, the predicted result showing good fitting with experimental results.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

Application of the dynamic model of Saeman to an industrial rotary kiln incinerator: Numerical and experimental results

2010 ◽  
Vol 30 (7) ◽  
pp. 1188-1195 ◽  
Author(s):  
L.G. Ndiaye ◽  
S. Caillat ◽  
A. Chinnayya ◽  
D. Gambier ◽  
B. Baudoin
Keyword(s):  
Dynamic Model ◽  
Rotary Kiln ◽  
Experimental Results

Identification of Viscoelastic Properties of Finite Element Structures With Joints

1997 ◽  
Author(s):  
Torsten Herrmann ◽  
Valdas Chaika
Keyword(s):  
Finite Element ◽  
Viscoelastic Properties ◽  
Sparse Matrix ◽  
Maxwell Model ◽  
Least Square ◽  
Identification Algorithm ◽  
Domain Identification ◽  
Generalized Maxwell Model ◽  
Maxwell Models ◽  
Complex Modal Analysis

Abstract Identification of the damping and stiffness parameters of the composite joints in finite element structures is analyzed. For the modeling of the viscoelastic properties of the joints the classical Voigt-Kelvin and generalized Maxwell model (three parameter solid) are used. A time domain identification algorithm for classically and non-classically damped dynamic systems is developed. It is based on the application of an extended Kalman filter and least square technique. The algorithm uses complex modal analysis and sparse matrix technology. Both force and base excited systems are considered. Experimental verification of the identification results is carried out on a test structure. The accuracy of the modeling of damping in the joint using the Voigt-Kelvin and generalized Maxwell models is investigated.

Identification of continuous-time hybrid “Box–Jenkins” systems with multiple unknown time delays using two-stage parameter estimation algorithm

2019 ◽  
Vol 36 (6) ◽  
pp. 2111-2130
Author(s):  
Yamna Ghoul
Keyword(s):  
Continuous Time ◽  
Time Delays ◽  
Least Square ◽  
Identification Algorithm ◽  
Recursive Least Square ◽  
Two Stage ◽  
Content Type ◽  
Single Output ◽  
Multiple Input ◽  
Unknown Time Delays

Purpose This study/paper aims to present a separable identification algorithm for a multiple input single output (MISO) continuous time (CT) hybrid “Box–Jenkins”. Design/methodology/approach This paper proposes an optimal method for the identification of MISO CT hybrid “Box–Jenkins” systems with unknown time delays by using the two-stage recursive least-square (TS-RLS) identification algorithm. Findings The effectiveness of the proposed scheme is shown with application to a simulation example. Originality/value A two-stage recursive least-square identification method is developed for multiple input single output continuous time hybrid “Box–Jenkins” system with multiple unknown time delays from sampled data. The proposed technique allows the division of the global CT hybrid “Box–Jenkins” system into two fictitious subsystems: the first one contains the parameters of the system model, including the multiple unknown time delays, and the second contains the parameters of the noise model. Then the TS-RLS identification algorithm can be applied easily to estimate all the parameters of the studied system.

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