Identification of Vehicle Inertia Parameters: From Test Bench Design to Movement Trajectory Optimization

2018 ◽  
Author(s):  
Di Yao ◽  
Kay Büttner ◽  
Günther Prokop
Keyword(s):  
Parameter Identification ◽  
Dynamic Model ◽  
Test Bench ◽  
Movement Trajectory ◽  
Inertial Parameters ◽  
Inertia Parameters ◽  
Observation Matrix ◽  
Moving Platform ◽  
Systematic Solution ◽  
Optimal Movement

This work presents a new systematic solution to identify the vehicle inertia parameters which are essential inputs for vehicle simulation and vehicle safety research. In conceptual design phase of this work, a virtual three Degree-of-Freedom (DoF) test bench/ parallel manipulator (PM) whose moving platform is used to clamp vehicle under test is developed. In order to realize the kinematic characteristics of the proposed PM, the kinematic analysis consists of inverse kinematic and singularity architecture is carried out. Aiming at obtaining all ten vehicle inertia parameters (i.e., mass, center of gravity and inertia tensor), the observation matrix for parameter identification is derived from the dynamic model of PM. To get the dynamic model, the Euler’s equation and Lagrange approach are applied to implement the dynamic analysis for PM’s moving platform and actuators, respectively. It is beneficial to reduce the complexity of dynamic model and load of numerical computation. In the following section, to minimize the sensitivity of parameter identification to measurement noise, an optimization process of searching for the optimal movement trajectory of PM is proposed. For this purpose, the parameterized finite-Fourier-series are used to definite the general movement trajectory of PM firstly. Subsequently, the parameters of general trajectory are optimized by employing a nonlinear iterative algorithm. Objective of this algorithm is to obtain the minimal condition number of observation matrix and meanwhile to ensure the PM still works in the achievable working space during the test. The results show that the vehicle inertial parameters can be effectively identified by executing the single optimal movement trajectory on the PM. It is expected that the proposed systematic solution could be an important approach to improve the identification efficiency and identification accuracy of vehicle inertial parameters.

scholarly journals Dynamic Modeling and Parameter Identification for a Gantry-type Automated Fiber Placement Machine

2021 ◽  
Author(s):  
Liang Cheng ◽  
Jianbo Wu ◽  
Yingjie Guo ◽  
Jiangxiong Li ◽  
Yinglin Ke
Keyword(s):  
Parameter Identification ◽  
Dynamic Model ◽  
Dynamic Modeling ◽  
Critical Role ◽  
Error Rates ◽  
Identification Algorithm ◽  
Fiber Placement ◽  
Inertial Parameters ◽  
Automated Fiber Placement ◽  
Model Linearization

Abstract Dynamic models play a critical role in the design of model-based controllers, and therefore have a significant effect on the dynamic characteristics of motion equipment. We mainly focus on the dynamic modeling and parameter identification for a gantry-type automated fiber placement (AFP) machine in this paper. First, a dynamic modeling process combining prismatic axes and revolute axes is conducted by the Newton-Euler method, in which the effects of friction and hydraulic balance system are also considered. Then, as the convenience for parameter identification and the application in linearity control, the methods of dynamic model linearization and determination of minimum inertial parameters based on the multi-body system (MBS) theory are proposed, and a dynamic model in the form of linearized minimum inertial parameters is consequently established. To identify the parameters in the model, key issues regarding excitation trajectory, filtering, and identification algorithm are discussed in detail. Finally, corresponding experiments are performed on the AFP machine, and experimental results show that there is a good agreement between the prediction of the model and the measurement in actuality. Data analysis shows that except for Z-axis, the relative error rates of the others are not greater than 5%, which proves the effectiveness of the established dynamic model and the identified parameters.

scholarly journals Development, Modeling and Control of a Dual Tilt-Wing UAV in Vertical Flight

Drones ◽  
2020 ◽  
Vol 4 (4) ◽  
pp. 71
Author(s):  
Luz M. Sanchez-Rivera ◽  
Rogelio Lozano ◽  
Alfredo Arias-Montano
Keyword(s):  
Dynamic Model ◽  
Attitude Control ◽  
Test Bench ◽  
Aerodynamic Coefficients ◽  
Nonlinear Dynamic Model ◽  
Modeling And Control ◽  
Drag And Lift ◽  
And Control ◽  
Dynamics Method ◽  
Indoor And Outdoor

Hybrid Unmanned Aerial Vehicles (H-UAVs) are currently a very interesting field of research in the modern scientific community due to their ability to perform Vertical Take-Off and Landing (VTOL) and Conventional Take-Off and Landing (CTOL). This paper focuses on the Dual Tilt-wing UAV, a vehicle capable of performing both flight modes (VTOL and CTOL). The UAV complete dynamic model is obtained using the Newton–Euler formulation, which includes aerodynamic effects, as the drag and lift forces of the wings, which are a function of airstream generated by the rotors, the cruise speed, tilt-wing angle and angle of attack. The airstream velocity generated by the rotors is studied in a test bench. The projected area on the UAV wing that is affected by the airstream generated by the rotors is specified and 3D aerodynamic analysis is performed for this region. In addition, aerodynamic coefficients of the UAV in VTOL mode are calculated by using Computational Fluid Dynamics method (CFD) and are embedded into the nonlinear dynamic model. To validate the complete dynamic model, PD controllers are adopted for altitude and attitude control of the vehicle in VTOL mode, the controllers are simulated and implemented in the vehicle for indoor and outdoor flight experiments.

scholarly journals Parameter Estimation of a Countershaft Brake for Heavy-Duty Vehicles with Automated Mechanical Transmission

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1036
Author(s):  
Yunxia Li ◽  
Lei Li
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
Dynamic Model ◽  
Control Flow ◽  
Mechanical Transmission ◽  
Equivalent Resistance ◽  
Heavy Duty ◽  
Characteristic Parameters ◽  
Resistance Torque ◽  
Heavy Duty Vehicles

A countershaft brake is used as a transmission brake (TB) to realize synchronous shifting by reducing the automated mechanical transmission (AMT) input shaft’s speed rapidly. This process is performed to reduce shifting time and improve shifting quality for heavy-duty vehicles equipped with AMT without synchronizer. To improve controlled synchronous shifting, the AMT input shaft’s equivalent resistance torque and the TB’s characteristic parameters are studied. An AMT dynamic model under neutral gear position is analyzed during the synchronous control interval. A dynamic model of the countershaft brake is discussed, and its control flow is given. The parameter identification method of the AMT input shaft’s equivalent resistance torque is given on the basis of the least squares algorithm. The parameter identification of the TB’s characteristic parameters is proposed on the basis of the recursive least squares method (RLSM). Experimental results show that the recursive estimations of the TB’s characteristic parameters under different duty cycles of the TB solenoid valve, including brake torque estimation, estimation accuracy, and braking intensity estimation, can be effectively estimated. The research provides some reliable evidence to further study the synchronous shifting control schedule for heavy-duty vehicles with AMT.

Inertia parameter identification of anunknown captured space target

2019 ◽  
Vol 91 (8) ◽  
pp. 1147-1155 ◽  
Author(s):  
Xiaofeng Liu ◽  
Bangzhao Zhou ◽  
Boyang Xiao ◽  
Guoping Cai
Keyword(s):  
Angular Momentum ◽  
Parameter Identification ◽  
Linear Momentum ◽  
Content Type ◽  
Identification Method ◽  
Robot Arms ◽  
Precise Estimation ◽  
Inertia Parameter ◽  
Inertia Parameters ◽  
Space Target

Purpose The purpose of this paper is to present a method to obtain the inertia parameter of a captured unknown space target. Design/methodology/approach An inertia parameter identification method is proposed in the post-capture scenario in this paper. This method is to resolve parameter identification with two steps: coarse estimation and precise estimation. In the coarse estimation step, all the robot arms are fixed and inertia tensor of the combined system is first calculated by the angular momentum conservation equation of the system. Then, inertia parameters of the unknown target are estimated using the least square method. Second, in the precise estimation step, the robot arms are controlled to move and then inertia parameters are once again estimated by optimization method. In the process of optimization, the coarse estimation results are used as an initial value. Findings Numerical simulation results prove that the method presented in this paper is effective for identifying the inertia parameter of a captured unknown target. Practical implications The presented method can also be applied to identify the inertia parameter of space robot. Originality/value In the classic momentum-based identification method, the linear momentum and angular momentum of system, both considered to be conserved, are used to identify the parameter of system. If the elliptical orbit in space is considered, the conservation of linear momentum is wrong. In this paper, an identification based on the conservation of angular momentum and dynamics is presented. Compared with the classic momentum-based method, this method can get a more accurate identification result.

scholarly journals Modeling and Experimental Design for the On-Orbit Inertial Parameter Identification of Free-Flying Space Robots

2005 ◽  
Author(s):  
Roberto Lampariello ◽  
Gerhard Hirzinger
Keyword(s):  
Energy Consumption ◽  
Experimental Design ◽  
Parameter Identification ◽  
Suitable Model ◽  
Space Robots ◽  
Full System ◽  
End Effector ◽  
Dynamic Motion ◽  
Inertial Parameters ◽  
Flying Robot

A method is proposed for the identification of the inertial parameters of a free-flying robot directly in orbit, using accelerometers. This can serve to improve the path planning and tracking capabilities of the robot, as well as its efficiency in energy consumption. The method is applied to the identification of the base body and of the load on the end-effector, giving emphasis to the experimental design. The problem of the identification of the full system is also addressed in its theoretical aspects. The experience from the Getex Dynamic Motion experiments performed on the ETS-VII satellite have allowed to determine a most suitable model for the identification.

Development of the Power Load Modeling System with Denoising and Parameter Identification

2013 ◽  
Vol 805-806 ◽  
pp. 712-715
Author(s):  
Li Di Wang ◽  
Qing Ying Ge ◽  
Zhe Li ◽  
Tai Gang Nian
Keyword(s):  
Parameter Identification ◽  
Dynamic Model ◽  
Variable Load ◽  
Signal Acquisition ◽  
Load Modeling ◽  
Load Model ◽  
Process Measurement ◽  
Power Load ◽  
Measurement Signal ◽  
Zip Model

The power load modeling system is designed with denoising and parameter identification. This system consists of signal acquisition, signal preprocessing, parameter identification, different load modeling methods such as ZIP model and Dynamic modeling. Original signal can be read from Excel file, which is the simulated signal or measurement signal. Then some kinds of denoising methods can be selected, which are mean filtering, medial filtering and wavelet denoising. After being denoised, the load signal is suitable for the parameter identification process. ZIP model is used to simulate the static load model, and the dynamic model is used to simulate the dynamic load model which is changeable during different periods. With the parameter identification and simulation process, measurement power load signal is used in the experiment, the dynamic model is more suitable for the variable load voltage features description.

scholarly journals Inertia Parameter Identification for an Unknown Satellite in Precapture Scenario

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Xiao-Feng Liu ◽  
Xiao-Yu Zhang ◽  
Pei-Ran Chen ◽  
Guo-Ping Cai
Keyword(s):  
Parameter Identification ◽  
Momentum Conservation ◽  
Space Robot ◽  
Control Force ◽  
Soft Contact ◽  
Identification Scheme ◽  
Inertia Parameter ◽  
Inertia Parameters ◽  
Dynamics And Control ◽  
And Control

The problem of dynamics and control using a space robot to capture a noncooperative satellite is an important issue for on-orbit services. Inertia parameters of the satellite should be identified before capturing such that the robot can design an active controller to finish the capturing task. In this paper, a new identification scheme is proposed for parameter identification of a noncooperative satellite. In this scheme, the space robot is controlled to contact softly and then maintain contact with the noncooperative target firstly, then the variation of momentum of the target during the contact-maintaining phase is calculated using the control force and torque acting on the base of the space robot and the kinematic information of the space robot, and finally, the momentum-conservation-based identification method is used to estimate inertia parameters of the target. To realize soft contact and then maintain contact, a damping contact controller is designed in this paper, in which a mass-damping system is designed to control the contact between the robot and the target. Soft contact and then contact maintenance can be realized by utilizing the buffering characteristics of the mass-damping system. The effectiveness of the proposed identification scheme is verified through numerical simulations at the end of this paper. Simulation results indicate that the proposed scheme can achieve high-precision identification results.

Parameter Identification for Model-Based Control of Hydraulically Actuated Open-Chain Manipulators

2019 ◽  
Author(s):  
Lionel Hulttinen ◽  
Janne Koivumäki ◽  
Jouni Mattila
Keyword(s):  
Parameter Identification ◽  
Nonlinear Model ◽  
A Priori ◽  
Chamber Pressure ◽  
Hydraulic Actuator ◽  
Open Chain ◽  
Model Based Control ◽  
Model Based ◽  
Inertial Parameters ◽  
Manipulator Link

Abstract In this paper, a nonlinear model-based controller with parameter identification is designed for a rigid open-chain manipulator arm actuated by servovalve-controlled hydraulic cylinders. The arising problem in adopting model-based controllers is how to acquire accurate estimates of system parameters, with limited available information about either the hydraulic actuator parameters or manipulator link inertial parameters. The objective of this study is to identify both the rigid-body parameters of the links and the hydraulic actuator parameters from collected cylinder chamber pressure and joint angle data, while no a priori knowledge of these parameters is available. Same physical plant models are used for control design as well as for parameter identification. Experimental results show that the proposed nonlinear model-based control scheme results in acceptable Cartesian position tracking performance in free-space motion when using the identified parameters.

Calculation of Optimized Movement Trajectories for the Human Forearm

2013 ◽  
Vol 401-403 ◽  
pp. 1347-1352
Author(s):  
Li Li Yang
Keyword(s):  
Discrete Time ◽  
Linear Quadratic Regulator ◽  
Minimum Variance ◽  
Movement Trajectory ◽  
Linear Quadratic ◽  
Variance Model ◽  
Movement Trajectories ◽  
Human Forearm ◽  
Optimal Movement ◽  
Time Linear

Using the minimum variance model, optimal human forearm trajectories formation was investigated using a discrete time linear quadratic regulator. First, the continuous dynamics of the human forearm were established on the basis of the relation between muscle torque and neural control signal, and then we transferred the continuous system dynamics to discrete time notation. Finally we expressed the objective function of minimum variance model using a discrete time linear quadratic regulator and employed Riccati recursion to obtain the optimal movement trajectories of the human forearm. The results of example simulation show that the optimal movement trajectory of the forearm follows a smooth curve, and the speed curve of the hand is single peaked and bell shaped. These are in good agreement with the inherent kinematic properties of optimal movement, and therefore the method is effective for calculating the optimal movement trajectory of the human forearm.

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