Online Estimation Algorithm for a Biaxial Ankle Kinematic Model With Configuration Dependent Joint Axes

2011 ◽  
Vol 133 (2) ◽  
Author(s):  
Y. H. Tsoi ◽  
S. Q. Xie
Keyword(s):  
Kinematic Model ◽  
Estimation Algorithm ◽  
Recursive Least Squares ◽  
Estimation Accuracy ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Extended Model ◽  
Identification Algorithm ◽  
Kinematic Parameters ◽  
Rls Algorithm

The kinematics of the human ankle is commonly modeled as a biaxial hinge joint model. However, significant variations in axis orientations have been found between different individuals and also between different foot configurations. For ankle rehabilitation robots, information regarding the ankle kinematic parameters can be used to estimate the ankle and subtalar joint displacements. This can in turn be used as auxiliary variables in adaptive control schemes to allow modification of the robot stiffness and damping parameters to reduce the forces applied at stiffer foot configurations. Due to the large variations observed in the ankle kinematic parameters, an online identification algorithm is required to provide estimates of the model parameters. An online parameter estimation routine based on the recursive least-squares (RLS) algorithm was therefore developed in this research. An extension of the conventional biaxial ankle kinematic model, which allows variation in axis orientations with different foot configurations had also been developed and utilized in the estimation algorithm. Simulation results showed that use of the extended model in the online algorithm is effective in capturing the foot orientation of a biaxial ankle model with variable joint axis orientations. Experimental results had also shown that a modified RLS algorithm that penalizes a deviation of model parameters from their nominal values can be used to obtain more realistic parameter estimates while maintaining a level of estimation accuracy comparable to that of the conventional RLS routine.

scholarly journals Cranking Capability Estimation Algorithm Based on Modeling and Online Update of Model Parameters for Li-Ion SLI Batteries

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3365
Author(s):  
Tae-Won Noh ◽  
Jung-Hoon Ahn ◽  
Byoung Kuk Lee
Keyword(s):  
Equivalent Circuit ◽  
Voltage Drop ◽  
Equivalent Circuit Model ◽  
Estimation Algorithm ◽  
Circuit Model ◽  
Estimation Accuracy ◽  
Voltage Response ◽  
Model Parameters ◽  
Voltage Range ◽  
Terminal Voltage

The terminal voltage of a starting–lighting–ignition (SLI) battery can decrease to a value lower than the allowable voltage range because of the high discharge current required to crank the engine of a vehicle. To avoid the safety problems generated by this voltage drop, this paper proposes a cranking capability estimation algorithm. The proposed algorithm includes an equivalent circuit model for describing the instantaneous voltage response to the cranking current profile. This algorithm predicts the minimum value of the terminal voltage for the cranking transient period by analyzing the polarization voltage and dynamic characteristic of the equivalent circuit model. The estimation accuracy is adjusted by an online update for the parameters of the equivalent circuit model, which varies with temperature, aging, and other factors. The proposed algorithm was validated by experiments with a 60Ah LiFePO4-type SLI battery.

Real-Time Battery Model Identification Using a Two Time-Scaled Approach

2013 ◽  
Author(s):  
Yiran Hu ◽  
Yue-Yun Wang
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
State Estimation ◽  
Real Time ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Model Parameter ◽  
Battery Model ◽  
Model Parameter Identification

Battery state estimation (BSE) is one of the most important design aspects of an electrified propulsion system. It includes important functions such as state-of-charge estimation which is essentially for the energy management system. A successful and practical approach to battery state estimation is via real time battery model parameter identification. In this approach, a low-order control-oriented model is used to approximate the battery dynamics. Then a recursive least squares is used to identify the model parameters in real time. Despite its good properties, this approach can fail to identify the optimal model parameters if the underlying system contains time constants that are very far apart in terms of time-scale. Unfortunately this is the case for typical lithium-ion batteries especially at lower temperatures. In this paper, a modified battery model parameter identification method is proposed where the slower and faster battery dynamics are identified separately. The battery impedance information is used to guide how to separate the slower and faster dynamics, though not used specifically in the identification algorithm. This modified algorithm is still based on least squares and can be implemented in real time using recursive least squares. Laboratory data is used to demonstrate the validity of this method.

scholarly journals Dual-Input Slope Seeking Control of Continuous Micro-Algae Cultures with Experimental Validation

2021 ◽  
Vol 11 (16) ◽  
pp. 7451
Author(s):  
Christian Feudjio Letchindjio ◽  
Jesús Zamudio Lara ◽  
Laurent Dewasme ◽  
Héctor Hernández Escoto ◽  
Alain Vande Wouwer
Keyword(s):  
Performance Index ◽  
Degrees Of Freedom ◽  
Incident Light ◽  
Experimental Studies ◽  
Estimation Algorithm ◽  
Recursive Estimation ◽  
Hammerstein Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Actual Process

This paper investigates the application of adaptive slope-seeking strategies to dual-input single output dynamic processes. While the classical objective of extremum seeking control is to drive a process performance index to its optimum, this paper also considers slope seeking, which allows driving the performance index to a desired level (which is thus sub-optimal). Moreover, the consideration of more than one input signal allows minimizing the input energy thanks to the degrees of freedom offered by the additional inputs. The actual process is assumed to be locally approachable by a Hammerstein model, combining a nonlinear static map with a linear dynamic model. The proposed strategy is based on the interplay of three components: (i) a recursive estimation algorithm providing the model parameters and the performance index gradient, (ii) a slope generator using the static map parameter estimates to convert the performance index setpoint into slope setpoints, and (iii) an adaptive controller driving the process to the desired setpoint. The performance of the slope strategy is assessed in simulation in an application example related to lipid productivity optimization in continuous cultures of micro-algae by acting on both the incident light intensity and the dilution rate. It is also validated in experimental studies where biomass production in a continuous photo-bioreactor is targeted.

scholarly journals The parameter identification of the autonomous underwater vehicle based on multi-innovation least squares identification algorithm

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142092101
Author(s):  
Zhang Huajun ◽  
Tong Xinchi ◽  
Guo Hang ◽  
Xia Shou
Keyword(s):  
Least Squares ◽  
Degrees Of Freedom ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Six Degrees Of Freedom ◽  
Recursive Least Squares Algorithm ◽  
Least Squares Algorithm

An accurate model is important for the engineer to design a robust controller for the autonomous underwater vehicle. There are two factors that make the identification difficult to get accurate parameters of an AUV model in practice. Firstly, the autonomous underwater vehicle model is a coupled six-degrees-of-freedom model, and each state of the kinetic model influences the other five states. Secondly, there are more than 100 hydrodynamic coefficients which have different effects, and some parameters are too small to be identified. This article proposes a simplified six-degrees-of-freedom model that contains the essential parameters and employs the multi-innovation least squares algorithm based on the recursive least squares algorithm to obtain the parameters. The multi-innovation least squares algorithm leverages several past errors to identify the parameters, and the identification results are more accurate than those of the recursive least squares algorithm. It collects the practical data through an experiment and designs a numerical program to identify the model parameters. Meanwhile, it compares the performances of the multi-innovation least squares algorithm with those of the recursive least squares algorithm and the least square method, the results show that the multi-innovation least squares algorithm is the most effective way to identify parameters for the simplified six-degrees-of-freedom model.

scholarly journals A continuous switching contact model for virtual environment based teleoperation

2021 ◽  
Author(s):  
Yongqing Fu ◽  
Baibo Wu ◽  
Weiyang Lin
Keyword(s):  
Energy Loss ◽  
Virtual Environment ◽  
Contact Model ◽  
Recursive Least Squares ◽  
Human Hand ◽  
Model Parameters ◽  
Rls Algorithm ◽  
Contact Materials ◽  
Logarithm Function ◽  
Extra Parameter

AbstractVirtual environment (VE), as the proxy of slave contact environment, is the most promising technology to solve the time-delay problems in teleoperation. The accuracy of the predicted force depends not only on the reliability of the contact model but also on the estimation algorithm’s adaptability. A new contact model is proposed to be applicable in various materials, which includes both the Kelvin–Voigt model (KVM) and Hunt–Crossley model (HCM). An extra parameter is set in the model to express the capacity of continuous switching between KVM and HCM, whose rationality is proved based on the energy loss. The energy loss is proportional to a power of impact velocity, and the exponent is bounded at [2,3], which exactly lies between KVM and HCM. Furthermore, to estimate the parameters with a single-stage method, the nonlinear model is linearized approximatively with logarithm function and polynomials. Then, the recursive least squares (RLS) algorithm combining forgetting factor and self-perturbing action is designed to identify the four parameters online. Finally, the model’s continuous switching is verified with ideal simulation, and the model parameters are continuously changed without jumpy switch error. In the experiment, sponge, foam, and human hand represent the complex contact materials of the slave environment where the predicted force is shown to follow the real contact force with enough accuracy. Therefore, the virtual model can be considered the substitution of slave contact environment so that the feedback force in master can be calculated in real-time.

scholarly journals Research on Co-Estimation Algorithm of SOC and SOH for Lithium-Ion Batteries in Electric Vehicles

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 181
Author(s):  
Chang-Qing Du ◽  
Jian-Bo Shao ◽  
Dong-Mei Wu ◽  
Zhong Ren ◽  
Zhong-Yi Wu ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Stress Test ◽  
Equivalent Circuit Model ◽  
Estimation Algorithm ◽  
Maximum Error ◽  
Recursive Least Squares ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Ambient Temperatures ◽  
Soc Estimation

The accurate estimation of the state of charge (SOC) and state of health (SOH) is of great significance to energy management and safety in electric vehicles. To achieve a good trade-off between real-time capability and estimation accuracy, a collaborative estimation algorithm for SOC and SOH is presented based on the Thevenin equivalent circuit model, which combines the recursive least squares method with a forgetting factor and the extended Kalman filter. First, the parameter identification accuracy is studied under a dynamic stress test (DST) and the federal urban driving schedule (FUDS) test at different ambient temperatures (0 °C, 25 °C, and 45 °C). Secondly, the FUDS test is used to verify the SOC estimation accuracy. Thirdly, two batteries with different aging degrees are used to validate the proposed SOH estimation algorithm. Subsequently, the accuracy of the SOC estimation algorithm is studied, considering the influence of updating the SOH. The proposed SOC estimation algorithm can achieve good performance at different ambient temperatures (0 °C, 25 °C, and 45 °C), with a maximum error of less than 2.3%. The maximum error for the SOH is less than 4.3% for two aged batteries at 25 °C, and it can be reduced to 1.4% after optimization. Furthermore, calibrating the capacity as the SOH changes can effectively improve the SOC estimation accuracy over the whole battery life.

scholarly journals Influence Analysis and Optimization of Sampling Frequency on the Accuracy of Model and State-of-Charge Estimation for LiNCM Battery

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1205 ◽  
Author(s):  
Pingwei Gu ◽  
Zhongkai Zhou ◽  
Shaofei Qu ◽  
Chenghui Zhang ◽  
Bin Duan
Keyword(s):  
State Estimation ◽  
Sampling Frequency ◽  
Operating Conditions ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Selection Strategy ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Battery Modeling ◽  
The Relationship

Battery characterization data is the basis for battery modeling and state estimation. It is generally believed that the higher the sampling frequency, the finer the data, and the higher the model and state estimation accuracy. However, scientific selection strategy for sampling frequency is very important but rarely studied. This paper studies the influence of sampling frequency on the accuracy of battery model and state estimation under four different sampling frequencies: 0.2 Hz, 1 Hz, 2 Hz, and 10 Hz. Then, a function is proposed to depict the relationship between accuracy and sampling frequency, which shows an optimal selection principle. The iterative identification algorithm is presented to identify the model parameters, and state-of-charge (SOC) is estimated via extended Kalman filter algorithm. Experimental results with different operating conditions clearly show the relationship between sampling frequency, accuracy, and data quantity, and the proposed selection strategy has high practical value and universality.

Kinematic Calibration of PHANTOM Premium 1.5/6DOF Haptic Device

2011 ◽  
Vol 486 ◽  
pp. 205-208 ◽  
Author(s):  
Ozgur Baser ◽  
Erhan Ilhan Konukseven
Keyword(s):  
Force Control ◽  
Kinematic Model ◽  
Haptic Device ◽  
Model Parameters ◽  
Haptic Interfaces ◽  
Kinematic Parameters ◽  
Haptic Devices ◽  
Transmission Errors ◽  
Control Requirement ◽  
Joint Transmission

Precise positioning and precise force control requirement in haptic devices necessitate the calibration of the device. Since force control algorithms in haptic interfaces employ Jacobian matrix that includes kinematic model parameters, calibration is not only important for pose accuracy but also for force control. The deviation in kinematic parameters and joint transmission errors are main reasons disturbing the calibration of the haptic devices. Capstan drives and parallelogram mechanisms are preferred to use for actuation in haptic device design. Their transmission errors should be estimated in the calibration. This paper presents a simulation study including the estimation of kinematic parameters and transmission errors due to the capstan drives and parallelogram mechanism for a PHANTOM Premium haptic device.

Wavelet-Based Identification Method of Li-Ion Battery Model for Electric Vehicles

2012 ◽  
Vol 608-609 ◽  
pp. 1529-1532
Author(s):  
Da Zhong Mu ◽  
Jiu Chun Jiang
Keyword(s):  
Electric Vehicles ◽  
Noise Removal ◽  
Recursive Least Squares ◽  
Estimation Accuracy ◽  
Model Parameters ◽  
Battery Management System ◽  
Li Ion Battery ◽  
Battery Model ◽  
Identification Method ◽  
Li Ion

Online parameters identification is one of the major functions of model-based battery management system (BMS), which can be used to monitor the working status of battery, such as state of charge (SOC) and state of health (SOH). This paper proposed a wavelet-based identification method of Li-ion battery model for Electric Vehicles (EVs). The main idea is to decompose the measured terminal voltage and current data at multiple scales, and then recursive least squares (RLS) algorithm is used to extract the model parameters at a suitable scale. The proposed method is shown to have good robustness to measured noise and thus enhances the estimation accuracy by taking advantage of the noise removal ability and signal approximation properties of wavelet decomposition.

Tire Rolling Kinematics Model for an Intelligent Tire Based on an Accelerometer

2020 ◽  
Vol 48 (4) ◽  
pp. 287-314
Author(s):  
Yan Wang ◽  
Zhe Liu ◽  
Michael Kaliske ◽  
Yintao Wei
Keyword(s):  
Elastic Deformation ◽  
Estimation Algorithm ◽  
Force Sensor ◽  
Euler Method ◽  
Rolling Contact ◽  
Body Motion ◽  
Identification Algorithm ◽  
Active Perception ◽  
Kinematics Model ◽  
Ring Model

ABSTRACT The idea of intelligent tires is to develop a tire into an active perception component or a force sensor with an embedded microsensor, such as an accelerometer. A tire rolling kinematics model is necessary to link the acceleration measured with the tire body elastic deformation, based on which the tire forces can be identified. Although intelligent tires have attracted wide interest in recent years, a theoretical model for the rolling kinematics of acceleration fields is still lacking. Therefore, this paper focuses on an explicit formulation for the tire rolling kinematics of acceleration, thereby providing a foundation for the force identification algorithms for an accelerometer-based intelligent tire. The Lagrange–Euler method is used to describe the acceleration field and contact deformation of rolling contact structures. Then, the three-axis acceleration vectors can be expressed by coupling rigid body motion and elastic deformation. To obtain an analytical expression of the full tire deformation, a three-dimensional tire ring model is solved with the tire–road deformation as boundary conditions. After parameterizing the ring model for a radial tire, the developed method is applied and validated by comparing the calculated three-axis accelerations with those measured by the accelerometer. Based on the features of acceleration, especially the distinct peak values corresponding to the tire leading and trailing edges, an intelligent tire identification algorithm is established to predict the tire–road contact length and tire vertical load. A simulation and experiments are conducted to verify the accuracy of the estimation algorithm, the results of which demonstrate good agreement. The proposed model provides a solid theoretical foundation for an acceleration-based intelligent tire.

Sign in / Sign up

Export Citation Format

Share Document