A systematic identification approach for biaxial piezoelectric stage with coupled Duhem-type hysteresis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Qun Chen ◽  
Zong-Xiao Yang ◽  
Zhumu Fu
Keyword(s):  
Parameter Identification ◽  
High Efficiency ◽  
Full Range ◽  
Unknown Parameters ◽  
Content Type ◽  
De Algorithm ◽  
Complicated Model ◽  
Identification Approach ◽  
Cross Axis ◽  
Systematic Identification

Purpose The problem of parameter identification for biaxial piezoelectric stages is still a challenging task because of the existing hysteresis, dynamics and cross-axis coupling. This study aims to find an accurate and systematic approach to tackle this problem. Design/methodology/approach First, a dual-input and dual-output (DIDO) model with Duhem-type hysteresis is proposed to depict the dynamic behavior of the biaxial piezoelectric stage. Then, a systematic identification approach based on a modified differential evolution (DE) algorithm is proposed to identify the unknown parameters of the Duhem-type DIDO model for a biaxial piezostage. The randomness and parallelism of the modified DE algorithm guarantee its high efficiency. Findings The experimental results show that the characteristics of the biaxial piezoelectric stage can be identified with adequate accuracy based on the input–output data, and the peak-valley errors account for 2.8% of the full range in the X direction and 1.5% in the Y direction. The attained results validated the correctness and effectiveness of the presented identification method. Originality/value The classical DE algorithm has many adjustment parameters, which increases the inconvenience and difficulty of using in practice. The parameter identification of Duhem-type DIDO piezoelectric model is rarely studied in detail and its successful application based on DE algorithm on a biaxial piezostage is hitherto unexplored. To close this gap, this work proposed a modified DE-based systematic identification approach. It not only can identify this complicated model with more parameters, but also has little tuning parameters and thus is easy to use.

Online Identification of Uncertain Fractional-Order Nonlinear Systems Using a Reinforced Differential Evolution Optimizer

2019 ◽  
Author(s):  
Jiamin Wei ◽  
Yongguang Yu ◽  
YangQuan Chen
Keyword(s):  
Differential Evolution ◽  
Parameter Identification ◽  
Fractional Order ◽  
High Efficiency ◽  
Problem Formulation ◽  
Unknown Parameters ◽  
Online Identification ◽  
Adaptive Differential Evolution ◽  
Efficiency And Effectiveness ◽  
Hyperchaotic Systems

Abstract Parameter identification as known as a significant issue is investigated in this paper. The research focus on online identifying unknown parameters of uncertain fractional-order chaotic and hyperchaotic systems, which shows great potential in recent applications. Up to now, most of the existing online identification methods only focus on integer-order systems, thus, it’s necessary to expand these fundamental results to uncertain fractional-order nonlinear dynamic systems and adopt an effective optimizer to deal with the model uncertainties. Motivated by this consideration, this research introduces an efficient optimizer to offline and online parameter identification of the fractional-order chaotic and hyperchaotic systems through non-Lyapunov way. For problem formulation, a multi-dimensional optimization problem is converted into from the problem of parameter identification, where both systematic parameters and fractional derivative orders are considered as independent unknown parameters to be estimated. The experimental results illustrate that SHADE is significantly superior to the other compared approaches. In this case, online identification is conducted via SHADE, the simulation results further indicate that success-history based adaptive differential evolution (SHADE) algorithm is capable of detecting and determining the variations of parameters in uncertain fractional-order chaotic and hyperchaotic systems, and also is supposed to be a successful and potentially promising method for handling the online identification problems with high efficiency and effectiveness.

A novel LLSDPso method for nonlinear dynamic parameter identification

2017 ◽  
Vol 37 (4) ◽  
pp. 490-498 ◽  
Author(s):  
Jian-jun Yuan ◽  
Weiwei Wan ◽  
Xiajun Fu ◽  
Shuai Wang ◽  
Ning Wang
Keyword(s):  
Parameter Identification ◽  
High Efficiency ◽  
Linear Part ◽  
Force Sensor ◽  
Least Square ◽  
Measurement Unit ◽  
Flexible Robot ◽  
Friction Coefficients ◽  
Content Type ◽  
Non Linear

Purpose This paper aims to propose a novel method to identify the parameters of robotic manipulators using the torque exerted by the robot joint motors (measured by current sensors). Design/methodology/approach Previous studies used additional sensors like force sensor and inertia measurement unit, or additional payload mounted on the end-effector to perform parameter identification. The settings of these previous works were complicated. They could only identify part of the parameters. This paper uses the torque exerted by each joint while performing Fourier periodic excited trajectories. It divides the parameters into a linear part and a non-linear part, and uses linear least square (LLS) parameter estimation and dual-swarm-based particle swarm optimization (DPso) to compute the linear and non-linear parts, respectively. Findings The settings are simpler and can identify the dynamic parameters, the viscous friction coefficients and the Coulomb friction coefficients of two joints at the same time. A SIASUN 7-Axis Flexible Robot is used to experimentally validate the proposal. Comparison between the predicted torque values and ground-truth values of the joints confirms the effectiveness of the method. Originality/value The proposed method identifies two joints at the same time with satisfying precision and high efficiency. The identification errors of joints do not accumulate.

A novel approach for experimental identification of vehicle dynamic parameters

2020 ◽  
Vol 234 (10-11) ◽  
pp. 2634-2648
Author(s):  
Di Yao ◽  
Philipp Ulbricht ◽  
Stefan Tonutti ◽  
Kay Büttner ◽  
Prokop Günther
Keyword(s):  
Parameter Identification ◽  
Dynamic Modeling ◽  
Trajectory Optimization ◽  
Dynamic Parameters ◽  
Vehicle Dynamic ◽  
Test Rig ◽  
Vehicle Simulation ◽  
Virtual Test ◽  
Identification Approach ◽  
Measuring Machine

Pervasive applications of the vehicle simulation technology are a powerful motivation for the development of modern automobile industry. As basic parameters of road vehicle, vehicle dynamic parameters can significantly influence the ride comfort and dynamics of vehicle, and therefore have to be calculated accurately to obtain reliable vehicle simulation results. Aiming to develop a general solution, which is applicable to diverse test rigs with different mechanisms, a novel model-based parameter identification approach using optimized excitation trajectory is proposed in this paper to identify the vehicle dynamic parameters precisely and efficiently. The proposed approach is first verified against a virtual test rig using a universal mechanism. The simulation verification consists of four sections: (a) kinematic analysis, including the analysis of forward/inverse kinematic and singularity architecture; (b) dynamic modeling, in which three kinds of dynamic modeling method are used to derive the dynamic models for parameter identification; (c) trajectory optimization, which aims to search for the optimal trajectory to minimize the sensitivity of parameter identification to measurement noise; and (d) multibody simulation, by which vehicle dynamic parameters are identified based on the virtual test rig in the simulation environment. In addition to the simulation verification, the proposed parameter identification approach is applied to the real test rig (vehicle inertia measuring machine) in laboratory subsequently. Despite the mechanism difference between the virtual test rig and vehicle inertia measuring machine, this approach has shown an excellent portability. The experimental results indicate that the proposed parameter identification approach can effectively identify the vehicle dynamic parameters without a high requirement of movement accuracy.

The parameter identification model considering both geometric parameters and joint stiffness

2019 ◽  
Vol 47 (1) ◽  
pp. 76-81
Author(s):  
Jing Bai ◽  
Le Fan ◽  
Shuyang Zhang ◽  
Zengcui Wang ◽  
Xiansheng Qin
Keyword(s):  
Parameter Identification ◽  
Industrial Robot ◽  
Joint Stiffness ◽  
Geometric Parameters ◽  
Least Square ◽  
Experimental Result ◽  
Positional Accuracy ◽  
Content Type ◽  
The Absolute ◽  
Identification Model

Purpose Both geometric and non-geometric parameters have noticeable influence on the absolute positional accuracy of 6-dof articulated industrial robot. This paper aims to enhance it and improve the applicability in the field of flexible assembling processing and parts fabrication by developing a more practical parameter identification model. Design/methodology/approach The model is developed by considering both geometric parameters and joint stiffness; geometric parameters contain 27 parameters and the parallelism problem between axes 2 and 3 is involved by introducing a new parameter. The joint stiffness, as the non-geometric parameter considered in this paper, is considered by regarding the industrial robot as a rigid linkage and flexible joint model and adds six parameters. The model is formulated as the form of error via linearization. Findings The performance of the proposed model is validated by an experiment which is developed on KUKA KR500-3 robot. An experiment is implemented by measuring 20 positions in the work space of this robot, obtaining least-square solution of measured positions by the software MATLAB and comparing the result with the solution without considering joint stiffness. It illustrates that the identification model considering both joint stiffness and geometric parameters can modify the theoretical position of robots more accurately, where the error is within 0.5 mm in this case, and the volatility is also reduced. Originality/value A new parameter identification model is proposed and verified. According to the experimental result, the absolute positional accuracy can be remarkably enhanced and the stability of the results can be improved, which provide more accurate parameter identification for calibration and further application.

USING AN IMPROVED BEE MEMORY DIFFERENTIAL EVOLUTION ALGORITHM FOR PARAMETER ESTIMATION TO SIMULATE BIOCHEMICAL PATHWAYS

2014 ◽  
Vol 22 (01) ◽  
pp. 101-121 ◽  
Author(s):  
CHUII KHIM CHONG ◽  
MOHD SABERI MOHAMAD ◽  
SAFAAI DERIS ◽  
MOHD SHAHIR SHAMSIR ◽  
LIAN EN CHAI ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Estimation Algorithm ◽  
Convergence Time ◽  
Unknown Parameters ◽  
Estimation Algorithms ◽  
Bee Colony ◽  
De Algorithm ◽  
Cell Cycle Pathway ◽  
Parameter Values

When analyzing a metabolic pathway in a mathematical model, it is important that the essential parameters are estimated correctly. However, this process often faces few problems like when the number of unknown parameters increase, trapping of data in the local minima, repeated exposure to bad results during the search process and occurrence of noisy data. Thus, this paper intends to present an improved bee memory differential evolution (IBMDE) algorithm to solve the mentioned problems. This is a hybrid algorithm that combines the differential evolution (DE) algorithm, the Kalman filter, artificial bee colony (ABC) algorithm, and a memory feature. The aspartate and threonine biosynthesis pathway, and cell cycle pathway are the metabolic pathways used in this paper. For three production simulation pathways, the IBMDE managed to robustly produce the estimated optimal kinetic parameter values with significantly reduced errors. Besides, it also demonstrated faster convergence time compared to the Nelder–Mead (NM), simulated annealing (SA), the genetic algorithm (GA) and DE, respectively. Most importantly, the kinetic parameters that were generated by the IBMDE have improved the production rates of desired metabolites better than other estimation algorithms. Meanwhile, the results proved that the IBMDE is a reliable estimation algorithm.

scholarly journals Adaptations in traditional courtyard houses in Baghdad, Iraq

2017 ◽  
Vol 35 (4) ◽  
pp. 348-363 ◽  
Author(s):  
Rand H.M. Agha ◽  
John M. Kamara
Keyword(s):  
Design Methodology ◽  
Full Range ◽  
Physical Structure ◽  
Structured Interviews ◽  
Content Type ◽  
Case Study Analysis ◽  
Physical Spaces ◽  
Adaptation Model

Purpose The purpose of this paper is to investigate the adaptations that have been made to traditional courtyard houses (TCHs) in Baghdad, Iraq. The aim is to develop an understanding of various factors in the adaptation of these buildings to suit contemporary lifestyles, which will contribute to the wider field of building adaptability. Design/methodology/approach Empirical evidence was collected through case studies of 12 TCHs in the Al-Kadhimiya area of Baghdad, which involved a physical survey of buildings and semi-structured interviews with 24 occupants. Findings Case study analysis show that building adaptability involves both a change to physical spaces and also to lifestyles; with the latter being more likely when there are limitations in how much change can be made to the physical structure. Research limitations/implications The focus of this research is mainly on users’ adaptation of spaces and therefore does not consider the full range of stakeholders involved in the adaptation process. The findings also only apply to the cases considered and may not be applicable to other house types or locations. Originality/value Studies on building adaptability mostly focus on the ease of change to the building fabric, although the role of users is acknowledged. This study provides insights into the complexity and variety of changes that users can make, which are influenced by lifestyles and driven by the need for comfort. These insights are represented in an adaptation model, which can serve as a basis for further research.

A framework for near real-time ROM stockpile modelling to improve blending efficiency

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shi Zhao ◽  
Tien-Fu Lu ◽  
Larissa Statsenko ◽  
Benjamin Koch ◽  
Chris Garcia
Keyword(s):  
High Resolution ◽  
Real Time ◽  
Laser Scanning ◽  
High Efficiency ◽  
Reference Model ◽  
Geometric Model ◽  
Mining Industry ◽  
Management Systems ◽  
Content Type ◽  
Ore Grade

Purpose In the mining industry, a run-of-mine (ROM) stockpile is a temporary storage unit, but it is also widely accepted as an effective method to reduce the short-term variations of ore grade. However, tracing ore grade at ROM stockpiles accurately using most current fleet management systems is challenging, due to insufficient information available in real time. This study aims to build a three-dimensional (3D) model for ROM stockpiles continuously based on fine-grained grade information through integrating data from a number of ore grade tracking sources. Design/methodology/approach Following a literature review, a framework for a new stockpile management system is proposed. In this system, near real-time high-resolution 3D ROM stockpile models are created based on dump/load locations measured from global positioning system sensors. Each stockpile model contains a group of layers which are separated by different qualities. Findings Acquiring the geometric shapes of all the layers in a stockpile and cuts made by front wheel loaders provides a better understanding about the quality and quality distribution within a stockpile when it is stacked/reclaimed. Such a ROM stockpile model can provide information on predicating ore blend quality with high accuracy and high efficiency. Furthermore, a 3D stockyard model created based on such ROM stockpile models can help organisations optimise material flow and reduce the cost. Research limitations/implications The modelling algorithm is evaluated using a laboratory scaled stockpile at this stage. The authors expect to scan a real stockpile and create a reference model from it. Meanwhile, the geometric model cannot represent slump or collapse during reclaiming faithfully. Therefore, the model is expected to be reconcile monthly using laser scanning data. Practical implications The proposed model is currently translated to the operations at OZ Minerals. The use of such model will reduce the handling costs and improve the efficiency of existing grade management systems in the mining industry. Originality/value This study provides a solution to build a near real-time high-resolution multi-layered 3D stockpile model through using currently available information and resources. Such novel and low-cost stockpile model will improve the production rates with good output product quality control.

Quality 4.0 conceptualisation and theoretical understanding: a global exploratory qualitative study

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jiju Antony ◽  
Olivia McDermott ◽  
Michael Sony
Keyword(s):  
Critical Success Factors ◽  
Success Factors ◽  
Full Range ◽  
Senior Management ◽  
Successful Implementation ◽  
Motivating Factors ◽  
Theoretical Understanding ◽  
Content Type ◽  
Critical Success ◽  
Management Professionals

PurposeQuality 4.0 has a unique potential to create a competitive advantage for organisations by improving customer experience and enhancing profitability. The purpose of this study is to examine Quality 4.0, the9; benefits, motivating factors, critical success factors and the skills required by quality professionals in the successful implementation of Quality 4.0. The study also investigates the organisational readiness factors9 and challenges that need to be addressed before Quality 4.0 adoption and assess their importance.Design/methodology/approachA qualitative interview approach was utilised by interviewing a panel of senior management, engineering and continuous improvement (CI); professionals working in leading companies in Asia, Europe and America who are currently deploying Quality 4.0.FindingsThis study provides a theoretical base for the Quality 4.0 body of knowledge in terms of an organisation’s adoption and overcoming implementation challenges and providing examples of Quality 4.0 application. Organisations can use this study to understand what Quality 4.0 means to industry, the benefits and motivating factors for implementing, the Critical Success Factors, challenges, the organisational readiness factors and the role of leadership in a Quality 4.0 deployment. In addition, the study looks at the skills required by future Quality 4.0 professionals in terms of hard skills, soft skills and a curriculum for educating future quality management professionals. The respondents cited that predictive analytics, sensors and tracking, and electronic feedback loops are the most critical technologies for driving Quality 4.0.Research limitations/implicationsOne of the limitations of this research was that as this area is a nascent area the researchers were limited in their literature review. The second limitation was that the study was based on 12 interviews. A more comprehensive longitudinal study would yield more data so that better and robust conclusions can be derived from the study.Originality/valueThis is the first empirical study on Quality 4.0, which captures the viewpoints of senior management professionals on a full range of topics related to Quality 4.0 motivation for deployment, implementation and readiness for its adoption.

Parameter identification for load frequency control using fuzzy FOPID in power system

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ram Kumar ◽  
Afzal Sikander
Keyword(s):  
Fuzzy Logic ◽  
Power System ◽  
Parameter Identification ◽  
Fractional Order ◽  
Optimization Technique ◽  
Load Frequency Control ◽  
Control Law ◽  
Content Type ◽  
Load Frequency ◽  
Frequency Controller

Purpose This paper aims to suggest the parameter identification of load frequency controller in power system. Design/methodology/approach The suggested control approach is established using fuzzy logic to design a fractional order load frequency controller. A new suitable control law is developed using fuzzy logic, and based on this developed control law, the unknown parameters of the fractional order proportional integral derivative (FOPID) controller are derived using an optimization technique, which is being used by minimizing the integral square error. In addition, to confirm the effectiveness of the proposed control design approach, numerous simulation tests were carried out on an actual single-area power system. Findings The obtained results reveal the superiority of the suggested controller as compared to the recently developed controllers with regard to time response specifications and quantifiable indicators. Additionally, the potential of the suggested controller is also observed by improving the load disturbance rejections under plant parametric uncertainty. Originality/value To the best of the authors’ knowledge, the work is not published anywhere else.

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