Parallel Recursive Estimation Algorithm for Dynamic Model Parameters of a Robot ARM

1992 ◽  
Vol 25 (29) ◽  
pp. 39-44
Author(s):  
G.M. Asher ◽  
H. Temeltas
Keyword(s):  
Dynamic Model ◽  
Estimation Algorithm ◽  
Recursive Estimation ◽  
Model Parameters ◽  
Robot Arm

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.

The series elastic gripper design, object detection and recognition by touch

2021 ◽  
pp. 1-33
Author(s):  
Ozan Kaya ◽  
Gokce Burak Taglioglu ◽  
Seniz Ertugrul
Keyword(s):  
Synthetic Data ◽  
Estimation Algorithm ◽  
Robot Arm ◽  
Force Estimation ◽  
Tactile Sensors ◽  
Stl File ◽  
Detection Of Objects ◽  
Set Up ◽  
Force Calculation ◽  
Series Elastic

Abstract In recent years, robotic applications have been improved for better object manipulation and collaboration with human. With this motivation, the detection of objects has been studied with serial elastic parallel gripper by simple touching in case of no visual data available. A series elastic gripper, capable of detecting geometric properties of objects is designed using only elastic elements and absolute encoders instead of tactile or force/torque sensors. The external force calculation is achieved by employing an estimation algorithm. Different objects are selected for trials for recognition. A Deep Neural Network model is trained by synthetic data extracted from STL file of selected objects . For experimental set-up, the series elastic parallel gripper is mounted on a Staubli RX160 robot arm and objects are placed in pre-determined locations in the workspace. All objects are successfully recognized using the gripper, force estimation and the DNN model. The best DNN model capable of recognizing different objects with the average prediction value ranging from 71% to 98%. Hence the proposed design of gripper and the algorithm achieved the recognition of selected objects without need for additional force/torque or tactile sensors.

scholarly journals A Fuzzy State-of-Charge Estimation Algorithm Combining Ampere-Hour and an Extended Kalman Filter for Li-Ion Batteries Based on Multi-Model Global Identification

2018 ◽  
Vol 8 (11) ◽  
pp. 2028 ◽  
Author(s):  
Xin Lai ◽  
Dongdong Qiao ◽  
Yuejiu Zheng ◽  
Long Zhou
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Estimation Algorithm ◽  
State Of Charge ◽  
Distribution Area ◽  
Model Parameters ◽  
Model Accuracy ◽  
Soc Estimation ◽  
Fuzzy Fusion ◽  
Fuzzy State

The popular and widely reported lithium-ion battery model is the equivalent circuit model (ECM). The suitable ECM structure and matched model parameters are equally important for the state-of-charge (SOC) estimation algorithm. This paper focuses on high-accuracy models and the estimation algorithm with high robustness and accuracy in practical application. Firstly, five ECMs and five parameter identification approaches are compared under the New European Driving Cycle (NEDC) working condition in the whole SOC area, and the most appropriate model structure and its parameters are determined to improve model accuracy. Based on this, a multi-model and multi-algorithm (MM-MA) method, considering the SOC distribution area, is proposed. The experimental results show that this method can effectively improve the model accuracy. Secondly, a fuzzy fusion SOC estimation algorithm, based on the extended Kalman filter (EKF) and ampere-hour counting (AH) method, is proposed. The fuzzy fusion algorithm takes advantage of the advantages of EKF, and AH avoids the weaknesses. Six case studies show that the SOC estimation result can hold the satisfactory accuracy even when large sensor and model errors exist.

Analytical and experimental analysis of axial force generated by a drive shaft system

2020 ◽  
Vol 234 (4) ◽  
pp. 691-706 ◽  
Author(s):  
Huayuan Feng ◽  
Subhash Rakheja ◽  
Wen-Bin Shangguan
Keyword(s):  
Dynamic Model ◽  
Axial Force ◽  
Friction Model ◽  
Drive Shaft ◽  
Contact Model ◽  
Model Parameters ◽  
Shaft System ◽  
Multibody Dynamic ◽  
Generated Axial Force ◽  
Operational Factors

The drive shaft system with a tripod joint is known to cause lateral vibration in a vehicle due to the axial force generated by various contact pairs of the tripod joint. The magnitude of the generated axial force, however, is related to various operating factors of the drive shaft system in a complex manner. The generated axial force due to a drive shaft system with a tripod joint and a ball joint was experimentally characterized considering ranges of operational factors, namely, the input toque, the shaft rotational speed, the articulation angle, and the friction. The data were analyzed to establish an understanding of the operational factors on the generated axial force. Owing to the observed significant effects of all the factors, a multibody dynamic model of the drive shaft system was formulated for predicting generated axial force under different operating conditions. The model integrated the roller–track contact model and the velocity-based friction model. Based on a quasi-static finite element model, a new methodology was proposed for identifying the roller–track contact model parameters, namely, the contact stiffness and force index. To further enhance the calculation accuracy of the multibody dynamic model, a new methodology for identifying the friction model parameters and the force index was proposed by using the measured data. The validity of the model was demonstrated by comparing the model-predicted and measured magnitudes of generated axial force for the ranges of operating factors considered. The results showed that the generated axial force of the drive shaft system can be calculated more accurately and effectively by using the identified friction and contact parameters in the paper.

“Sintering” Models and In-Situ Experiments: Data Assimilation for Microstructure Prediction in SLS Additive Manufacturing of Nylon Components

MRS Advances ◽  
2020 ◽  
Vol 5 (29-30) ◽  
pp. 1593-1601
Author(s):  
W. Steven Rosenthal ◽  
Francesca C. Grogan ◽  
Yulan Li ◽  
Erin I. Barker ◽  
Josef F. Christ ◽  
...  
Keyword(s):  
Adaptive Sampling ◽  
Selective Laser Sintering ◽  
Phase Field Model ◽  
Estimation Algorithm ◽  
Model Parameters ◽  
Multiple Data ◽  
In Situ Experiments ◽  
Parameter Estimation Algorithm ◽  
Microstructure Prediction

ABSTRACTSelective laser sintering methods are workhorses for additively manufacturing polymer-based components. The ease of rapid prototyping also means it is easy to produce illicit components. It is necessary to have a data-calibrated in-situ physical model of the build process in order to predict expected and defective microstructure characteristics that inform component provenance. Toward this end, sintering models are calibrated and characteristics such as component defects are explored. This is accomplished by assimilating multiple data streams, imaging analysis, and computational model predictions in an adaptive Bayesian parameter estimation algorithm. From these data sources, along with a phase-field model, bulk porosity distributions are inferred. Model parameters are constrained to physically-relevant search directions by sensitivity analysis, and then matched to predictions using adaptive sampling. Using this feedback loop, data-constrained estimates of sintering model parameters along with uncertainty bounds are obtained.

scholarly journals Rotor flux oriented control of induction machine based drives with compensation for the variation of all machine parameters

2013 ◽  
Vol 61 (2) ◽  
pp. 309-324 ◽  
Author(s):  
G. Extremiana ◽  
G. Abad ◽  
J. Arza ◽  
J. Chivite-Zabalza ◽  
I. Torre
Keyword(s):  
Dynamic Performance ◽  
Estimation Algorithm ◽  
Model Parameters ◽  
Electrical Machine ◽  
Saturation Curve ◽  
Machine Model ◽  
Rotor Resistance ◽  
Iron Losses ◽  
Rotor Flux ◽  
On Line

Abstract The performance of rotor flux oriented induction motor drives, widely used these days, relies on the accurate knowledge of key machine parameters. In most industrial drives, the rotor resistance, subject to temperature variations, is estimated on-line due to its significant influence on the control behaviour. However, the rest of the model parameters are also subject to slow variations, determined mainly by the operating point of the machine, compromising the dynamic performance and the accuracy of the torque estimation. This paper presents an improved rotor-resistance on-line estimation algorithm that contemplates the iron losses of the electrical machine, the iron saturation curve and the mechanical losses. In addition, the control also compensates the rest of the key machine parameters such as the leakage and magnetizing inductances and the iron losses. These parameters are measured by an off-line estimation procedure and stored in look up-tables used by the control. The paper begins by presenting the machine model and the proposed rotor flux oriented control strategy. Subsequently, the off-line parameter measurement procedure is described. Finally, the algorithm is extensively evaluated and validated experimentally on a 15 kW test bench

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