Modal Control of Fast Large-Scale Robot Motions

1987 ◽  
Vol 109 (2) ◽  
pp. 80-87 ◽  
Author(s):  
Y. Stepanenko
Keyword(s):  
Large Scale ◽  
Closed Loop ◽  
Dynamic Models ◽  
General Structure ◽  
Numerical Differentiation ◽  
Multiprocessor System ◽  
Model Parameters ◽  
Control Law ◽  
Prismatic Joints ◽  
Linear State

This study concentrates on the following topics in linear state-feedback robotic control: an algorithm for the generation of linearized robot models, a control law providing a desired eigenstructure for the linearized models, and the eigenvalue sensitivity to changes of the linearized model parameters. The algorithm allows the computer generation of linearized dynamic models for any articulated mechanism with revolute or prismatic joints. It does not include numerical differentiation and is based on a compound-vector technique and Newton-Euler dynamics. The control law allows the arbitrary assignment of all eigenvalues and certain entries of the closed-loop eigenvectors. The general structure of the closed-loop modal matrix and the flexibility available in eigenvector assignment are considered. A sensitivity analysis is given for the decoupled control law resulting from a particular eigenvector assignment. An experimental vertion of the developed modal controller was implemented on a multiprocessor system based on Motorola 68020 microprocessors. Details of the implementation and results of robot motion simulation are also included.

scholarly journals Patient-specific logic models of signaling pathways from screenings on cancer biopsies to prioritize personalized combination therapies

2018 ◽  
Author(s):  
Federica Eduati ◽  
Patricia Jaaks ◽  
Christoph A. Merten ◽  
Mathew J. Garnett ◽  
Julio Saez- Rodriguez
Keyword(s):  
Signaling Pathways ◽  
Large Scale ◽  
Dynamic Models ◽  
Ex Vivo ◽  
Response To Therapy ◽  
Mechanistic Modeling ◽  
Patient Specific ◽  
Model Parameters ◽  
Specific Response ◽  
Tumor Stages

AbstractMechanistic modeling of signaling pathways mediating patient-specific response to therapy can help to unveil resistance mechanisms and improve therapeutic strategies. Yet, creating such models for patients, in particular for solid malignancies, is challenging. A major hurdle to build these models is the limited material available, that precludes the generation of large-scale perturbation data. Here, we present an approach that couples ex vivo high-throughput screenings of cancer biopsies using microfluidics with logic-based modeling to generate patient-specific dynamic models of extrinsic and intrinsic apoptosis signaling pathways. We used the resulting models to investigate heterogeneity in pancreatic cancer patients, showing dissimilarities especially in the PI3K-Akt pathway. Variation in model parameters reflected well the different tumor stages. Finally, we used our dynamic models to efficaciously predict new personalized combinatorial treatments. Our results suggest our combination of microfluidic experiments and mathematical model can be a novel tool toward cancer precision medicine.

scholarly journals Static and Dynamic Characterization and Control of a High-Performance Electro-Hydraulic Actuator

Actuators ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 46
Author(s):  
Govind N. Sahu ◽  
Suyash Singh ◽  
Aditya Singh ◽  
Mohit Law
Keyword(s):  
Machine Tool ◽  
High Performance ◽  
Closed Loop ◽  
Dynamic Models ◽  
Static Model ◽  
Dynamical Model ◽  
Model Parameters ◽  
Closed Loop Control ◽  
Hydraulic Actuator ◽  
Loop Control

This paper characterizes the static, dynamic, and controlled behavior of a high-performance electro-hydraulic actuator to assess its suitability for use in evaluating machine tool behavior. The actuator consists of a double-acting piston and cylinder arrangement controlled by a servo valve and a separate rear chamber controlled by a separate valve, designed to work in conjunction to generate static forces of up to 7000 N that can be superposed with dynamic forces of up to ±1500 N. This superposition of periodic forces with a non-zero mean makes the actuator capable of applying realistic loading conditions like those experienced by machines during cutting processes. To characterize the performance of this actuator, linearized static and dynamic models are described. Since experiments with the actuator exhibit nonlinear characteristics, the linearized static model is expanded to include the influence of nonlinearities due to flow, leakages, saturations, and due to friction and hysteresis. Since all major nonlinearities are accounted for in the expanded static model, the dynamical model remains linear. Unknown static and dynamical model parameters are calibrated from experiments, and the updated models are observed to capture experimentally observed behavior very well. Validated models are used to tune the proportional and integral gains for the closed-loop control strategy, and the model-based tuning in turn guides appropriate closed-loop control of the actuator to increase its bandwidth to 200 Hz. The statically and dynamically characterized actuator can aid machine tool structural testing. Moreover, the validated models can instruct the design and development of other higher-performance electro-hydraulic actuators, guide the conversion of the actuator into a damper, and also test other advanced control strategies to further improve actuator performance.

scholarly journals Efficient parameterization of large-scale dynamic models based on relative measurements

2019 ◽  
Author(s):  
Leonard Schmiester ◽  
Yannik Schälte ◽  
Fabian Fröhlich ◽  
Jan Hasenauer ◽  
Daniel Weindl
Keyword(s):  
Large Scale ◽  
Dynamic Models ◽  
Error Model ◽  
Supplementary Information ◽  
Reaction Networks ◽  
Model Parameters ◽  
Biological Processes ◽  
Wide Range ◽  
Scale Models ◽  
Relative Measurements

Abstract Motivation Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale. Results Here, we propose a novel hierarchical approach combining (i) the efficient analytic evaluation of optimal scaling, offset and error model parameters with (ii) the scalable evaluation of objective function gradients using adjoint sensitivity analysis. We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation model (>1000 state variables, >4000 parameters) using relative protein, phosphoprotein and viability measurements. The hierarchical formulation improves optimizer performance considerably. Furthermore, we show that this approach allows estimating error model parameters with negligible computational overhead when no experimental estimates are available, providing an unbiased way to weight heterogeneous data. Overall, our hierarchical formulation is applicable to a wide range of models, and allows for the efficient parameterization of large-scale models based on heterogeneous relative measurements. Availability and implementation Supplementary code and data are available online at http://doi.org/10.5281/zenodo.3254429 and http://doi.org/10.5281/zenodo.3254441. Supplementary information Supplementary data are available at Bioinformatics online.

Experimental Validation of a Robust H∞ Control Method on Miniaturized Optical Image Stabilizer Prototypes

2020 ◽  
Vol 142 (12) ◽  
Author(s):  
Alireza Alizadegan ◽  
Pan Zhao ◽  
Ryozo Nagamune ◽  
Mu Chiao
Keyword(s):  
Large Scale ◽  
Closed Loop ◽  
Control Method ◽  
Controller Design ◽  
Design Method ◽  
Optical Image ◽  
Small Scale ◽  
Practical Implementation ◽  
Model Parameters ◽  
Experimental Frequency

Abstract This paper validates a robust H∞ controller design method, experimentally, on miniaturized prototypes of the magnetically actuated lens-tilting optical image stabilizers (OISs) with product variabilities. Five small-scale OIS prototypes with product variations are constructed by three-dimensional (3D) printing. For the prototypes, the model parameters are identified based on experimental frequency response data of the prototypes. Using the identified model, a robust H∞ controller is designed to guarantee the robust stability of the closed-loop system and to optimize the closed-loop performance. The experimental results reveal larger and more complex uncertainties in miniaturized OISs with mass-produced parts compared to large-scale prototypes. Despite the increased amount of uncertainties, it is demonstrated that the robust H∞ controller still outperforms the conventional controllers in terms of robust closed-loop stability, performance, and controller order for practical implementation on a mobile phone device.

scholarly journals Mini-batch optimization enables training of ODE models on large-scale datasets

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Paul Stapor ◽  
Leonard Schmiester ◽  
Christoph Wierling ◽  
Simon Merkt ◽  
Dilan Pathirana ◽  
...  
Keyword(s):  
Large Scale ◽  
Dynamic Models ◽  
Dynamic Modelling ◽  
Optimization Methods ◽  
Scale Model ◽  
Model Parameters ◽  
Scaling Properties ◽  
Ode Models ◽  
Cellular Signal ◽  
Order Of Magnitude

AbstractQuantitative dynamic models are widely used to study cellular signal processing. A critical step in modelling is the estimation of unknown model parameters from experimental data. As model sizes and datasets are steadily growing, established parameter optimization approaches for mechanistic models become computationally extremely challenging. Mini-batch optimization methods, as employed in deep learning, have better scaling properties. In this work, we adapt, apply, and benchmark mini-batch optimization for ordinary differential equation (ODE) models, thereby establishing a direct link between dynamic modelling and machine learning. On our main application example, a large-scale model of cancer signaling, we benchmark mini-batch optimization against established methods, achieving better optimization results and reducing computation by more than an order of magnitude. We expect that our work will serve as a first step towards mini-batch optimization tailored to ODE models and enable modelling of even larger and more complex systems than what is currently possible.

Assessment of bioenergy development potential in agricultural enterprises

2020 ◽  
pp. 165-171
Author(s):  
Iryna Hryhoruk
Keyword(s):  
Renewable Energy ◽  
Large Scale ◽  
Agricultural Waste ◽  
General Structure ◽  
Geographical Location ◽  
Economic Assessment ◽  
Energy Resources ◽  
Industrial Complex ◽  
Renewable Energy Resources ◽  
Energy Potential

Exhaustion of traditional energy resources, their uneven geographical location, and catastrophic changes in the environment necessitate the transition to renewable energy resources. Moreover, Ukraine's economy is critically dependent on energy exports, and in some cases, the dependence is not only economic but also political, which in itself poses a threat to national security. One of the ways to solve this problem is the large-scale introduction and use of renewable energy resources, bioenergy in particular. The article summarizes and offers methods for assessing the energy potential of agriculture. In our country, a significant amount of biomass is produced every year, which remains unused. A significant part is disposed of due to incineration, which significantly harms the environment and does not allow earning additional funds. It is investigated that the bioenergy potential of agriculture depends on the geographical distribution and varies in each region of Ukraine. Studies have shown that as of 2019 the smallest share in the total amount of conventional fuel that can be obtained from agricultural waste and products suitable for energy production accounts for Zakarpattya region - 172.5 thousand tons. (0.5% of the total) and Chernivtsi region - 291.3 thousand tons. (0.9%). Poltava region has the greatest potential - 2652.2 thousand tons. (7.8%) and Vinnytsia - 2623.7 thousand tons. (7.7%). It should be noted that the use of the energy potential of biomass in Ukraine can be called unsatisfactory. The share of biomass in the provision of primary energy consumption is very small. For bioenergy to occupy its niche in the general structure of the agro-industrial complex, it is necessary to develop mechanisms for its stimulation. In addition, an effective strategy for the development of the bioenergy sector of agriculture is needed. The article considers the general energy potential of agriculture, its indicative structure. The analysis is also made in terms of areas. In addition, an economic assessment of the possible use of existing potential is identified.

scholarly journals Why ability point estimates can be pointless: a primer on using skill measures from large-scale assessments in secondary analyses

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Clemens M. Lechner ◽  
Nivedita Bhaktha ◽  
Katharina Groskurth ◽  
Matthias Bluemke
Keyword(s):  
Measurement Error ◽  
Statistical Models ◽  
Test Scores ◽  
Large Scale ◽  
Equation Modeling ◽  
Model Parameters ◽  
Advantages And Disadvantages ◽  
Point Estimates ◽  
Secondary Analyses ◽  
Large Scale Assessments

AbstractMeasures of cognitive or socio-emotional skills from large-scale assessments surveys (LSAS) are often based on advanced statistical models and scoring techniques unfamiliar to applied researchers. Consequently, applied researchers working with data from LSAS may be uncertain about the assumptions and computational details of these statistical models and scoring techniques and about how to best incorporate the resulting skill measures in secondary analyses. The present paper is intended as a primer for applied researchers. After a brief introduction to the key properties of skill assessments, we give an overview over the three principal methods with which secondary analysts can incorporate skill measures from LSAS in their analyses: (1) as test scores (i.e., point estimates of individual ability), (2) through structural equation modeling (SEM), and (3) in the form of plausible values (PVs). We discuss the advantages and disadvantages of each method based on three criteria: fallibility (i.e., control for measurement error and unbiasedness), usability (i.e., ease of use in secondary analyses), and immutability (i.e., consistency of test scores, PVs, or measurement model parameters across different analyses and analysts). We show that although none of the methods are optimal under all criteria, methods that result in a single point estimate of each respondent’s ability (i.e., all types of “test scores”) are rarely optimal for research purposes. Instead, approaches that avoid or correct for measurement error—especially PV methodology—stand out as the method of choice. We conclude with practical recommendations for secondary analysts and data-producing organizations.

scholarly journals Power-to-Green Methanol via CO2 Hydrogenation—A Concept Study Including Oxyfuel Fluidized Bed Combustion of Biomass

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4638
Author(s):  
Simon Pratschner ◽  
Pavel Skopec ◽  
Jan Hrdlicka ◽  
Franz Winter
Keyword(s):  
Large Scale ◽  
Positive Impact ◽  
Renewable Energy Sources ◽  
Air Separation ◽  
Model Parameters ◽  
Processing Unit ◽  
Fluidized Bed Combustion ◽  
Separation Unit ◽  
Oxyfuel Combustion ◽  
Wind Park

A revolution of the global energy industry is without an alternative to solving the climate crisis. However, renewable energy sources typically show significant seasonal and daily fluctuations. This paper provides a system concept model of a decentralized power-to-green methanol plant consisting of a biomass heating plant with a thermal input of 20 MWth. (oxyfuel or air mode), a CO2 processing unit (DeOxo reactor or MEA absorption), an alkaline electrolyzer, a methanol synthesis unit, an air separation unit and a wind park. Applying oxyfuel combustion has the potential to directly utilize O2 generated by the electrolyzer, which was analyzed by varying critical model parameters. A major objective was to determine whether applying oxyfuel combustion has a positive impact on the plant’s power-to-liquid (PtL) efficiency rate. For cases utilizing more than 70% of CO2 generated by the combustion, the oxyfuel’s O2 demand is fully covered by the electrolyzer, making oxyfuel a viable option for large scale applications. Conventional air combustion is recommended for small wind parks and scenarios using surplus electricity. Maximum PtL efficiencies of ηPtL,Oxy = 51.91% and ηPtL,Air = 54.21% can be realized. Additionally, a case study for one year of operation has been conducted yielding an annual output of about 17,000 t/a methanol and 100 GWhth./a thermal energy for an input of 50,500 t/a woodchips and a wind park size of 36 MWp.

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