Machine Learning Application for Oscillation Detection in Control Loops

2020 ◽  
pp. 1067-1075
Author(s):  
Sachin Sharma ◽  
Vineet Kumar ◽  
K. P. S. Rana
Keyword(s):  
Machine Learning ◽  
Control Loops ◽  
Oscillation Detection

Roboterbasierte spanende Bearbeitung*/Robot-Based Milling Operation. Machine Learning Algorithm for a model-based feed-forward torque control

2019 ◽  
Vol 109 (05) ◽  
pp. 352-357
Author(s):  
C. Brecher ◽  
L. Gründel ◽  
L. Lienenlüke ◽  
S. Storms
Keyword(s):  
Machine Learning ◽  
Position Control ◽  
Learning Algorithm ◽  
Milling Process ◽  
Industrial Robots ◽  
Torque Control ◽  
Feed Forward ◽  
Control Loops ◽  
Model Based ◽  
Machine Learning Approach

Die Lageregelung von konventionellen Industrierobotern ist nicht auf den dynamischen Fräsprozess ausgelegt. Eine Möglichkeit, das Verhalten der Regelkreise zu optimieren, ist eine modellbasierte Momentenvorsteuerung, welche in dieser Arbeit aufgrund vieler Vorteile durch einen Machine-Learning-Ansatz erweitert wird. Hierzu wird die Umsetzung in Matlab und die simulative Evaluation erläutert, die im Anschluss das Potenzial dieses Konzeptes bestätigt.   The position control of conventional industrial robots is not designed for the dynamic milling process. One possibility to optimize the behavior of the control loops is a model-based feed-forward torque control which is supported by a machine learning approach due to many advantages. The implementation in Matlab and the simulative evaluation are explained, which subsequently confirms the potential of this concept.

scholarly journals Safety model construction for a complex automatic transportation system

Dependability ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 31-37
Author(s):  
А. V. Ozerov ◽  
А. М. Olshansky
Keyword(s):  
Machine Learning ◽  
Systems Approach ◽  
Process Analysis ◽  
Hazard Analysis ◽  
Autonomous Systems ◽  
Safety Evaluation ◽  
Transportation Systems ◽  
Control Loops ◽  
Transport Control ◽  
Local Risk

The Aim of the paper is to consider approaches to the analysis of a safety model of complex multi-loop transportation systems comprising not completely supervised subsystems. Method. For the description of a safety model, the paper uses systems theoretic process analysis (STPA) methods and the principles specified in ISO/PAS 21448:2019 (SOTIF). Result. The paper shows drawbacks of the FTA and FMEA local risk analysis methods and demonstrates a demand for some universal approach based on the combination of STPA and control theory. It gives an overview of the major stages of such analysis for the safety model of complex transportation systems exemplified by the Moscow Central Circle, which provide a feedback for safety evaluation of a transport control system under development. The paper analyzes the feasibility of using a virtual model for control purposes in the form of a so-called “supervised artificial neural network”.Conclusion. Today, railways are actively testing autonomous systems (with no driver onboard) that apply as their subsystems automatic perception modules using machine learning. The introduction of the latter into the control loop complicates the task of hazard analysis and safety evaluation of such systems using conventional FTA and FMEA methods. The construction of a safety model of such complex multi-loop transportation systems comprising not completely supervised subsystems that use machine learning methods with not completely predictable behavior requires the application of a systems approach to the analysis of unsafe scenarios along with the compilation of a scenario library and the formalization of a hazard model’s description, pertaining to the boundaries of various control loops as well, in order to reduce the regions of unknown unsafe scenarios for autonomous transportation systems under development.

Introduction and Application Aspects of Machine Learning for Model Reference Adaptive Control With Polynomial Neurons

2020 ◽  
pp. 59-84
Author(s):  
Ivo Bukovsky ◽  
Peter M. Benes ◽  
Martin Vesely
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Plant Identification ◽  
Control Loops ◽  
Efficient Machine ◽  
Incremental Gradient Descent ◽  
And Control ◽  
Model Reference Adaptive ◽  
Neuro Controller

This chapter recalls the nonlinear polynomial neurons and their incremental and batch learning algorithms for both plant identification and neuro-controller adaptation. Authors explain and demonstrate the use of feed-forward as well as recurrent polynomial neurons for system approximation and control via fundamental, though for practice efficient machine learning algorithms such as Ridge Regression, Levenberg-Marquardt, and Conjugate Gradients, authors also discuss the use of novel optimizers such as ADAM and BFGS. Incremental gradient descent and RLS algorithms for plant identification and control are explained and demonstrated. Also, novel BIBS stability for recurrent HONUs and for closed control loops with linear plant and nonlinear (HONU) controller is discussed and demonstrated.

Oscillation Detection in Process Industries by a Machine Learning-Based Approach

2019 ◽  
Vol 58 (31) ◽  
pp. 14180-14192 ◽  
Author(s):  
Jônathan W. V. Dambros ◽  
Jorge O. Trierweiler ◽  
Marcelo Farenzena ◽  
Marius Kloft
Keyword(s):  
Machine Learning ◽  
Process Industries ◽  
Oscillation Detection

Expressing uncertainty in neural networks for production systems

2021 ◽  
Vol 69 (3) ◽  
pp. 221-230
Author(s):  
Samim Ahmad Multaheb ◽  
Bernd Zimmering ◽  
Oliver Niggemann
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Production Systems ◽  
Control Loops ◽  
Automation Systems ◽  
High Level

Abstract The application of machine learning, especially of trained neural networks, requires a high level of trust in their results. A key to this trust is the network’s ability to assess the uncertainty of the computed results. This is a prerequisite for the use of such networks in closed-control loops and in automation systems. This paper describes approaches for enabling neural networks to automatically learn the uncertainties of their results.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining
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