Unstable Systems and Control Solutions

This article discusses the work that resulted in the development of two battery-powered self-propelled electric mining machines intended for operation in the conditions of a Polish copper ore mine. Currently, the global mining industry is seeing a growing interest in battery-powered electric machines, which are replacing solutions powered by internal combustion engines. The cooperation of Mine Master, Łukasiewicz Research Network—Institute of Innovative Technologies EMAG and AGH University of Science and Technology allowed carrying out a number of works that resulted in the production of two completely new machines. In order to develop the requirements and assumptions for the designed battery-powered propulsion systems, underground tests of the existing combustion machines were carried out. Based on the results of these tests, power supply systems and control algorithms were developed and verified in a virtual environment. Next, a laboratory test stand for validating power supply systems and control algorithms was developed and constructed. The tests were aimed at checking all possible situations in which the battery gets discharged as a result of the machine’s ride or operation and when it is charged from the mine’s mains or with energy recovered during braking. Simulations of undesirable situations, such as fluctuations in the supply voltage or charging power limitation, were also carried out at the test stand. Positive test results were obtained. Finally, the power supply systems along with control algorithms were implemented and tested in the produced battery-powered machines during operational trials. The power systems and control algorithms are universal enough to be implemented in two different types of machines. Both machines were specially designed to substitute diesel machines in the conditions of a Polish ore mine. They are the lowest underground battery-powered drilling and bolting rigs with onboard chargers. The machines can also be charged by external fast battery chargers.

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Data-driven science and engineering: machine learning, dynamical systems, and control (brunton, steven l. and kutz, j. nathan; 2020) [bookshelf]

IEEE Control Systems Magazine ◽

10.1109/mcs.2021.3076544 ◽

2021 ◽

Vol 41 (4) ◽

pp. 95-102

Author(s):

Dirk M. Luchtenburg

Keyword(s):

Machine Learning ◽

Dynamical Systems ◽

Data Driven ◽

Science And Engineering ◽

Systems And Control ◽

And Control

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An Introduction to Development of Centralized and Distributed Stochastic Approximation Algorithm with Expanding Truncations

Algorithms ◽

10.3390/a14060174 ◽

2021 ◽

Vol 14 (6) ◽

pp. 174

Author(s):

Wenxiao Zhao

Keyword(s):

Approximation Algorithm ◽

Stochastic Approximation ◽

Probabilistic Method ◽

Principal Component ◽

Stochastic Approximation Algorithm ◽

The Past ◽

Systems And Control ◽

Ode Method ◽

Centralized Algorithm ◽

And Control

The stochastic approximation algorithm (SAA), starting from the pioneer work by Robbins and Monro in 1950s, has been successfully applied in systems and control, statistics, machine learning, and so forth. In this paper, we will review the development of SAA in China, to be specific, the stochastic approximation algorithm with expanding truncations (SAAWET) developed by Han-Fu Chen and his colleagues during the past 35 years. We first review the historical development for the centralized algorithm including the probabilistic method (PM) and the ordinary differential equation (ODE) method for SAA and the trajectory-subsequence method for SAAWET. Then, we will give an application example of SAAWET to the recursive principal component analysis. We will also introduce the recent progress on SAAWET in a networked and distributed setting, named the distributed SAAWET (DSAAWET).

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