Inversion of Separable Kernel Operator and Its Application in Control Synthesis

The traditional hydrothermal method to prepare zeolite will inevitably use a large amount of water as a solvent, which will lead to higher autogenous pressure, low efficiency, and wastewater pollution. The solvent-free method can be used to synthesize various types of zeolites by mechanical mixing, grinding, and heating of solid raw materials, which exhibits the apparent advantages of high yield, low pollution, and high efficiency. This review mainly introduces the development process of solvent-free synthesis, preparation of hierarchical zeolite, morphology control, synthesis mechanism and applications of solvent-free methods. It can be believed that solvent-free methods will become a research focus and have enormous industrial application potential.

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Numerical Study of an Oscillating-Wing Wingmill for Ocean Current Energy Harvesting: Fluid-Solid-Body Interaction with Feedback Control

Journal of Marine Science and Engineering ◽

10.3390/jmse9010023 ◽

2020 ◽

Vol 9 (1) ◽

pp. 23

Author(s):

David Balam-Tamayo ◽

Carlos Málaga ◽

Bernardo Figueroa-Espinoza

Keyword(s):

Energy Harvesting ◽

Numerical Study ◽

Angle Of Attack ◽

Ocean Current ◽

Control Synthesis ◽

Dimensionless Number ◽

Control Law ◽

Loop Control ◽

Power Extraction ◽

Current Energy

The performance and flow around an oscillating foil device for current energy extraction (a wingmill) was studied through numerical simulations. OpenFOAM was used in order to study the two-dimensional (2D) flow around a wingmill. A closed loop control law was coded in order to follow a reference angle of attack. The objective of this control law is to modify the angle of attack in order to enhance the lift force (and increase power extraction). Dimensional analysis suggests a compromise between the generator (or damper) stiffness and actuator/control gains, so a parametric study was carried out while using a new dimensionless number, called B, which represents this compromise. It was found that there is a maximum on the efficiency curve in terms of the aforementioned dimensionless parameter. The lessons that are learned from this fluid-structure and feedback coupling are discussed; this interaction, combined with the feedback dynamics, may trigger dynamic stall, thus decreasing the performance. Moreover, if the control strategy is not carefully selected, then the energy spent on the actuator may affect efficiency considerably. This type of simulation could allow for the system identification, control synthesis, and optimization of energy harvesting devices in future studies.

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Robust Stabilization of Interval Plants with Uncertain Time-Delay Using the Value Set Concept

Mathematics ◽

10.3390/math9040429 ◽

2021 ◽

Vol 9 (4) ◽

pp. 429

Author(s):

Pedro Zamora ◽

Alejandro Arceo ◽

Noé Martínez ◽

Gerardo Romero ◽

Luis E. Garza

Keyword(s):

Time Delay ◽

Robust Stabilization ◽

Parametric Uncertainty ◽

Exclusion Principle ◽

Control Synthesis ◽

Classical Orthogonal Polynomials ◽

Loop Control ◽

Value Set ◽

Interval Plants ◽

Uncertain Time

This paper considers the robust stabilization problem for interval plants with parametric uncertainty and uncertain time-delay based on the value set characterization of closed-loop control systems and the zero exclusion principle. Using Kharitonov’s polynomials, it is possible to establish a sufficient condition to guarantee the robust stability property. This condition allows us to solve the control synthesis problem using conditions similar to those established in the loopshaping technique and to parameterize the controllers using stable polynomials constructed from classical orthogonal polynomials.

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Control Synthesis as Machine Learning Control by Symbolic Regression Methods

Applied Sciences ◽

10.3390/app11125468 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5468

Author(s):

Elizaveta Shmalko ◽

Askhat Diveev

Keyword(s):

Machine Learning ◽

Genetic Programming ◽

Mathematical Formulation ◽

Small Variation ◽

Learning Control ◽

Symbolic Regression ◽

Control Technique ◽

Control Synthesis ◽

Basic Solution ◽

Regression Methods

The problem of control synthesis is considered as machine learning control. The paper proposes a mathematical formulation of machine learning control, discusses approaches of supervised and unsupervised learning by symbolic regression methods. The principle of small variation of the basic solution is presented to set up the neighbourhood of the search and to increase search efficiency of symbolic regression methods. Different symbolic regression methods such as genetic programming, network operator, Cartesian and binary genetic programming are presented in details. It is shown on the computational example the possibilities of symbolic regression methods as unsupervised machine learning control technique to the solution of MLC problem of control synthesis for obtaining the stabilization system for a mobile robot.

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