Genetic Algorithms and Genetic Programming for Control

Dracopoulos, Dimitris C.

doi:10.1007/978-3-662-03423-1_19

Cited by 16 publications

(11 citation statements)

References 40 publications

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“…EAs are employed in a broad range of engineering disciplines [23] such as architecture [24,25], electrical [26,27], chemical [28,29] and mechanical engineering [30,31]. EAs are also employed in other important fields as computer science [32], control [33] and signal processing [34]. Moreover, EAs have been previously used to extract the parameters of semiconductor devices, such as MOSFETs [27,[35][36][37][38], photovoltaic (PV) modules [39,40], or solar cells [31,39,41].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary parameter extraction for an organic TFT compact model including contact effects

Romero

González

Picos

et al. 2018

Organic Electronics

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In this work, an evolutionary parameter extraction procedure is combined with a compact model for the current-voltage characteristics of organic thin-film transistors as a part of a circuit design tool to extract the device parameters. This procedure can be used to overcome shortcomings of previous parameter extraction procedures. The proposed evolutionary procedure can be used in those situations whereby the parameter set extracted by other procedures does not comply its physical meaning, or if a poor agreement between the experimental data and the analytical results exists. In the last case, the evolutionary procedure can be used as a problem optimization method. After the evolutionary parameter extraction procedure is applied to the transistor output characteristics, the obtained results show an excellent agreement with the experimental data.

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Section: Introductionmentioning

confidence: 99%

Evolutionary parameter extraction for an organic TFT compact model including contact effects

Romero

González

Picos

et al. 2018

Organic Electronics

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“…For example, an artificial neural network [3][4][5], which is often used for solving of the machine learning problems, has a set structure and large number of unknown parameters. In contrast, symbolic regression methods [6][7][8] allow you to search for both function structure and parameters.…”

Section: Problem Statement Of Machine Learning Definitionmentioning

confidence: 99%

Machine Learning Control Based on Approximation of Optimal Trajectories

2021

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The paper is devoted to an emerging trend in control—a machine learning control. Despite the popularity of the idea of machine learning, there are various interpretations of this concept, and there is an urgent need for its strict mathematical formalization. An attempt to formalize the concept of machine learning is presented in this paper. The concepts of an unknown function, work area, training set are introduced, and a mathematical formulation of the machine learning problem is presented. Based on the presented formulation, the concept of machine learning control is considered. One of the problems of machine learning control is the general synthesis of control. It implies finding a control function that depends on the state of the object, which ensures the achievement of the control goal with the optimal value of the quality criterion from any initial state of some admissible region. Supervised and unsupervised approaches to solving a problem based on symbolic regression methods are considered. As a computational example, a problem of general synthesis of optimal control for a spacecraft landing on the surface of the Moon is considered as supervised machine learning control with a training set.

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“…The crossover fraction is set to be 0.8, while the elite count reproduction is 2. To reduce the possibility of reaching local minima, instead of global one, mutation rate is increased and the optimization process is repeated using the optimal solution as the initial one [26].…”

Section: Genetic Secondary Pid Controllermentioning

confidence: 99%

Secondary and Tertiary Voltage Control of a Multi-Region Power System

2020

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This paper presents techniques for the application of tertiary and secondary voltage control through the use of intelligent proportional integral derivative (PID) controllers and the wide area measurement system (WAMS) in the IEEE 39 bus system (New England system). The paper includes power system partitioning, pilot bus selection, phasor measurement unit (PMU) placement, and optimal secondary voltage control parameter calculations to enable the application of the proposed voltage control. The power system simulation and analyses were performed using the DIgSILENT and MATLAB software applications. The optimal PMU placement was performed in order to apply secondary voltage control. The tertiary voltage control was performed through an optimal power flow optimization process in order to minimize the active power losses. Two different methods were used to design the PID secondary voltage control, namely, genetic algorithm (GA) and neural network based on genetic algorithm (NNGA). A comparison of system performances using these two methods under different operating conditions is presented. The results show that NNGA secondary PID controllers are more robust than GA ones. The paper also presents a comparison between system performance with and without secondary voltage control, in terms of voltage deviation index and total active power losses. The graph theory is used in system partitioning, and sensitivity analysis is used in pilot bus selection, the results of which proved their effectiveness.

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Genetic Algorithms and Genetic Programming for Control

Cited by 16 publications

References 40 publications

Evolutionary parameter extraction for an organic TFT compact model including contact effects

Evolutionary parameter extraction for an organic TFT compact model including contact effects

Machine Learning Control Based on Approximation of Optimal Trajectories

Secondary and Tertiary Voltage Control of a Multi-Region Power System

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