Advanced and Innovative Optimization Techniques in Controllers: A Comprehensive Review

New commercial power electronic controllers come to the market almost every day to help improve electronic circuit and system performance and efficiency. In DC–DC switching-mode converters, a simple and elegant hysteretic controller is used to regulate the basic buck, boost and buck–boost converters under slightly different configurations. In AC–DC converters, the input current shaping for power factor correction posts a constraint. But, several brilliant commercial controllers are demonstrated for boost and fly back converters to achieve almost perfect power factor correction. In this paper a comprehensive review of the various advanced optimization techniques used in power electronic controllers is presented

Generalized predictive control based on particle swarm optimization for linear/nonlinear process with constraints

Abstract: This paper presents an intelligent generalized predictive controller (GPC) based on particle swarm optimization (PSO) for linear or nonlinear process with constraints. We propose several constraints for the plants from the engineering point of view and the cost function is also simplified. No complicated mathematics is used which originated from the characteristics ofPSO. This method is easy to be used to control the plants with linear or/and nonlinear constraints. Numerical simulations are used to show the performance of this control technique for linear and nonlinear processes, respectively. In the first simulation, the control signal is computed based on an adaptive linear model. In the second simulation, the proposed method is based on a fixed neural network model for a nonlinear plant. Both of them show that the proposed control scheme can guarantee a good control performance

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

A review of optimization approaches for controlling water-cooled central cooling systems

Buildings consume a large amount of energy across all sectors of society, and a large proportion of building energy is used by HVAC systems to provide a comfortable and healthy indoor environment. In medium and large-size buildings, the central cooling system accounts for a major share of the energy consumption of the HVAC system. Improving the cooling system efficiency has gained much attention as the reduction of cooling system energy use can effectively contribute to environmental sustainability. The control and operation play an important role in central cooling system energy efficiency under dynamic working conditions. It has been proven that optimization of the control of the central cooling system can notably reduce the energy consumption of the system and mitigate greenhouse gas emissions. In recent years, numerous studies focus on this topic to improve the performance of optimal control in different aspects (e.g., energy efficiency, stability, robustness, and computation efficiency). This paper provides an up-to-date overview of the research and development of optimization approaches for controlling water-cooled central cooling systems, helping readers to understand the new significant trends and achievements in this area. The optimization approaches have been classified as system-model-based and data-based. In this paper, the optimization methodology is introduced first by summarizing the key decision variables, objective function, constraints, and optimization algorithms. The principle and performance of various optimization approaches are then summarized and compared according to their classification. Finally, the challenges and development trends for optimal control of water-cooled central cooling systems are discussed

Load Frequency Control (LFC) Strategies in Renewable Energy‐Based Hybrid Power Systems:A Review

The hybrid power system is a combination of renewable energy power plants and conventional energy power plants. This integration causes power quality issues including poor settling times and higher transient contents. The main issue of such interconnection is the frequency variations caused in the hybrid power system. Load Frequency Controller (LFC) design ensures the reliable and efficient operation of the power system. The main function of LFC is to maintain the system frequency within safe limits, hence keeping power at a specific range. An LFC should be supported with modern and intelligent control structures for providing the adequate power to the system. This paper presents a comprehensive review of several LFC structures in a diverse configuration of a power system. First of all, an overview of a renewable energy-based power system is provided with a need for the development of LFC. The basic operation was studied in single-area, multi-area and multi-stage power system configurations. Types of controllers developed on different techniques studied with an overview of different control techniques were utilized. The comparative analysis of various controllers and strategies was performed graphically. The future scope of work provided lists the potential areas for conducting further research. Finally, the paper concludes by emphasizing the need for better LFC design in complex power system environments

Radial Basis Function Neural Networks : A Review

Radial Basis Function neural networks (RBFNNs) represent an attractive alternative to other neural network models. One reason is that they form a unifying link between function approximation, regularization, noisy interpolation, classification and density estimation. It is also the case that training RBF neural networks is faster than training multi-layer perceptron networks. RBFNN learning is usually split into an unsupervised part, where center and widths of the Gaussian basis functions are set, and a linear supervised part for weight computation. This paper reviews various learning methods for determining centers, widths, and synaptic weights of RBFNN. In addition, we will point to some applications of RBFNN in various fields. In the end, we name software that can be used for implementing RBFNNs

Tilt-fractional order proportional integral derivative control for DC motor using particle swarm optimization

Introduction. Recently, the most desired goal in DC motor control is to achieve a good robustness and tracking dynamic of the set-point reference speed of the feedback control system. Problem. The used model should be as general as possible and consistently represent systems heterogeneous (which may contain electrical, mechanical, thermal, magnetic and so on). Goal. In this paper, the robust tilt-fractional order proportional integral derivative control is proposed. The objective is to optimize the controller parameters from solving the criterion integral time absolute error by particle swarm optimization. The control strategy is applied on DC motor to validate the efficiency of the proposed idea. Methods. The proposed control technique is applied on DC motor where its dynamic behavior is modeled by external disturbances and measurement noises. Novelty. The proposed control strategy, the synthesized robust tilt-fractional order proportional integral derivative speed controller is applied on the DC motor. Their performance and robustness are compared to those provided by a proportional integral derivative and fractional order proportional integral derivative controllers. Results. This comparison reveals superiority of the proposed robust tilt-fractional order proportional integral derivative speed controller over the remaining controllers in terms of robustness and tracking dynamic of the set-point reference speed with reduced control energy.Вступ. Останнім часом найбільш бажаною метою керування двигуном постійного струму є досягнення гарної надійності та динамічного відстеження заданої опорної швидкості системи керування зі зворотним зв’язком. Проблема. Використовувана модель має бути якомога загальнішою і несуперечливо представляти різнорідні системи (які можуть містити електричні, механічні, теплові, магнітні тощо). Мета. У цій статті пропонується робастне управління похідною пропорційного інтеграла дробового порядку нахилу. Мета полягає в тому, щоб оптимізувати параметри контролера шляхом вирішення критерію інтегральної абсолютної тимчасової помилки шляхом оптимізації рою частинок. Стратегія управління застосовується до двигуна постійного струму для перевірки ефективності запропонованої ідеї. Методи. Пропонований метод управління застосовується до двигуна постійного струму, динамічна поведінка якого моделюється зовнішніми перешкодами та шумами вимірів. Новизна. Пропонована стратегія управління, синтезований робастний пропорційно-інтегрально-диференціальний регулятор швидкості нахилу дробового порядку застосовується до двигуна постійного струму. Їх продуктивність та надійність порівнюються з показниками, що забезпечуються контролерами пропорційної інтегральної похідної та пропорційної інтегральної похідної дробового порядку. Результати. Це порівняння показує перевагу запропонованого робастного пропорційно-інтегрально-диференціального регулятора швидкості нахилу дробового порядку над іншими регуляторами з погляду робастності та динамічного відстеження заданої опорної швидкості зі зменшеною енергією управління