Design of Intelligent PID Controller for AVR System Using an Adaptive Neuro Fuzzy Inference System

This paper presents a hybrid approach involving signal to noise ratio (SNR) and particle swarm optimization (PSO) for design the optimal and intelligent proportional-integral-derivative (PID) controller of an automatic voltage regulator (AVR) system with uses an adaptive neuro fuzzy inference system (ANFIS). In this paper determined optimal parameters of PID controller with SNR-PSO approach for some events and use these optimal parameters of PID controller for design the intelligent PID controller for AVR system with ANFIS.  Trial and error method can be used to find a suitable design of anfis based an intelligent controller. However, there are many options including fuzzy rules, Membership Functions (MFs) and scaling factors to achieve a desired performance. An optimization algorithm facilitates this process and finds an optimal design to provide a desired performance. This paper presents a novel application of the SNRPSO approach to design an intelligent controller for AVR. SNR-PSO is a method that combines the features of PSO and SNR in order to improve the optimize operation. In order to emphasize the advantages of the proposed SNR-PSO PID controller, we also compared with the CRPSO PID controller. The proposed method was indeed more efficient and robust in improving the step response of an AVR system and numerical simulations are provided to verify the effectiveness and feasibility of PID controller of AVR based on SNRPSO algorithm.DOI:http://dx.doi.org/10.11591/ijece.v4i5.652

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes

Optimal frequency control in microgrid system using fractional order PID controller using Krill Herd algorithm

This paper investigates the use of fractional order Proportional, Integral and Derivative (FOPID) controllers for the frequency and power regulation in a microgrid power system. The proposed microgrid system composes of renewable energy resources such as solar and wind generators, diesel engine generators as a secondary source to support the principle generators, and along with different energy storage devices like fuel cell, battery and flywheel. Due to the intermittent nature of integrated renewable energy like wind turbine and photovoltaic generators, which depend on the weather conditions and climate change this affects the microgrid stability by considered fluctuation in frequency and power deviations which can be improved using the selected controller. The fractional-order controller has five parameters in comparison with the classical PID controller, and that makes it more flexible and robust against the microgrid perturbation. The Fractional Order PID controller parameters are optimized using a new optimization technique called Krill Herd which selected as a suitable optimization method in comparison with other techniques like Particle Swarm Optimization. The results show better performance of this system using the fractional order PID controller-based Krill Herd algorithm by eliminates the fluctuations in frequency and power deviation in comparison with the classical PID controller. The obtained results are compared with the fractional order PID controller optimized using Particle Swarm Optimization. The proposed system is simulated under nominal conditions and using the disconnecting of storage devices like battery and Flywheel system in order to test the robustness of the proposed methods and the obtained results are compared.У статті досліджено використання регуляторів пропорційного, інтегрального та похідного дробового порядку (FOPID) для регулювання частоти та потужності в електромережі. Запропонована мікромережева система складається з поновлюваних джерел енергії, таких як сонячні та вітрогенератори, дизельних генераторів як вторинного джерела для підтримки основних генераторів, а також з різних пристроїв для накопичування енергії, таких як паливна батарея, акумулятор і маховик. Через переривчасту природу інтегрованої відновлювальної енергії, наприклад, вітрогенераторів та фотоелектричних генераторів, які залежать від погодних умов та зміни клімату, це впливає на стабільність мікромережі, враховуючи коливання частоти та відхилення потужності, які можна поліпшити за допомогою вибраного контролера. Контролер дробового порядку має п’ять параметрів порівняно з класичним PID-контролером, що робить його більш гнучким та надійним щодо збурень мікромережі. Параметри PID-контролера дробового порядку оптимізовані за допомогою нової методики оптимізації під назвою «зграя криля», яка обрана як підходящий метод оптимізації порівняно з іншими методами, такими як оптимізація методом рою частинок. Результати показують кращі показники роботи цієї системи за допомогою алгоритму «зграя криля», заснованого на PID-контролері дробового порядку, виключаючи коливання частоти та відхилення потужності порівняно з класичним PID-контролером. Отримані результати порівнюються з PID-контролером дробового порядку, оптимізованим за допомогою оптимізації методом рою частинок. Запропонована система моделюється в номінальному режимі роботи та використовує відключення накопичувальних пристроїв, таких як акумулятор та маховик, щоб перевірити надійність запропонованих методів та порівняти отримані результати

DIFFERENTIAL EVOLUTION FOR OPTIMIZATION OF PID GAIN IN ELECTRICAL DISCHARGE MACHINING CONTROL SYSTEM

ABSTRACT PID controller of servo control system maintains the gap between Electrode and workpiece in Electrical Dis- charge Machining (EDM). Capability of the controller is significant since machining process is a stochastic phenomenon and physical behaviour of the discharge is unpredictable. Therefore, a Proportional Integral Derivative (PID) controller using Differential Evolution (DE) algorithm is designed and applied to an EDM servo actuator system in order to find suitable gain parameters. Simulation results verify the capabilities and effectiveness of the DE algorithm to search the best configuration of PID gain to maintain the electrode position. Keywords: servo control system; electrical discharge machining; proportional integral derivative; con- troller tuning; differential evolution

A novel intelligent adaptive control of laser-based ground thermal test

AbstractLaser heating technology is a type of potential and attractive space heat flux simulation technology, which is characterized by high heating rate, controlled spatial intensity distribution and rapid response. However, the controlled plant is nonlinear, time-varying and uncertainty when implementing the laser-based heat flux simulation. In this paper, a novel intelligent adaptive controller based on proportion–integration–differentiation (PID) type fuzzy logic is proposed to improve the performance of laser-based ground thermal test. The temperature range of thermal cycles is more than 200K in many instances. In order to improve the adaptability of controller, output scaling factors are real time adjusted while the thermal test is underway. The initial values of scaling factors are optimized using a stochastic hybrid particle swarm optimization (H-PSO) algorithm. A validating system has been established in the laboratory. The performance of the proposed controller is evaluated through extensive experiments under different operating conditions (reference and load disturbance). The results show that the proposed adaptive controller performs remarkably better compared to the conventional PID (PID) controller and the conventional PID type fuzzy (F-PID) controller considering performance indicators of overshoot, settling time and steady state error for laser-based ground thermal test. It is a reliable tool for effective temperature control of laser-based ground thermal test

Nonlinear system identification and control using state transition algorithm

By transforming identification and control for nonlinear system into optimization problems, a novel optimization method named state transition algorithm (STA) is introduced to solve the problems. In the proposed STA, a solution to a optimization problem is considered as a state, and the updating of a solution equates to a state transition, which makes it easy to understand and convenient to implement. First, the STA is applied to identify the optimal parameters of the estimated system with previously known structure. With the accurate estimated model, an off-line PID controller is then designed optimally by using the STA as well. Experimental results have demonstrated the validity of the methodology, and comparisons to STA with other optimization algorithms have testified that STA is a promising alternative method for system identification and control due to its stronger search ability, faster convergence rate and more stable performance.Comment: 20 pages, 18 figure

Chaotic multi-objective optimization based design of fractional order PI{\lambda}D{\mu} controller in AVR system

In this paper, a fractional order (FO) PI{\lambda}D\mu controller is designed to take care of various contradictory objective functions for an Automatic Voltage Regulator (AVR) system. An improved evolutionary Non-dominated Sorting Genetic Algorithm II (NSGA II), which is augmented with a chaotic map for greater effectiveness, is used for the multi-objective optimization problem. The Pareto fronts showing the trade-off between different design criteria are obtained for the PI{\lambda}D\mu and PID controller. A comparative analysis is done with respect to the standard PID controller to demonstrate the merits and demerits of the fractional order PI{\lambda}D\mu controller.Comment: 30 pages, 14 figure