2,429 research outputs found

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

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    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

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

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    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

    Design of extended Kalman filtering neural network control system based on particle swarm identification of nonlinear U-model

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    This paper studies the modelling of a class of nonlinear plants with known structures but unknown parameters and proposes a general nonlinear U-model expression. The particle swarm optimization algorithm is used to identify the time-varying parameters of the nonlinear U-model online, which solves the identification problem of the nonlinear U-model system. Newton iterative algorithm is used for nonlinear model transformation. Extended Kalman filter (EKF) is used as the learning algorithm of radial basis function (RBF) neural network to solve the interference problem in a nonlinear system. After determining the number of network nodes in the neural network, EKF can simultaneously determine the network threshold and weight matrix, use the online learning ability of the neural network, adjust the network parameters, make the system output track the ideal output, and improve the convergence speed and anti-noise capability of the system. Finally, simulation examples are used to verify the identification effect of the particle swarm identification algorithm based on the U-model and the effectiveness of the extended Kalman filtering neural network control system based on particle swarm identification

    PERFORMANCE ANALYSIS OF PSO-PD CONTROLLER IN CONTROLING THE RIGID GANTRY CRANE SYSTEM

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    Karya tulis ini membahas tentang algoritma particle swarm optimization (PSO) untuk mengoptimalkan penguat pengendali PD yang dinamakan pengendali PSO-PD. Efektivitas algoritma pengendali yang diusulkan diuji dengan menggunakan fungsi step dan dibandingkan dengan pengendali PD berbasis Zigler-Nichols (ZN-PD). Hasil simulasi yang didapatkan menunjukkan bahwa pengendali PSO-PD menghasilkan waktu naik dan waktu puncak yang lebih lambat dibandingkan dengan pengendali ZN-PD, tetapi memiliki waktu tunak yang lebih cepat dan nilai overshoot yang kecil di bawah trayektori yang didefinisikan.Kata kunci: Sistem gantry crane, PSO, Gain PD, Sudut ayunan AbstractThis paper presents the particle swarm optimization (PSO) algorithm to optimize the gains of the PD controller to form what so-called the particle swarm optimization (PSO-PD) controller. The effectiveness of the proposed control algorithm is tested under constant step function and compared with Ziegler-Nichols (ZN-PD) controller. Simulation results show that proposed controller has slower rise time and peak time than ZN-PD controller as well as small overshoot under the predefined trajectories

    African vulture optimizer algorithm based vector control induction motor drive system

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    This study describes a new optimization approach for three-phase induction motor speed drive to minimize the integral square error for speed controller and improve the dynamic speed performance. The new proposed algorithm, African vulture optimizer algorithm (AVOA) optimizes internal controller parameters of a fuzzy like proportional differential (PD) speed controller. The AVOA is notable for its ease of implementation, minimal number of design parameters, high convergence speed, and low computing burden. This study compares fuzzy-like PD speed controllers optimized with AVOA to adaptive fuzzy logic speed regulators, fuzzy-like PD optimized with genetic algorithm (GA), and proportional integral (PI) speed regulators optimized with AVOA to provide speed control for an induction motor drive system. The drive system is simulated using MATLAB/Simulink and laboratory prototype is implemented using DSP-DS1104 board. The results demonstrate that the suggested fuzzy-like PD speed controller optimized with AVOA, with a speed steady state error performance of 0.5% compared to the adaptive fuzzy logic speed regulator’s 0.7%, is the optimum alternative for speed controller. The results clarify the effectiveness of the controllers based on fuzzy like PD speed controller optimized with AVOA for each performance index as it provides lower overshoot, lowers rising time, and high dynamic response

    Effects of Multiple Combination Weightage using MOPSO for Motion Control Gantry Crane System

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    This paper presents the implementation of Multi Objective Particle Swarm Optimization in controlling motion control of Gantry Crane System. Three objective functions are considered to be optimized, named (i) steady state error, (ii) overshoot, and (iii) settling time. Six cases with different setting of weight summation are analyzed in order to obtain five parameters (PID and PD) controller. A combination of PID and PD controller is observed and utilized for controlling trolley movement to desired position and reduced the payload oscillation concurrently. Various cases of weight summation values will affect to the controller parameters and system responses. The performances of the system is conducted and presented within Matlab environment
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