In order to provide the comfortable areas for human, the comfortable rooms are the basic needs area, controlled temperature, and relative humidity (RH). The aim of this study is to control and maintain the temperature and RH of the comfortable room using a proportional integral derivative (PID) controller tuned by metaheuristic optimization. In tuning gains of the PID controller, the modern metaheuristic optimizations, ant colony optimization (ACO), and symbiotic organism search (SOS) are applied and the performance of the proposed control system is compared to that of the traditional methods. In the experimental testing, the controlled room size is tested in the area of width 7.80 m, length 8.00 m, and height 3.80 m. The simulation results show that the performance of the proposed control system-tuned gains of PID controllers by using SOS algorithm has the least steady-state error with 15% rise time and also the overshoot can reach the setpoint. In the case of disturbance occurring in the system, the proposed control system is able to approach the setpoint. Therefore, the PID controller tuned by SOS algorithm can regulate the temperature and humidity of the comfortable room, proficiently.

Moisture is one of the most important factors impacting the talc pellet process. In this study, a hybrid model (HM) based on the combination of intelligent algorithms, self-organizing map (SOM), the adaptive neuron fuzzy inference system (ANFIS) and metaheuristic optimizations, genetic algorithm (GA) and particle swarm optimization (PSO) is introduced, namely, HM-GA and HM-PSO. The main purpose is to predict the moisture in the talc pellet process related to symmetry in the aspect of real-world application problem. In the combination process, SOM classifies the suitable input data. The GA and PSO, as the training algorithms of ANFIS, are investigated to compare the prediction skill. Five factors, including talc powder, water, temperature, feed speed, and air flow of 52 experiment cases designed by central composite design (CCD), are the training set data. Three different measures evaluate the capacity of moisture prediction. The comparison results show that the HM-PSO can provide the smallest difference between train and test datasets under the condition of the moisture being less than 5%. As a result, the HM-PSO model achieves the best result in predicting the moisture for the talc pellet process with R = 0.9539, RMSE = 1.0693, and AAD = 0.393, compared to others.

The objective of this paper is to present the three phase induction motor drive using the cooperation of fuzzy logic controller and proportional plus integral (PI) controller as a hybrid run on field oriented control (FOC) for improving the performance of rotor speed. The system is fed to a three phase induction motor by voltage source inverter that is used space vector modulation (SVM) technique. This system is implemented with the control system on dSPACE programming which is supported by MATLAB/Simulink through a dSPACE-ds1140 interfacing module. In the implementation, the conventional PI controllers are replaced by hybrid fuzzy PI controllers of both an outer speed control loop and two inner currents control loops that are controlled stator flux and rotor torque of the induction motor. The experimental results are compared with conventional PI controllers. As a result, the performance of design model by hybrid fuzzy PI controller is better than the conventional PI controllers.

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