The electric motor is one of the appliances that consume considerable energy. Therefore, the control method which can reduce energy consumption with better performance is needed. The purpose of this research is to minimize the energy consumption of the DC motor with maintaining the performance using Hybrid Fuzzy-PID. The input of the Fuzzy system is the error and power of the system. Where error is correlated with matric Q and power is correlated with matric R. Therefore, adjusting the fuzzy rule on error and power is like adjust matrices Q and R in LQR method. The proposed algorithm can reduce energy consumption. However, system response is slightly decrease shown from ISE (Integral Square Error). The energy reduction average is up to 5.58% while the average of ISE increment is up to 1.89%. The more speed variation in the system, the more energy can be saved by the proposed algorithm. While in terms of settling time, the proposed algorithm has the longest time due to higher computation time in the fuzzy system. This performance can be increased by tuning fuzzy rules. This algorithm offers a solution for a complex system which difficult to be modeled.

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The electric motor is an electromagnetic device that converts electrical energy into mechanical energy. There are many industrial sectors using electrical motors. Almost 90% of industries still use PID control because of its simplicity, applicability, and reliability. However, the weakness of PID is that it takes a long time to tune. PSO is one of the optimization methods which can be used to tune PID. The objective function is needed when using PSO to tune PID control. Five different objective functions which are ISE, IAE, ITSE, ITAE, and MSE is compared in terms of performance and control energy. The PID controller is applied in DC motor speed control in a simulation environment. Three testing condition is carried out which is step responses, set-point changes, and disturbance rejection. The simulation result shows that in terms of performance, ITSE is the best one. On the other hand, in terms of control energy, ISE is using the lowest energy to control the plant.

In the digitalization and automation era, the internet has become an inseparable part of human life It provides a place for devices that are connected and can be controlled wirelessly through a network infrastructure, which is called the internet of things (IoT). In this research, the dual mode system of smart home based on IoT is proposed. In this system, the smart home can be controlled both manually and automatically. The key component for the proposed system is the relay mode which can be controlled to select the mode. The hardware implementation was done to test the proposed system with good result. Blynk app is used to control in automatic mode with virtual switch. When the manual mode is selected, the automatic mode is turned off and vice versa.

The main problem of using a Proportional Integral (PI) Controller in Brushless Direct Current (BLDC) motor speed control is tuning the PI’s parameter and its performance cannot adapt to the system behavior changes. Particle Swarm Optimization (PSO) has been chosen to optimize the tuning. Fuzzy Logic Controller (FLC) is used to online tuning PI’s parameters to adapt to system conditions. Optimal adaptive PI, which combines the PSO method and FLC method to tune PI, is proposed. It was successfully implemented in the simulation environment. The test was carried out in three conditions: step responses, set-point changes, and disturbance rejection. The proposed algorithm is superior with no overshoot/undershoot. Whereas in terms of settling time is in between PI and PI-PSO. PI controller has the smallest control effort. However, the other parameter is the worst. PI-PSO is superior in terms of settling time and Integral of Absolute Error (IAE) except for the step response test. The proposed method has lower IAE and higher control effort by 78.73 % and 60 % compared to PI control. On the other hand, it has a higher IAE dan lower control effort by 11.82 % and 33.88 % compared to PI-PSO. Therefore, the optimal adaptive PI control can reduce energy consumption compared to optimal PI with better performance than PI control.