In this paper, the efficiency of photovoltaic panels is improved by adding a sun tracking system. The solar tracking system is used for tracking the sun so that photovoltaic always faces the sun. This system uses a dual axis consisting of horizontal rotation axis and a vertical rotation axis. The horizontal rotational axis motion is to follow the azimuth angle of the sun from north to south. Then, to follow the sun's azimuth angle from east to west is the vertical axis motion. Both types of movements are controlled using a PID controller that is optimized with an artificial intelligence approach, namely particle swarm optimization (PID-PSO), firefly algorithm (PID-FA), imperialist competitive algorithm (PID-ICA), bat algorithm (PID-BA), and ant colony optimization (PID-ACO). Experiments of various approaches were carried out and the corresponding performance compared. The experimental results show that PID-BA performs best in terms of settling time and overshoot. The results also allow the comparison of different PID controller and the calculation of the fastest completion time.

The sun emits light which is solar radiation energy. This solar radiation energy can be utilized for humans, including by changing the energy of solar radiation into electrical energy. Radiation energy is changed with the help of solar or photovoltaic panels. To increase the efficiency of electricity production in solar panels, by adding Dual Axis Tracking controls. The Dual Axis Tracking Photovoltaic system is expected that solar panels will always face the sun. Because of the deviation of the direction of the solar panel to the direction of the sun will reduce the performance of generating electricity. In this study comparing 4 dual-axis tracking design methods, namely without a controller, with PID-PSO controllers that have been studied, with PDI-FA controller, and with ANFIS-FA controller. The simulation results show that ANFIS-FA is the best controller of the four methods used. This research will be a reference to compare with other methods to obtain the best method in this study.

Water flow measurements have been needed by controllers in industrial processes. The quantity of water must be determined to control the volume of water used in the storage tank. Water flow performance control models based on tanks are required using a Proportional-Integral-Derivative (PID) control system. This system uses a flow sensor to detect the speed of an actuator. Actuators stabilize the output water speed per minute at a certain point. Manually determining the value of a PID constant will be very difficult and not optimal. Then we need an automatic and accurate control method. This study focuses on four comparisons of designed methods related to water level without control, standard PID method, Fuzzy Logic method, Fuzzy-PID method, and Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The simulation results found that the four control models have different performance. The PID-ANFIS model obtained the smallest overshot value in the PID-ANFIS model of 0.5135 pu, the smallest undershot in PID-ANFIS was 0.5291 pu. Current Output Output obtains the smallest overshot value in the PID-ANFIS model of 0.0023 pu, the smallest undershot in the PID-ANFIS model is 0.0014 pu.

This paper presents a comparison of optimization for vehicle steering controls system simulation using several Artificial Intelligence (AI) for optimizing Proportional Integral Derivative (PID) control parameters to suppress errors on lateral motion and the yaw motion of vehicles. This paper compares five kinds of tuning methods of parameter for PID controller, among other are Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bat Algorithm (BA) and Imperialist Competitive Algorithm (ICA). The vehicles are represented in the model vehicle with 10 degrees of Freedom of vehicle dynamics system. The simulation results show that the PID control tuned by AI in the vehicle steering control system can adjust the plant output to the desired trajectory so that the stability of the vehicle is maintained. Vehicle yaw error and lateral error can be reduced by using ICA to determine PID parameter. The main advantage of proposed optimization is faster and more accurate compared with PID controller. So the error of the controller is reduced too. The results obtained are of vehicle motion can be maintained in accordance with the desired trajectory with smaller error and was able to achieve higher speeds than with the control system using optimized without parameters. This paper only deals with software simulation to proof the effect of AI optimization. The hardware implementation will be investigated in the next future.

This paper presents the simulation of vehicle steering control system using the Imperialist Competitive Algorithm (ICA) for optimizing Proportional Integral Derivative (PID) control parameters to suppress errors on lateral motion and the yaw motion of vehicles. The vehicles are represented in the model vehicle with 10 degrees of Freedom of vehicle dynamics system. The simulation results show that the PID control tuned by the ICA in the vehicle steering control system can adjust the plant output to the desired trajectory so that the stability of the vehicle is maintained. Vehicle yaw error and lateral error can be reduced by using ICA to determine PID parameter. The main advantage of proposed optimization is faster and more accurate compared with standard PID controller. And then the error of the controller is reduced too. The results obtained are of vehicle motion can be maintained in accordance with the desired trajectory with smaller error and was able to achieve higher speeds than with the control system using optimized without parameters. This paper only deals with software simulation to proof the effect of ICA-PID optimization. The hardware implementation will be investigated in the next future.

Adaptive Neuro-Fuzzy Inference System (ANFIS) is an Artificial Intelligence (AI) called Artificial Neural Network (ANN) based on Takagi-Sugeno Fuzzy Inference System (FIS). ANFIS integrates neural networks and Fuzzy Logic principles, has the ability to take advantage of both within a single framework. ANFIS can be used to control and optimize the system automatically. Optimization of steer movement is necessary for steer movement with the vehicle. Faulty movement leads the car will generate errors position of the car on the vehicle line. Several studies have been developed in fully automated steer by cabling systems, including those devoted to input paths, using GPS technology and trajectories. The steering system in this study used PID and ANFIS controllers, which were tuned with some Artificial Intelligence (AI). AI is expected to assist in accelerating and optimizing the control process. In this research, we will develop a completely “Automatic Steer by Wire System” model using 10 Degree Of Freedom (DOF) consisting of 3-DOF Vehicle Handling Model and 7-DOF Vehicle Ride Model. The method already used is the PID that is tuned using AI. This research combines ANFIS method with Firefly Algorithm (FA), Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA). Then compare the result with the FA-PID, PSO-PID, ICA-PID, FA-PID-ANFIS, PSO-PID-ANFIS, and ICA-PID-ANFIS. Furthermore, this method is used to design a real electric car. At a standard speed of 13.8 km/h, the ICA-PID-ANFIS method has the smallest error of 0.005070 m. If the speed of the vehicle is changed is able to maintain stay on track only up to speed 62.1 km/h. But the ICA-PID method can keep it on track to speeds of 69.0 km/h. The results of this simulation is a simulation of the ideal conditions of the vehicle. This research is used to determine the best method that can adjust the actual trajectory. So it can be used as reference for real vehicles.