Distance Estimation on Ultrasonic Sensor Using Kalman Filter

This research discusses about the distance estimation on ultrasonic sensor using Kalman Filter method. Accuracy level problem on ultrasonic sensor will be increased using Kalman Filter. Kalman Filter consists of two parts which are prediction and update. This research applies Kalman Filter method using Arduino Uno and Ultrasonic sensor HC-SR04. The test result compares the sensor data before and after Kalman Filter is applied. The test result of sensor value after given Kalman Filter depends on the value of noise sensor covariance matrix (R) and process noise covariance (Q). The best value of R and Q is 100 and 0.01. If the distance value between R and Q is too small, the filtering result will be invisible. In contrast, if the distance value between R and Q is too far, the filtering result could remove the original measured sensor data. In conclusion, applying Kalman Filter method in Ultrasonic sensor could estimate and increase the accuracy of sensor value up to 7%

Artificial Potential Field Algorithm for Obstacle Avoidance in UAV Quadrotor for Dynamic Environment

Artificial potential field (APF) is the effective real-time guide, navigation, and obstacle avoidance for UAV Quadrotor. The main problem in APF is local minima in an obstacle or multiple obstacles. In this paper, some modifications and improvements of APF will be introduced to solve one-obstacle local minima, two-obstacle local minima, Goal Not Reachable Near Obstacle (GNRON), and dynamic obstacle. The result shows that the improved APF gave the best result because it made the system reach the goal position in all of the examinations. Meanwhile, the APF with virtual force has the fastest time to reach the goal; however, it still has a problem in GNRON. It can be concluded that the APF needs to be modified in its algorithm to pass all of the local minima problems

Multibody Modeling and Balance Control of a Reaction Wheel Inverted Pendulum Using LQR Controller

In this study, modeling and LQR control of a reaction wheel inverted pendulum system is described. The reaction wheel inverted pendulum model is created by using a 3D CAD platform and exported to Simscape Multibody. The multibody model is linearized to derive a state-space representation. A LQR (Linear-quadratic regulator) controller is designed and applied for balance control of the pendulum. The results show that deriving a state-space representation from multibody is an easy and effective way to model dynamic systems and balance control of the reaction wheel inverted pendulum is successfully achieved by LQR controller. Results are given in the form of graphics

Particle Swarm Optimization (PSO) Tuning of PID Control on DC Motor

The use of DC motors is now common because of its advantages and has become an important necessity in helping human activities. Generally, motor control is designed with PID control. The main problem that is often discussed in PID is parameter tuning, namely determining the value of the Kp, Ki, and Kd parameters in order to obtain optimal system performance. In this study, one method for tuning PID parameters on a DC motor will be used, namely the Particle Swarm Optimization (PSO) method. Parameter optimization using the PSO method has stable results compared to other methods. The results of tuning the PID controller parameters using the PSO method on the MATLAB Simulink obtained optimal results where the value of Kp = 8.9099, K = 2.1469, and Kd = 0.31952 with the value of rise time of 0.0740, settling time of 0.1361 and overshoot of 0. Then the results of hardware testing by entering the PID value in the Arduino IDE software produce a stable motor speed response where Kp = 1.4551, Ki= 1.3079, and Kd = 0.80271 with a rise time value of 4.3296, settling time of 7.3333 and overshoot of 1