To avoid battery failure and keep the battery lifetime, a system needs control its use by considering two of several parameters of Battery Management System (BMS) such as State of Charge (SOH) and State of Health (SOH). The State of Charge in Battery Management System provides the percentage of battery capacity, while the State Of Health measures the battery health. The Thevenin battery model is used to describe polarization characteristic and dynamic behavior of the battery and estimared using KalmanFilter(KF). Parameters in the model were estimated using Recursive Least Square. As the results, KF is better then RLS to estimate SOH with a mean relative error as much as 5.26% while RLS has 7.08%.

In this paper, the potential field principle is applied to several UAVs (Unmanned Aerial Vehicles) for optimal path planning. Each UAV has its own goals and it is used the attractive potential field to reach the goals. On the contrary, each UAV is considered as obstacle for other UAVs that must be avoided. In this research, there are two types of obstacles, i.e the static and dynamic. The repulsive potential field principle is used to avoid for both static and dynamic obstacles. The whole method is implemented into Parrot AR Drone 2.0 Quadcopter model of UAV and simulated in Gazebo Simulator by Robot Operating System (ROS). The results of this research are to control the thrust of quadcopter so that it is in flying position, the value of value of roll (α) or pitch (β) must set to not equal or approaching 90° or -90° and not between -180° and 180° or between -90° and 90°. Based on the dynamic obstacle performance test with parameter tuning, the optimal avoidance is when the η value is 7.8 while when in static and dynamic test with parameter tuning, the optimal avoidance is when the η value is 7.9 noted by the fastest time and the shortest path.

In this research, three battery models are proposed, i.e., simple battery model, Thevenin battery model, and modified Thevenin battery model. State-of-Charge (SOC) estimation based on those battery models are conducted with coulomb counting, open circuit voltage, and Kalman Filter. Simulation about false on battery capacity initialization is tested over the battery. The results show that the proposed battery modeling can accurately provide SOC estimation. On the simulation of false initialization, SOC estimation method with coulomb counting, open circuit voltage model 1 and 2 methods cannot track the true value, while Kalman Filter method can accurately provide SOC estimation and is able to correct false SOC initialization in short time.

This paper presents the gain scheduling based linearization of simplified quadrotor non-linear model as well as the test for the system controllability and observability for various equilibrium point. The simplified non-linear model of quadrotor is linearized at some equilibrium points to get the linear state equation. Then, the controllability and observability test is done to the linearized model for various equilibrium points. The system behavior is then tested by using the state feedback controller to confirm the result.

Potential-function-based control strategy for path planning and formation control of Quadrotors is proposed in this work. The potential function is used to attract the Quadrotor to the goal location as well as avoiding the obstacle. The algorithm to solve the so called local minima problem by utilizing the wall-following behavior is also explained. The resulted path planning via potential function strategy is then used to design formation control algorithm. Using the hybrid virtual leader and behavioral approach schema, the formation control strategy by means of potential function is proposed. The overall strategy has been successfully applied to the Quadrotor's model of Parrot AR Drone 2.0 in Gazebo simulator programmed using Robot Operating System.

The main disadvantage of an Inertial Navigation System is a low accuracy due to noise, bias, and drift error in the inertial sensor. This research aims to develop the accelerometer and gyroscope sensor for quadrotor navigation system, bias compensation, and Zero Velocity Compensation (ZVC). Kalman Filter is designed to reduce the noise on the sensor while bias compensation and ZVC are designed to eliminate the bias and drift error in the sensor data. Test results showed the Kalman Filter design is acceptable to reduce the noise in the sensor data. Moreover, the bias compensation and ZVC can reduce the drift error due to integration process as well as improve the position estimation accuracy of the quadrotor. At the time of testing, the system provided the accuracy above 90 % when it tested indoor.