Object recognition system using computer vision that is implemented on Remote Controlled Weapon Station (RCWS) is discussed. This system will make it easier to identify and shoot targeted object automatically. Algorithm was created to recognize real time multiple objects using two methods i.e. Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) combined with K-Nearest Neighbors (KNN) and Random Sample Consensus (RANSAC) for verification. The algorithm is designed to improve object detection to be more robust and to minimize the processing time required. Objects are registered on the system consisting of the armored personnel carrier, tanks, bus, sedan, big foot, and police jeep. In addition, object selection can use mouse to shoot another object that has not been registered on the system. Kinect™ is used to capture RGB images and to find the coordinates x, y, and z of the object. The programming language used is C with visual studio IDE 2010 and opencv libraries. Object recognition program is divided into three parts: 1) reading image from kinect™ and simulation results, 2) object recognition process, and 3) transfer of the object data to the ballistic computer. Communication between programs is performed using shared memory. The detected object data is sent to the ballistic computer via Local Area Network (LAN) using winsock for ballistic calculation, and then the motor control system moves the direction of the weapon model to the desired object. The experimental results show that the SIFT method is more suitable because more accurate and faster than SURF with the average processing time to detect one object is 430.2 ms, two object is 618.4 ms, three objects is 682.4 ms, and four objects is 756.2 ms. Object recognition program is able to recognize multi-objects and the data of the identified object can be processed by the ballistic computer in realtime.

Most methods of maximum power point tracking (MPPT) for photovoltaic (PV) focus only on tracking performance while robustness against disturbances has rarely been addressed. This paper proposes a new MPPT control method that provides robustness against direct current (DC) link voltage disturbance as well as good tracking performance. The method uses indirect MPPT control topology which incorporates two controllers. For the external controller, we use an adaptive proportional-integral (PI) control which is real-time tuned by fuzzy logic (FL). New membership functions and rule base are proposed using only one fuzzy input variable and 10 fuzzy rules. The internal controller is a PI controller. The PV panel is connected to a boost DC-DC converter. The proposed MPPT control is compared with the fuzzy logic controller (FLC). Performance is evaluated under DC link voltage disturbance, steady-state condition, and rapid solar radiation changes. Simulation results indicate that the proposed method provides 41.2 % better robustness against DC link voltage disturbance as compared to the direct FLC. Experimental results under natural climate conditions with real solar radiation validate that the proposed method works well in regulating the MPP at steady-state solar irradiance as well as in tracking the MPP towards rapid solar irradiance changes. It yields the PV power tracking speed of 95.75 W/s.

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Driver mechanism with two degree of freedom (MP 2-DK) is a robotic device that can be used for various applications such as turret drive system, gutling gun, launcher, radar antennas, and communications satellite antennas. The precision and the speed of a MP 2-DK are determined by its control system. The calculation inverse angle due to interference in six degree of freedom is necessary to control a MP 2 DK. This paper analyses three calculation methods of inverse angle which are iteration method using Jacobian matrix, reduction of matrix equations using positioning geometry, and an analytical derivation using a rotation matrix. The simulation results of the three methods showed that the first and the third methods could visually demonstrate three rotational disturbances, whereas the second method could only demonstrate the pitch and yaw (PY) disturbances. The third method required less processing time than the first and the second methods. The best method based on this research was the method of rotation matrix.