The launcher of a hard-kill type APS (Active Protection System) requires rapid and precise driving to aim at incoming threats after detection. High angular acceleration is necessary for rapid driving, which demands high energy consumption. However, the capacity of the capacitor bank and power supply unit is limited due to weight and space constraints. If energy becomes insufficient during continuous operation, the voltage of the capacitor bank can drop below the minimum operating voltage of the drive motor, leading to problems such as torque deficiency. Therefore, it is necessary to determine an allowable angular acceleration that satisfies precision within the available energy and generate a driving profile accordingly. This paper proposes a method for deriving an allowable angular acceleration by analyzing the allowable energy and validates it through simulation. We examined the allowable energy by verifying the charged voltage of the capacitor bank, formulated equations for energy at the point of maximum consumption, and derived an equation for allowable angular acceleration through numerical analysis. By applying the proposed algorithm in simulations, we confirmed that the voltage of the capacitor bank did not drop below the minimum operating voltage of the driving motor during three consecutive operations. Therefore, it is expected that the stability of the APS launcher can be improved by applying the proposed algorithm, and continuous operation with limited performance is anticipated to be possible.

For motor controller designers, building a simulation environment is not a difficult process. After verifying the controller by simulation, it is common to select 20kHz for the current control loop, 1kHz for the speed loop, and 100Hz for the position loop when implementing the actual HW embedded system. This is because maximized cycles (20kHz) for each control loop are unnecessary in control theory and are a waste of cost and HW resources. However, in a simulation environment, each loop will often have the same control cycle (20kHz maximum). This is because we think it is unnecessary to reflect this part in the simulation. In this paper, it is shown that the difference in the sampling time of each control loop makes a big difference in the simulation result, and as a solution, it is proposed to apply LPF to the position loop output stage. In the process, the reasons for the differences were analyzed, and the effect of LPF, the reason for application, and the feasibility of implementation were proved by actual software coding.

Controller modeling is essential for the design. It allows various control techniques to be simulated in advance, and various interpretations can be performed. If this is not the case, we need to reverse engineering in the real system developed by others. In this paper, controller modeling was reversely designed using the frequency test results of the target system. First, the characteristic equation of the target equipment was based on and a block diagram was assumed. Thereafter, controller variables were estimated using the frequency test results for each of the four control loops. In addition, time response simulations were performed using the estimated controller modeling. This method is thought to be of great help to reverse engineering in situations where there is completed equipment but no controller modeling.

Most sensors are affected by temperature, so they are tested in advance and used for temperature compensation. However, sensor affected by the temperature hysteresis is not compensated. This is because even if compensation is made in the form of a general n-th polynomial, the effect of hysteresis remains the same. In this paper, a method of compensating accelerometer biases with hysteresis using a new parameter C was studied. This technique goes beyond finding the appropriate variable for compensation and is a method of creating the parameter itself with a combination of new variables. As a result, most errors could be eliminated.

In this paper, the goal is to obtain a dynamic model of a particular system. The system is a combination of a wheeled vehicle(chassis) with a turret rotating in azimuth direction and a gun rotating in a elevation direction. At this time, the motion of the gun according to the shaking of the continuous shot is obtained using the coordinate transformation equation in the azimuth and elevation angle. Also, the dynamic model for the swaying of wheeled vehicle is obtained through the Lagrange’s equation. Through this, we analyze the tumbles of the gun, whiat is the major term, and what dynamics are needed for stabilization control.