###### Abstract
In another study, a null-space solution was used to plan the contact position between a picking object and a robot gripper [10]. There was a study of behavior-based control techniques for mobile robots [11]. Like the previous studies, this paper improves the accuracy of the system identification by using a null-space solution. To specify the solution of the null space, a constraint is required. In this study, weights are added to the dynamic equation of the loading device, and a null-space solution is specified with the constraint that the weights should offset. The estimated unknown parameter can be adjusted by the relative size of the weights, and a more accurate estimation is possible. These system identification techniques are verified by a simulation. After the system identification process, the PID (Proportional-Integral-Differential) controller is determined by gain scheduling. There are previous studies for controller scheduling to respond to a system change. A study handled PID scheduling for sudden braking and acceleration of a vehicle [12]. In the other studies, the controller for a fixed-wing UAV was adjusted depending on the velocity [13] and gains for a robot arm were with the degree of the joint [14]. Also, there were various strategies to schedule PID gains. The Fuzzy logic was used, for example, and there were studies for a UAV [15] and hybrid stepper motor [16]. In the other studies, the gain scheduler was based on a genetic Fuzzy model [17], and neuro-Fuzzy networks [18]. A quadrotor UAV was controlled with a gain scheduler based on the theory of Lyapunov [19]. A study developed a gain scheduler for a 2-axes pneumatic artificial muscle robot arm named MIMO neural PID control [20]. In this paper, there are two independent gain scheduler sets for each frame of each axis of the 2-axes gimbal structure loading device. The gain scheduler is constructed as each surface functions set that uses the estimated unknown parameters in the system identification process as the variables. The gains of each PID controller are determined by assigning each estimated unknown parameter to the gain scheduling surface function. The three-dimensional gain surface functions are constructed in advance by interpolating the optimized PID gains obtained by varying the unknown parameters. In the case of the controller structure, the weights for the null-space solution of the system identification are determined by the torque of each motor. Therefore, the motors can operate with the optimized PID controller to the required torque. The experiments using a motion platform were conducted with the loading device. This paper shows three experimental results. The weights of the luggage for each experiment change. The tilt motion of the motion platform simultaneously tilts and returns 20 degrees around the pitch axis and 15 degrees around the roll axis. In the results of the experiments, the dynamic equation that moves in equal motion with the real loading device is estimated by the system identification. Also, the PID gain scheduler adjusts the controller depending on the demanded motor torque which changes with the luggage. The structure of this paper is as follows. Section 2 presents the system identification technique. Section 3 handles the structure of the adaptive controller, including the gain scheduler. Section 4 shows the experiment. Finally, Section 5 is the conclusion of this paper.