In the paper, a novel trajectory shaping scheme to improve the manipulation skill of the articulated manipulator for the liquid container transportation is proposed. The paper investigated the transient free surface behavior of liquid inside a cup during the horizontal transportation. Based on the observation of the free surface vibration of the liquid, the adaptive command shaping filter is proposed for the generation of the transportation trajectory to reduce the fluctuation magnitude without increasing the time required to travel the given transportation distance. Simulation results verify the effectiveness of the proposed scheme. An experimental testbed of an articulated manipulator with a simplified model of a liquid container is established. The proposed manipulation approach is implemented in the experimental setup and the experimental results also verify the effectiveness of the proposed scheme.

In this paper, we made a simple paper feeding system which is one of MTS (media transport system) and controllers. The plant has a flexible paper and two driving rollers and two driven rollers. The control system has two conventional PID controllers. Skew angle and feeding speed of MTS deteriorate the quality of feeding system. In order to control a feeding speed and skew of feeding paper, we control rotational velocity of two driving rollers. Therefore, this controller has two inputs and two outputs as MIMO (multi-input and multi-output) system. The control inputs were the feeding speed and the skew displacement of the paper. The control outputs were the rotational velocity to each driving roller. To find appropriate PID gains of two controllers, we proposed an optimization technique. We assume the system variables and performance of a whole system as follows. PID gains of two controllers for skew and feeding speed are system variables. System performance is both skew and feeding speed. We simulates to making mathematical correlation using global Kriging interpolation. To find appropriate value of system variables, optimization method is simulation in sequence as following method. First, the optimization solver simulates with DOE (design of experiment) tables to find correlation equation of both system variable and performances. Then, the solver guesses the appropriate values and simulates if the system variables are appropriate or not. If the result of validation doesn't satisfy the convergence and iteration tolerance, the solver makes a new Kriging models and iterates this sequence until satisfy the tolerances.