For the tacit knowing underlying basic highly-skilled handwork, this research focuses on the clarification of its basic structure and application, constructs the analytical theory, clears up effective aims for general workers to learn the skill, and establishes an efficient self-training method of the handwork. For that purpose, first the application process of the highly-skilled handwork is closely observed from the view point of phenomenology. Then, testing data are taken, and a mathematical model is established to grasp the skill as high-order vector orbits. Furthermore, by applying multi-dimensional differential geometry to this mathematical model, the tacit knowing is substantiated and its configuration is visualized and indexed. The objectives are accomplished by those configuration and indices.

This paper considers self-triggered model predictive control (MPC) for trajectory tracking of a mobile robot with uncertain slips between the wheels and ground over a network. Since the trajectory tracking model of the robot has a time-varying term due to the uncertain slip, the paper introduces a reachable set of the trajectory tracking nominal model to construct tube constraints. The proposed tube-based MPC problem has the properties that the tracking error and control input satisfy constant constraints under the uncertainty and that the MPC problem has recursive feasibility. Furthermore, the paper employs a self-triggered mechanism to cope with a remote manipulation of the robot over the network and reduce the communication. Numerical examples illustrate the effectiveness of the proposed tube-based MPC.

A segmented mirror control system is essential to realize extremely large telescopes. In this paper, we report the results of the control experiments of the Centralized Control System (CCS) which has been implemented in the Seimei telescope. Theoretical dynamic characteristics are observed from the transient response analysis. The control performance required from the astronomical observation is shown to be successfully achieved. This report is the world's first experimental result of segmented primary mirror control and would be useful for the development of the giant segmented primary mirror telescopes in the future.

Turbo-charged engine is a system contained complicated non-linear characteristics. In addition, the system have constrains for actuators and internal states. Therefore, to apply a nonlinear control system design such as Nonlinear Model Predictive Control (NMPC) is highly expected. In this paper, we propose a control-oriented model of the engine by fusion of a physical modeling and a system identification. The proposed model is derived by three steps. For the first step, we construct a control-oriented reduction model using Lasso regression analysis method for each element system of the engine. The second step, we combine the element models to give a state-space model of the entire turbocharged engine. The third step, we construct an error compensation model to improve the accuracy of the state-space model. The effectiveness of the proposed method is shown by using experiments data from actual engine.

In this paper, we discuss the estimation of passivity based on input-output data without a system model. In the conventional data-driven estimation method based on the gradient approach, the convergence is slow and a large number of experiments is required. Therefore, we propose the method of reduction in the amount of data for data-driven passivity estimation to MIMO systems. Furthermore, we show the convergence property of the proposed method. In addition, through numerical simulations, the effectiveness of the proposed method is verified.