Not only in homes and office buildings but in factories, energy saving is an important management theme. It is well known that energy saving is closely related to productivity improvement in the factory. However, it is difficult to visualize and analyze the energy information in association with the production information because the energy information and the production information are collected independently in the factory. In this paper, in order to solve this problem, we propose a factory energy management system that works with production information. In the proposed system, the PLC (Programmable Logic Controller) equipped with the power measurement unit collects energy information in association with the production information. Based on the collected information, the energy management system provides the energy visualization and the energy analysis capabilities by specific energy consumption. In this study, by introducing the proposed system to a production line of breaker in the factory, we evaluated the effectiveness of the proposed method.

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In automated production of industrial products such as semiconductor wafers and liquid crystal panels, transfer robots are widely used for conveyance. Although high speed motion provides shorten cycle time residual vibration generally occurs. Thus, vibration control is needed to suppress the residual vibration. This paper presents an identification technique based on FFT and least-square approach and proposes to employ a preshaping approach to reduce the residual vibration. Firstly, we achieve good vibration suppression by applying this approach to angular velocity commands for the robot. However, using a preshaping approach in that way causes a change in the end-effector trajectory, which may result in collision of the semiconductor wafer with the processing equipment. For this reason, we apply the preshaping approach to end-effector velocity instead of joint angular velocity to maintain the original end effector trajectory, and then derive the angular velocity by using inverse kinematics. Hence, we can achieve cycle time reduction by vibration suppression under the exact tracking to the original trajectory.

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This paper presents a novel method for selecting optimum measurement wavelengths in multi-wavelength interferometry. It is based on a coincidence error analysis in exact fractions solutions. By this method, three wavelengths of 470, 560, and 600nm have been selected. With these wavelengths, a surface profiler has been developed using three-wavelength single-shot interferometry, and an extended measurement range over 8µm has been experimentally proved.

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It is well-known that preprocessing of the measured input-output data is very important to obtain accurate parameter estimates by system identification methods. This paper treats LTI (linear time-invariant) systems with integrators that appear in various engineering fields, for example, a physical system with rigid body motion and DC servo motors. However, it is known that it is difficult to identify the systems with integrators because they are unstable and astatic system. The purpose of this paper is to propose an appropriate preprocessing method and an identification procedure for the systems with integrators. First, preprocessing of input-output data for such systems is proposed, which removes low-frequency disturbances. Second, a new identification procedure for such systems is proposed, which applies an integrator on input data. Finally, effectiveness of the proposed methods is examined through numerical examples.

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In model predictive control (MPC), an optimal control problem is solved at each time steps to determine control input. To realize on-line control of MPC, reducing computational time is requisite. In this paper, we apply a semismooth Newton method for MPC with simple bounds. The semismooth Newton method is one of iterative methods and is used to solve a complementarity problem and a KKT system of optimization problems. The semismooth Newton method has an advantage over other QP solvers, such as interior point methods and so on, that the initial point can be chosen arbitrarily, and this enables hot start. We show that the proposed method is globally convergent. We also show the condition guaranteeing the nonsingularity of the generalized Jacobian at a solution, which is closely related to the quadratic convergence of the algorithm. This is the first result to clarify the reason why constraints on state variables make MPC computationally expensive from the algorithmic perspective. Some numerical examples show that the proposed method is practically efficient.

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This paper outlines a probabilistic neural network developed on the basis of time-series discriminant component analysis (TSDCA) that can be used to classify high-dimensional time-series patterns. TSDCA involves the compression of high-dimensional time series into a lower-dimensional space using a set of orthogonal transformations and the calculation of posterior probabilities based on a hidden Markov model that incorporates a Gaussian mixture model expressed in the reduced-dimensional space. The analysis can be incorporated into a neural network so that parameters can be obtained appropriately as network coefficients according to backpropagation through time-based training algorithm. The network is considered to enable high-accuracy classification of high-dimensional time-series patterns and to reduce the computation time taken for network training. In the experiments, the validity of the network was demonstrated for high-dimensional artificial data. The results showed that the network can achieve high discrimination performance in a relatively short learning time. The applicability to biosignal classification was also demonstrated through an EEG discrimination experiment.

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This paper is concerned with applicability of the digitally implemented intelligent PID control. First, a stability condition of the intelligent PID control systems is given for a class of linear plants when the controller including derivatives are implemented digitally. Second, some simulation results are given to show the effectiveness of the intelligent PID controller and validity of the stability condition. Furthermore, experimental validation for the intelligent PID controller is performed by using a servo motor control.

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This paper studies pose synchronization on the Special Euclidean group SE(3) for a network of rigid bodies. In particular, we extend our earlier results on the topic to expand a class of interaction graphs for which synchronization is proved. We first give the definitions of rigid body networks and pose synchronization as the controlled system and goal of this work, respectively. We next introduce our previous results as the preliminaries. Then, by introducing a condensation graph and stability theory of perturbed systems, we derive a necessary and sufficient condition in terms of interconnection topologies for pose synchronization on SE(3). We finally demonstrate the effectiveness of the present analysis through simulation.

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This paper reports development of a real-time remote control system for robots using augmented reality (AR) technology. An operator can control a robot via the Internet or LAN in real-time by using the system, obtaining an image composed of a remote web camera image and 3D virtual objects. In the paper, a transmission method of the web camera image using UDP and an application method of AR are explained and application of the system to the treasure hunter robot exhibited in Akagane Industrial Expo 2012 is presented.

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