This paper is intended to analyze benefits of microscopic real-time optimization for traffic signal control. To this end, we first present a microscopic traffic model consisting of signals and vehicles, where each vehicle dynamics is explicitly incorporated in the form of a mixed logical dynamical system (MLDS) differently from many other related publications taking macroscopic flow models. We then formulate a model predictive control (MPC) problem and then reduce the problem to a mixed integer programming problem. Furthermore, we introduce a linear temporal logic (LTL) constraint to reduce the computation burden and to eliminate unrealistic solutions. The proposed control scheme is then applied to a real-time traffic simulator and it is confirmed that: (i) the number of waiting vehicles for traffic lights is reduced with 54.5% as compared to a fixed-time control scheme and (ii) the average computation time is reduced to about one-tenth relative to the case without the LTL constraint.

We propose a new approach to controller parameter tuning in the two-degree-of-freedom control architecture, what is called as Estimated Response Iterative Tuning (ERIT). The idea is very simple in the sense that the feed-forward controller is tuned so as to realize that the estimated response calculated in the off-line is equal to the desired response. This approach is very useful because the required material for the optimization are the obtained data and the available information like the feedback controller, the desired transfer function, and the initial feed-forward controller. Here, we analyze the cost function which appears in the ERIT. To show the validity of the proposed method, we give an illustrative example.

We propose a “static” C^∞ differentiable control Lyapunov function (CLF) design method for differentially flat systems by dynamic extension and minimum projection method. For differentially flat systems, “dynamic” CLF with dynamic compensators are designed. Our proposed “static” CLF is obtained by the reduction of the “dynamic” CLF. Then we design a static state feedback controller with the obtained static CLF. Finally we show an example to show that our proposed static CLF based controller can stabilizes the origin of the system.

Performances of main components such as an engine, hydraulic pump, cylinder, valve and so on for a hydraulic excavator have been improved year by year. The system also related with a controller to assist operations have been complex more and more. An excavator is a general construction machine for a civil engineering and performs a various of applications such as a digging, truck loading, lifting and leveling. A powerful digging force and fast motions are required. Besides, very sensitive and accurate operations are needed. There is a response delay of the hydraulic system from an operator input command to a hydraulic cylinder motion as an actuator. When an assist controller to manipulate the bucket is developed, the delay should be considered in order to improve the accuracy. In this paper, we investigate to utilize Smith Predictor for a leveling operation which is required high accuracy as a system with time delay. It's confirmed that Smith Predictor is better than a conventional PID controller to quantitatively compare the following trajectories of the bucket tip to a target.

The statistical property of wind power fluctuation, which does not fit to the normal distribution because of its high probability of the extremal outlier, is regarded as a source of severe damage to power systems. In view of this, the authors have proposed an evaluation method for the impact of wind power fluctuation on power system quality, assuming that this heavy-tailed uncertainty obeys a power-law. In this paper, we first examine the validity of this assumption based on real data of frequency deviation under wind power interconnection. Then, the evaluation method is improved by extending theoretical results, and is applied to analysis of load frequency control model to verify its advantage over Monte Carlo methods.

This paper proposes a group method of data handling based PID (GMDH-PID) controller using LASSO that is one of the sparse modeling methods. A database-driven PID (DD-PID) controller is one of the effective nonlinear PID controller design methods. However, some electrical control units in industrial systems cannot implement its algorithm because the DD-PID control scheme requires a large amount of memory capacity and high-performance processor. In this paper, a GMDH-PID controller that approximately expresses the behavior of the DD-PID controller by combinations of simple nonlinear functions is proposed to solve the above problem. Although the method can reduce the amount of memory capacity and calculation costs, calculated coefficients in nonlinear functions may not stable due to the multicollinearity of input signals. In this research, instead of the least squares method, LASSO is employed as a coefficient calculation method of the GMDH in which some coefficients can be calculated as zero in order to avoid the influence of the multicollinearity. The effectiveness of the proposed method is evaluated by simulation examples.

The steel tube for liquefied natural gas in a petrochemical plant is used. This steel tube of a large diameter is covered with the thermal insulation material and the sheet metal. On the inspection of the thickness or the corrosion in this steel tube, the sheet metal and the thermal insulation material are removed from the steel tube. However, much cost and time are needed for this inspection. In this paper, the pulse electromagnetic inspection method for detecting the defect in the steel tube from the upper side of the insulation material and the sheet metal is proposed. The defect is inspected by measuring of the slope of the output voltage from the maximum output voltage value obtained immediately after cutting off the pulse wave magnetic field. In addition, the proposed inspection method is investigated by the 3-D nonlinear FEM taking account of hysteresis magnetizing properties and eddy current in the steel tube.