At present, the unprecedented cholera outbreak occurs in Yemen and various kinds of infectious diseases are still threat to us in the high-developed medical technology society. Hence, the strategization to control the spread of the infectious diseases becomes imperative. In the vector-borne diseases such as malaria and dengue fever, there exists time delay caused by an incubation period in the virus development in the vectors on the transmission of disease. It should be noted that there is possibility of getting reinfected in the infectious disease such as malaria. Moreover, in the realistic spread of the infectious disease, environmental change and individual difference cause some kinds of random fluctuations in the infection, the recovery rates and the vaccination effect. Taking these facts into consideration, we propose two types of the stochastic delayed infectious models with reinfection. Since the spread of infection has reference to the stability of the disease-free steady state (DFS) of the stochastic infectious models, we analyze the stability of the DFS by using the stochastic Lyapunov theorem. By calculating the Lyapunov exponent, we study the influence of the random noise in the infectious model on each population behavior by numerical simulations.

A PDS based bilateral control system of flexible master-slave arms with random delay is investigated in this paper. Moreover, a flexible slave arm employed in this research is affected by a contact force. A rigid master arm and a flexible slave arm which is actuated by a high-geared servomotor construct the proposed bilateral control system. Both arms are connected by a communication network. It is considered that this network occurs a random delay. Furthermore, the contact force is added to the flexible arm during its motion. Thus, the motion of this arm is restricted by the contact force. A Kalman filter is designed to estimate signals and controllers for both arms are designed as the PD- and PDS-controllers, respectively. Numerical simulations are achived to confirm the performance of the proposed system such as the instability of its motion and the adaptivity of the Kalman filter.

In this paper, water level control of after condenser in a spray flash desalination system is considered. First, a mathematical model of the water level is constructed based on mass conservation law and thermophysical property. Secondly, control system using PI controller and some characteristics concerning the valve is designed. A simulation result by using the control system shows that the behavior of an experimental result is not captured appropriately. In order to tackle this problem,in this paper, another water level model is proposed by introducing a white Gaussian process. The effectiveness of the control system with proposed model is verified through simulation results.

Motivated by a biological motion control model called feedback error learning (FEL), feedforward learning control schemes have been extensively studied with emphasis on their implementation by linear filters and tuning parameters. Its multi-input multi-output (MIMO) generalization, however, has been redundant in terms of pole/zero cancellation structure. The objective of this paper is to propose an exact feedforward controller by introducing the notion of left-right polynomial matrix factorization.

The author previously applied Extended Kalman Filter for estimating the posture of depth sensor attached to a walking person or a mobile vehicle in conjunction with the surface of the plane, where the posture of the sensor was included in the state vector of the model. However, it could not adapt to situations when the initial value of Extended Kalman Filter was not appropriate. The initial value of the Extended Kalman Filter strongly affects the filtering results, especially for the model when the domain of the state vector has several groups. Different starting point leads to different results. In this paper, setting method of the initial value of the algorithm is proposed. Trial and error approach has proven to work well and has a good property to adapt to various situations. By defining the reset conditions differently, it is shown that the system can detect different surfaces.