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This paper deals with a control strategy for a rotary crane. On operating a crane, it is necessary to transfer a load without swing in a time as short as possible. We constitute a automatic control system to automate the operation of rotary crane.The control variables of rotary crane system are a rotary velocity and an angle of elevation. In order to realize the time optimal control system with no oscillation of a load at the desirable position, and to prevent the effect of disturbance, fuzzy theory are applied to the system. This paper presents the result of control simulation. Also, it is examined that this control method is efficient for practical use.control methods of rotary crane by using fuzzy theory as well as by skilled professional operators.

In digitally controlled circuits for power converter circuits, sampling data is important because a digital control circuit is operated on the basis of these data. If the sampled values have been affected by switching noise from the power circuit, the control stability of the circuit would be disturbed. This paper proposes a noiseless sampling method for both synchronous sampling and multisampling, which can sample a value without being affected by noise. The synchronous sampling method may be affected by noise depending on the duty ratio of the circuit.The noiseless sampling method for synchronous sampling changes the timing of the sampling to a position that is less susceptible to noise. The noiseless sampling method for multisampling does not obtain the data immediately after turn-on and -off switching.The control circuit can avoid switching noise by using noiseless sampling, which leads to a disturbance in the control circuit and enhances the robustness of the circuit when applying the multisampling method. Experimental results are presented to verify that the current control of the proposed sampling methods is not disturbed by noise.

In industrial systems, certain process variables that need to be monitored for detecting faults are often difficult or impossible to measure. Soft sensor techniques are widely used to estimate such difficult-to-measure process variables from easy-to-measure ones. Soft sensor modeling requires training datasets including the information of various states such as operation modes, but the fault dataset with the target variable is insufficient as the training dataset. This paper describes a semi-supervised approach to soft sensor modeling to incorporate an incomplete dataset without the target variable in the training dataset. To incorporate the incomplete dataset, we consider the properties of processes at transition points between operation modes in the system. The regression coefficients of the operation modes are estimated under constraint conditions obtained from the information on the mode transitions. In a case study, this constrained soft sensor modeling was used to predict refrigerant leaks in air-conditioning systems with heating and cooling operation modes. The results show that this modeling method is promising for soft sensors in a system with multiple operation modes.

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