For human-centered automation, this study presents a wireless sensor network using predicted mean vote (PMV) as a thermal comfort index around occupants in buildings. The network automatically controls air conditioning by means of changing temperature settings in air conditioners. Interior devices of air conditioners thus do not have to be replaced. An adaptive neurofuzzy inference system and a particle swarm algorithm are adopted for solving a nonlinear multivariable inverse PMV model so as to determine thermal comfort temperatures. In solving inverse PMV models, the particle swarm algorithm is more accurate than ANFIS according to computational results. Based on the comfort temperature, this study utilizes feedforward-feedback control and digital self-tuning control, respectively, to satisfy thermal comfort. The control methods are validated by experimental results. Compared with conventional fixed temperature settings, the present control methods effectively maintain the PMV value within the range of and energy is saved more than 30% in this study.Note to Practitioners-For advanced control of unitary air conditioners in rooms, air conditioners may have to be retrofitted or connected with extra devices by wire connection, whose processes may be difficult for users, and inappropriate installation may damage original air-conditioning units. This study hence presents a noninvasive method for indoor thermal comfort with a wireless sensor network. The present method facilitates hardware implementation without changing interior devices of the air conditioner. The wireless sensor network measures temperature, air velocity, and humidity around occupants and further transmits temperature commands for air conditioner control. Based on the measured data, a PMV model is adopted to evaluate thermal comfort. Using an inverse PMV model with feedforward-feedback control and selftuning control, respectively, this study aims to automatically maintain human thermal comfort as well as save energy. The ANFIS model and a particle swarm algorithm are used to solve the inverse PMV model and determine the thermal comfort temperature. Based on that temperature, feedforward-feedback control, and self-tuning control are used to determine appropriate temperature settings in the air conditioner so as to change the cooling capacity and maintain thermal comfort. Experimental results show that the present control method can maintain thermal comfort and saves 30% more energy than the conventional method.

To improve preventive maintenance, this study uses a hybrid Petri net modelling method coupled with parameter trend and fault tree analysis to perform early failure detection and isolation. A Petri net arrangement is proposed that facilitates alarm, early failure detection, fault isolation, event count, system state description and automatic shutdown or regulation. These functions are very useful for health monitoring and preventive maintenance of a system. A fault diagnosis system for district heating and cooling facilities is employed as an example to demonstrate the proposed method.

This study develops dynamic analysis based on the dynamic stiffness method for a rotating beam of nonuniform cross-section. To deal with nonuniform beams, coefficients related to material and geometric properties in the equation of motion are expressed in a polynomial form. A dynamic stiffness matrix is accordingly formulated in terms of power series. The dynamic response of the rotating beam is calculated by performing modal analysis. It is demonstrated that the present method provides an alternative to the finite element method in dealing with nonuniform rotating beams.

In order to make a robot precisely track desired periodic trajectories, this work proposes a sliding mode based repetitive learning control method, which incorporates characteristics of sliding mode control into repetitive learning control. The learning algorithm not only utilizes shape functions to approximate influence functions in integral transforms, but also estimates inverse dynamics functions based on integral transforms. It learns at each sampling instant the desired input joint torques without prior knowledge of the robot dynamics. To carry out sliding mode control, a reaching law method is employed, which is robust against model uncertainties and external disturbances. Experiments are performed to validate the proposed method. [S0022-0434(00)02001-3]

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