Hitachi, Ltd.

Tadayoshi Saito

Hiroshi Matsumoto

Masahide Nomura

Susumu Takahashi

Makoto Shimoda

Junzo Kawakami

Hisanori Miyagaki

Kouji Tachibana

Nobuyuki Yokokawa

Nobuo Kurihara

Yoshio Sato

Hiroshi Yokoyama

Seiitsu Nigawara

Akira Kaji

Kenji Baba

Yasunori Katayama

Akira Sugano

Shigeyoshi Kawano

Hiromasa Yamaoka

Mitsuyo Nishikawa

Hiroshi Hanaoka

Yasuhiro Tennichi

Toshihiko Higashi

Mitsuaki Kobayashi

Kohji Tachibana

Eiji Tohyama

Masakazu Kondoh

Testuo Suehiro

A process control method and a process control apparatus using a fuzzy control with reducing rules and membership functions. The process control apparatus for determining a manipulating variable of a controlled object, includes an input/output unit for inputting a present value of a controlled variable and for outputting the determined manipulating variable, a predicting unit having a simulator of a controlled object, for calculating a control variable after a predetermined time has passed, assuming now that a present value of the manipulating variable is continued, and a fuzzy controlling unit for inferring a manipulating variable at a present time by inputting a deviation value between a preset set-point value and a predicted value. The simulator simulates the controlled object as dead time and a first order delay. Since only the predicted value of the controlled variable is employed as the input of the fuzzy control, fuzzy inference can be simplified and noise resistance can be improved.

Process control method and system with employment of fuzzy inference

A state diagnostic system for equipment is disclosed. The system is constructed of a neural network model for learning in advance one or more samples of information on vibrations, which are produced in a specific operation state of the equipment, in association with the corresponding operation state and obtaining an output signal corresponding to results of the learning when information on vibrations produced upon operation of the equipment is inputted; an input unit for inputting to the neural network model the information on the vibrations produced upon operation of the equipment; and an output unit for outputting the output signal from the neural network model as diagnostic results to a user. State diagnostic methods, learning system, preview/predict system, diagnosis training system, service life estimation assisting system, service life estimation assisting system, and maintenance assisting system are also disclosed.

Neural network state diagnostic system for equipment

A method for controlling a controlled system by a controller such that a controlled variable can be brought into conformity with a desired value. With respect to at least one of input/output variables for a combined controlling-controlled system, which includes in combination the controller and the controlled system, and input/output variables for the controlled system, information containing its characteristics is taken out from the combined controlling-controlled system. The information with the characteristics contained therein is inputted to a neural network which has been caused beforehand to learn a correlation between the information containing the characteristics and control parameters. From the neural network, one or more of the control parameters, said one or more control parameters corresponding to a corresponding number of inputs to the neural network, are outputted to the controller.

Process control method and system for performing control of a controlled

A process control system controls a large scale plant such as thermal power plant. This process control system includes a target setting unit for setting an operation target, a control unit for receiving a signal indicating the operation target and for outputting a controlled variable to operate the process, an evaluation unit for quantitatively evaluating operation characteristics corresponding to the operation target of the process operated on the basis of a signal indicating the controlled variable supplied from the control unit, a modification unit for extracting an optimum operation process qualitatively squaring or conforming with the evaluated value derived by the evaluation unit out of a modification rule predetermining operation unit in qualitative relation between the operation characteristics and the operation target of the process and for determining the modification rate of the control unit, a storage unit having a model of a neural network for storing a relation between the operation target and the modification rate derived by the modification unit as a connection state within a circuit, and a learning unit for making the model of the neural network learn the relation between the operation target and the modification rate.

Process control system and power plant process control system

A process control apparatus and a process control system for observing the waveform of the deviation between a set point which controls the process in a control loop and the process controlled variable transmitted from the process so that the operating parameters are adjusted in accordance with a predetermined adjustment rule by using the deviation based upon the absolute value of a wave for every half cycle of the wave. Therefore, even if the process controlled variable is influenced, the area of the wave cannot be changed significantly. As a result, the operating parameters can be adjusted correctly, causing the process controlled variable to quickly coincide with the set point.

Process control apparatus and method for adjustment of operating

A self-tuning controller apparatus which comprises: a control response pattern recognition portion for receiving a set-point and a controlled variable of a process and for obtaining three performance indexes from a controlled variable response pattern generated in response to a change in the set-point or a disturbance; a control parameter correction portion for qualitatively evaluating the three performance indexes and for inferring a correction value of a PID control parameter on the basis of the result of evaluation; and a control performance dissatisfaction degree evaluation portion for evaluating the inferred correction value of the PID control parameter in accordance with the dissatisfaction degrees of the two performance indexes for the respective target specifications of the two of the three performance indexes. The control performance dissatisfaction degree evaluation portion acts to make the PID control parameter converge stably. In a process control system including a plurality of vertically distributed control elements each capable of self-tuning its control parameter, the control performance dissatisfaction degree evaluation portion contributes to autonomous functioning of each of the control elements.

Self-tuning controller apparatus and process control system

PID control method and PID controller for determining, from a control response from a process, characteristic values representative of controllability and automatically deriving and setting PID constants from the determined characteristic values. The error between a set point and a controlled value is decided as to whether to be due to a change in set point or due to a disturbance, and PID constants are set on the basis of results of decision.

Method and apparatus of automatically setting PID constants

A PID controller system comprises a PID controller for PID controlling a process standing for an object to be controlled, and an automatic adjuster being responsive to a variable relating to the manipulation of the PID controller and the response shape of the control variable from the process to obtain necessary performance indexes and preparing, on the basis of the performance indexes, optimum control parameters for tuning the actual control variable to the control command value, the optimum control parameters being fedback from the automatic adjuster to the PID controller.

Pid controller system

A method of controlling combustion in a furnace of a boiler or the like having a burner for a main combustion and a burner for a reducing combustion in order to effect combustion for furnace denitrification. The method comprises the steps of: estimating the NO.sub.x generation amount from data on a flame formed by the main combustion; estimating the reducing agent generation amount from data on a flame formed by the reducing combustion; and controlling the flow rates of fuel and air supplied for the main and reducing combustions so that the amount of NO.sub.x emission as the difference between the NO.sub.x generation amount and the reducing agent generation amount is below a specified value. In practice, each of the NO.sub.x generation amount and the reducing agent generation amount is estimated from the flame pattern, the flame volume, the distance between the outlet of the burner concerned and the root of the flame concerned, etc., thereby to distributively control the flow rates of fuel and air supplied to each of the main combustion burner and the reducing combustion burner so that the amount of NO.sub.x emission is below a specified value.

Method of controlling combustion

A system and method for the adaptive control of a process in which a feed forward control signal corresponding to a process demand is calculated according to a predetermined algebraic function, while a feedback correction signal is calculated on the basis of an error of a process feedback signal indicative of an error of a controlled variable from a predetermined setting, and the controlled variable of the process is controlled on the basis of the sum of these two signals. The adaptive control is such that, when a set point of the function deviates from the actual process demand, a value corresponding to the error appears in the feedback correction signal, and this value is used for automatically modifying the function itself to ensure the adaptive control of the process. A determination is made whether or not the process is in the steady state and when steady state operation is determined, the function of the feed forward control signal is modified.

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Hitachiota, Japan

Tadayoshi Saito