초록 ▼

A method and system for controlling a dynamic nonlinear plant. An input signal controls the plant and an output signal represents a state of the plant in response to the received input signal. A memory stores input and output signals corresponding to m consecutive past states of the plant. A compute

A method and system for controlling a dynamic nonlinear plant. An input signal controls the plant and an output signal represents a state of the plant in response to the received input signal. A memory stores input and output signals corresponding to m consecutive past states of the plant. A computer neural network predicts a set of future output states representative of the output signal corresponding to the next n consecutive future states of the plant in response to a set of trial control inputs. The trial control inputs represent the input signal corresponding to the next n consecutive future states of the plant. The neural network predicts the future output states based on the past input and output signals and the future trial control inputs. A processor generates the trial control inputs and determines a performance index, indicative of plant performance over time in response to the trial control inputs, as a function of the future output states. The processor generates the input signal for controlling the plant and modifies it as a function of the trial control inputs so that the performance index reaches a desired value.

대표청구항 ▼

[ What is claimed is:] [1.] A method of controlling a dynamic nonlinear plant, said plant receiving an input control signal for controlling the plant and generating an output signal representative of a state of the plant in response to the received input control signal, said method comprising the st

[ What is claimed is:] [1.] A method of controlling a dynamic nonlinear plant, said plant receiving an input control signal for controlling the plant and generating an output signal representative of a state of the plant in response to the received input control signal, said method comprising the steps of:storing the input control and output signals corresponding to m consecutive past states of the plant over m time steps into the past;generating a set of trial control inputs, said trial control inputs being representative of the input control signal corresponding to the next n consecutive future states of the plant over n time steps into the future;predicting a set of future output states n time steps into the future for each trial control input with a computer neural network having a parallel processing arrangement, said future output states being representative of the output signal corresponding to the next n consecutive future states of the plant over n time steps into the future in response to the trial control inputs, said neural network predicting the future output states based on the past input and output signals and the future trial control inputs without using a recurrent network;determining a performance index as a function of the future output states, said performance index being indicative of plant performance over time in response to the trial control inputs; andmodifying the input control signal as a function of the trial control inputs for controlling the plant so that the performance index reaches a desired value.