Universal process control using artificial neural networks
First Claim
1. A control system for controlling an open-loop stable process, said process having a process output represented by a process output signal, and being responsive to a control signal for changing said process output as a function of said control signal, said system comprising:
- a) setpoint selection means for generating a setpoint signal representative of a desired value of said process output;
b) comparator means connected to said setpoint selection means and said process output for comparing said process output signal to said setpoint signal and for deriving from said comparison an error signal representative of the difference between said process output signal and said setpoint signal;
c) sampling means connected to said comparator means for producing time-spaced samples of said error signal at selected time intervals; and
d) control means connected to said sampling means and said process, said control means being responsive to said error signal samples for generating said control signal,e) said control means being an artificial neural network, said network including a plurality of layers of neurons connected so as to interact with each other in accordance with variable weights, a first of said layers having a plurality of neuron inputs, and another of said layers having a network output; and
f) said sampling means being so connected to said network as to simultaneously apply to individual ones of said neuron input signals representative of individual ones of said time-spaced error signal samples, and also to simultaneously vary individual ones of said weights as a function of individual ones of said time-spaced error signal samples.
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Abstract
Adaptive control for a wide variety of complex processes is provided by an ANN controller with input and hidden layers having a plurality of neurons, and an output layer with a single neuron. The inputs to the ANN are a time sequence of error values, and the neuron paths are weighted as a function of these error values and the present-time process output. The present-time error value may be added to the output layer of the ANN to provide faster response to sudden input changes. The controller of this invention can efficiently handle processes with nonlinear, time-varying, coupled and variable-structure behaviors as well as process parameter and/or structure uncertainties. Large steady-state gains in the process can be compensated by attenuating the ANN block output.
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Citations
11 Claims
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1. A control system for controlling an open-loop stable process, said process having a process output represented by a process output signal, and being responsive to a control signal for changing said process output as a function of said control signal, said system comprising:
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a) setpoint selection means for generating a setpoint signal representative of a desired value of said process output; b) comparator means connected to said setpoint selection means and said process output for comparing said process output signal to said setpoint signal and for deriving from said comparison an error signal representative of the difference between said process output signal and said setpoint signal; c) sampling means connected to said comparator means for producing time-spaced samples of said error signal at selected time intervals; and d) control means connected to said sampling means and said process, said control means being responsive to said error signal samples for generating said control signal, e) said control means being an artificial neural network, said network including a plurality of layers of neurons connected so as to interact with each other in accordance with variable weights, a first of said layers having a plurality of neuron inputs, and another of said layers having a network output; and f) said sampling means being so connected to said network as to simultaneously apply to individual ones of said neuron input signals representative of individual ones of said time-spaced error signal samples, and also to simultaneously vary individual ones of said weights as a function of individual ones of said time-spaced error signal samples. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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8. The system of claim 7, in which the constant η
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9. The system of claim 8, in which said learning rate is selected from a range of substantially 1 to substantially 3.
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10. The system of claim 9, in which said learning rate is selected to be substantially 2.
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11. The system of claim 1, further comprising means for attenuating the output of said network to compensate for high DC gain in said process.
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Specification