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US5847952A
Expired / Lapsed
US
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Nonlinear-approximator-based automatic tuner
Assignee: Honeywell Inc
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Filing: 1996-06-28
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Granted: 1998-12-08
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Expires: 2016-06-28
Abstract
Abstract An automatic tuner for control systems that produces, as output values, parameters of an arbitrary controller. The controller is in a control loop so that its output effects changes in actuators and regulates a physical process. The controller has either linear or nonlinear controller components, or a combination of both. The tuner has a nonlinear approximator that has been optimized off-line. The off-line optimization is done without supervised learning so that desired outputs of the nonlinear approximator do not need to be available, and separate optimization to generate the desired outputs is not necessary. The off-line optimization can also rely on arbitrary criteria. Such optimization ensures robustness of generated controller parameters so that the input process characteristics do not need to be highly accurate. The inputs to the nonlinear approximator consist of two sets of input parameters, either of which may be empty. A first set of input parameters can relate to process characteristics. A second set of input parameters can relate to desired closed-loop system behavior. The output values may be proportional and/or integral and/or derivative gains for PID-like controllers, or otherwise be parameters for delay-compensation controllers, controllers that consist of lead-lag terms in combination with PID controllers, higher-order linear controllers, or nonlinear controllers of predetermined structure. The nonlinear approximator may be implemented as a compositional sigmoidal mapping, a multilayer perception structure, a fuzzy logic model, a radial basis function network, a polynomial expansion, or other parametrized nonlinear structure.
Inventors
- 👤 Tariq Samad
Bibliographic Details
Application Number
—
Jurisdiction
US
Data Source
google_patents
Last Synchronized
2026-10-31 16:54
CPC Classifications
G; G05; G05B; G05B13/00; G05B13/02; G05B13/0265; G05B13/027
Claims Analysis
Total Claims: 32
Claim 1
Independent Claim
151 words
A tuner for producing internal gain parameters of a controller, comprising: preprocessing means, having an input and an output, for processing a set of parameters at the input into a set of transformed parameters at the output; nonlinear approximator means, having an input connected to the output of said preprocessing means and having an output, for operating on the set of transformed parameters to produce a set of normalized controller tuning parameters at the output wherein said nonlinear approximator means is offline optimized in model based simulation without advance supervised learning; and postprocessing means, having a first input connected to the output of said nonlinear approximator means, and having a second input and an output, for scaling the set of normalized controller tuning parameters into a set of scaled controller tuning parameters at the output, wherein the scaled controller tuning parameters are fed to the controller for optimally controlling a process.
Claim 2
Dependent Claim
151 words
A tuner for producing internal gain parameters of a controller, comprising: preprocessing means, having an input and an output, for processing a set of parameters at the input into a set of transformed parameters at the output; nonlinear approximator means, having an input connected to the output of said preprocessing means and having an output, for operating on the set of transformed parameters to produce a set of normalized controller tuning parameters at the output wherein said nonlinear approximator means is offline optimized in model based simulation without advance supervised learning; and postprocessing means, having a first input connected to the output of said nonlinear approximator means, and having a second input and an output, for scaling the set of normalized controller tuning parameters into a set of scaled controller tuning parameters at the output, wherein the scaled controller tuning parameters are fed to the controller for optimally controlling a process.
Claim 3
Dependent Claim
14 words
The tuner of claim 1 wherein said nonlinear approximator means is a neural network.
Claim 4
Dependent Claim
14 words
The tuner of claim 1 wherein said nonlinear approximator means is a fuzzy approximator.
Claim 5
Dependent Claim
16 words
The tuner of claim 1 wherein said nonlinear approximator means is a radial basis function approximator.
Claim 6
Dependent Claim
18 words
The tuner of claim 1 wherein said nonlinear approximator means is an approximator that utilizes nontranscendental mathematical functions.
Claim 7
Dependent Claim
20 words
The tuner of claim 1 wherein said nonlinear approximator means is an approximator that utilizes computationally simple compositional sigmoidal mappings.
Claim 8
Dependent Claim
164 words
A tuner for producing internal gain parameters of a controller, comprising: preprocessing means, having an input and an output, for transforming a set of input parameters at the input into a set of normalized parameters at the output, nonlinear approximator means, having an input connected to the output of said preprocessing means and having an output, for algorithmically operating on the set of normalized parameters in that said nonlinear approximator means is setup offline using a system that integrates closed-loop simulations with an optimization algorithm that optimizes parameters of said nonlinear approximator means to minimize a cost function that indicates performance of the closed-loop simulations to result in a set of normalized tuning parameters at the output; and postprocessing means, having a first input connected to the output of said nonlinear approximator means, having a second input connected to the input of the preprocessing means and having an output, for scaling the set of normalized tuning parameters into controller tuning parameters at the output.
Claim 9
Dependent Claim
164 words
A tuner for producing internal gain parameters of a controller, comprising: preprocessing means, having an input and an output, for transforming a set of input parameters at the input into a set of normalized parameters at the output, nonlinear approximator means, having an input connected to the output of said preprocessing means and having an output, for algorithmically operating on the set of normalized parameters in that said nonlinear approximator means is setup offline using a system that integrates closed-loop simulations with an optimization algorithm that optimizes parameters of said nonlinear approximator means to minimize a cost function that indicates performance of the closed-loop simulations to result in a set of normalized tuning parameters at the output; and postprocessing means, having a first input connected to the output of said nonlinear approximator means, having a second input connected to the input of the preprocessing means and having an output, for scaling the set of normalized tuning parameters into controller tuning parameters at the output.
Claim 10
Dependent Claim
69 words
The tuner of claim 7 wherein: the input parameters are either or both of two types of parameters, wherein the two types of parameters are: parameters characterizing a system to be controlled; and parameters characterizing a desired closed-loop behavior; and said nonlinear approximator means is optimized offline without supervised learning and wherein the offline optimization does not require generation of optimized tuning parameters for optimizing said nonlinear approximator means.
Claim 11
Dependent Claim
14 words
The tuner of claim 8 wherein said nonlinear approximator means is a neural network.
Claim 12
Dependent Claim
14 words
The tuner of claim 8 wherein said nonlinear approximator means is a fuzzy approximator.
Claim 13
Dependent Claim
16 words
The tuner of claim 8 wherein said nonlinear approximator means is a radial basis function approximator.
Claim 14
Dependent Claim
18 words
The tuner of claim 8 wherein said nonlinear approximator means is an approximator that utilizes nontranscendental mathematical functions.
Claim 15
Dependent Claim
20 words
The tuner of claim 8 wherein said nonlinear approximator means is an approximator that utilizes computationally simple compositional sigmoidal mappings.
Claim 16
Independent Claim
123 words
A system for setting up a tuner comprising: a tuner having inputs; approximator parameters; controller parameters; a closed-loop control simulator; a cost function; and an optimization algorithm; and wherein: the tuner includes a nonlinear approximator, the behavior of which can be modified by adjusting the approximator parameters; the tuner outputs controller parameters which are used in simulations with the closed loop control simulator; a value of the cost function is computed from closed loop control simulations; the value of the cost function is input to the optimization algorithm which optimizes approximator parameters; optimized approximator parameters minimize the value of the cost function; a minimized value of the cost function leads to appropriate controller parameters; and appropriate controller parameters result in optimized controller performance.
Claim 17
Independent Claim
123 words
A system for setting up a tuner comprising: a tuner having inputs; approximator parameters; controller parameters; a closed-loop control simulator; a cost function; and an optimization algorithm; and wherein: the tuner includes a nonlinear approximator, the behavior of which can be modified by adjusting the approximator parameters; the tuner outputs controller parameters which are used in simulations with the closed loop control simulator; a value of the cost function is computed from closed loop control simulations; the value of the cost function is input to the optimization algorithm which optimizes approximator parameters; optimized approximator parameters minimize the value of the cost function; a minimized value of the cost function leads to appropriate controller parameters; and appropriate controller parameters result in optimized controller performance.
Claim 18
Dependent Claim
13 words
The system of claim 14 wherein the nonlinear approximator is a neural network.
Claim 19
Dependent Claim
13 words
The system of claim 14 wherein the nonlinear approximator is a fuzzy approximator.
Claim 20
Dependent Claim
15 words
The system of claim 14 wherein the nonlinear approximator is a radial basis function approximator.
Claim 21
Dependent Claim
17 words
The system of claim 14 wherein the nonlinear approximator is an approximator that utilizes nontranscendental mathematical functions.
Claim 22
Dependent Claim
19 words
The tuner of claim 14 wherein the nonlinear approximator is an approximator that utilizes computationally simple compositional sigmoidal mappings.
Claim 23
Dependent Claim
140 words
A tuner for producing internal gain parameters of a controller, comprising: preprocessing means, having an input and an output, for processing a set of input tuning parameters that indicate desired or appropriate performance characteristics at the input into a set of transformed parameters at the output; nonlinear approximator means, having an input connected to the output of said preprocessing means and having an output, for operating on the set of transformed parameters to produce a set of normalized tuning parameters at the output; and postprocessing means, having a first input connected to the output of said nonlinear approximator means, and having a second input and an output, for scaling the set of normalized tuning parameters into a set of scaled tuning parameters at the output, wherein the scaled tuning parameters are fed to the controller for optimally controlling a process.
Claim 24
Dependent Claim
140 words
A tuner for producing internal gain parameters of a controller, comprising: preprocessing means, having an input and an output, for processing a set of input tuning parameters that indicate desired or appropriate performance characteristics at the input into a set of transformed parameters at the output; nonlinear approximator means, having an input connected to the output of said preprocessing means and having an output, for operating on the set of transformed parameters to produce a set of normalized tuning parameters at the output; and postprocessing means, having a first input connected to the output of said nonlinear approximator means, and having a second input and an output, for scaling the set of normalized tuning parameters into a set of scaled tuning parameters at the output, wherein the scaled tuning parameters are fed to the controller for optimally controlling a process.
Claim 25
Dependent Claim
14 words
The tuner of claim 20 wherein said nonlinear approximator means is a neural network.
Claim 26
Dependent Claim
14 words
The tuner of claim 20 wherein said nonlinear approximator means is a fuzzy approximator.
Claim 27
Dependent Claim
16 words
The tuner of claim 20 wherein said nonlinear approximator means is a radial basis function approximator.
Claim 28
Dependent Claim
18 words
The tuner of claim 20 wherein said nonlinear approximator means is an approximator that utilizes nontranscendental mathematical functions.
Claim 29
Dependent Claim
20 words
The tuner of claim 20 wherein said nonlinear approximator means is an approximator that utilizes computationally simple compositional sigmoidal mappings.
Claim 30
Dependent Claim
26 words
The tuner of claim 20 further comprising a set of input tuning parameters that indicate characteristics of open loop dynamics of a system to be controlled.
Claim 31
Dependent Claim
153 words
The tuner for producing actual values of internal gain parameters of a controller that is to be used for controlling a process, comprising: preprocessing means, having an input and an output, for processing a set of parameters at the input into a set of transformed parameters at the output; nonlinear approximator means, having an input connected to the output of said preprocessing means and having an output, for operating on said set of transformed parameters to produce normalized actual values of internal gain parameters of said controller; and postprocessing means having a first input connected to the output of said nonlinear approximator means, and having a second input and an output, for scaling said normalized actual values of internal gain parameters into said actual values of internal gain parameters of said controller at the output, wherein said actual values of internal gain parameters are input to the controller for optimally controlling said process.
Claim 32
Dependent Claim
153 words
The tuner for producing actual values of internal gain parameters of a controller that is to be used for controlling a process, comprising: preprocessing means, having an input and an output, for processing a set of parameters at the input into a set of transformed parameters at the output; nonlinear approximator means, having an input connected to the output of said preprocessing means and having an output, for operating on said set of transformed parameters to produce normalized actual values of internal gain parameters of said controller; and postprocessing means having a first input connected to the output of said nonlinear approximator means, and having a second input and an output, for scaling said normalized actual values of internal gain parameters into said actual values of internal gain parameters of said controller at the output, wherein said actual values of internal gain parameters are input to the controller for optimally controlling said process.
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USPTO Annuity & Maintenance Windows
| Annuity Stage | Window Opens | Payment Deadline | Surcharge Period Until | Estimated Fee | Status |
|---|---|---|---|---|---|
| 3.5 Years (Year 4) | 2001-12-07 | 2002-06-08 | 2002-12-08 | $2000.00 | Window Closed / Lapsed |
| 7.5 Years (Year 8) | 2005-12-07 | 2006-06-08 | 2006-12-08 | $3760.00 | Window Closed / Lapsed |
| 11.5 Years (Year 12) | 2009-12-07 | 2010-06-08 | 2010-12-08 | $7700.00 | Window Closed / Lapsed |