Parameterized neurocontrollers
First Claim
1. A controller of a process which accepts modification of its behavior through input signals representative of parameters that are members of a set of parameters including:
- control parameters pc, process parameters pp, and disturbance parameters pd (which may collectively be called P) wherein said controller comprises a neural network;
wherein the neural network is trained to mimic an existing controller which receives inputs from the set of all P inputs or any subset thereof (except only the subset including only P, I and D inputs), and xp, y, yr, u and all algebraic, differential and integral operators of these xp, y, yr and u inputs, and has an output in a closed loop use, said training occurring by;
collecting said P parameters, said xp, yr, y, u and any other of said inputs as data, andusing said collected data as training data in a learning program which modifies the neural network in a training phase at least until an output from said neural network is similar or identical to the acceptable output generated by said existing controller;
wherein;
xp is dynamic state variables of the process,y is a process output signal,yr is a reference input signal, andu is a control adjustment signal and,coupling said modified neural network to a second process similar to a first process previously controlled by the existing controller; and
applying said neural network to the second process using inputs that the existing controller would have used to control the first process so that the neural network controls the second process.
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Abstract
A controller based on a neural network whose output is responsive to input signals that represent user or designer defined control system parameters which may include process parameters, control parameters and/or disturbance parameters. The neural network can be "trained" to mimic an existing controller which may or not receive inputs of control system parameters. The trained neural network controller may have advantages of faster execution and reduced code size. The neural network can also be trained to result in a nonlinear controller that is more powerful than an existing controller.
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Citations
9 Claims
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1. A controller of a process which accepts modification of its behavior through input signals representative of parameters that are members of a set of parameters including:
- control parameters pc, process parameters pp, and disturbance parameters pd (which may collectively be called P) wherein said controller comprises a neural network;
wherein the neural network is trained to mimic an existing controller which receives inputs from the set of all P inputs or any subset thereof (except only the subset including only P, I and D inputs), and xp, y, yr, u and all algebraic, differential and integral operators of these xp, y, yr and u inputs, and has an output in a closed loop use, said training occurring by; collecting said P parameters, said xp, yr, y, u and any other of said inputs as data, and using said collected data as training data in a learning program which modifies the neural network in a training phase at least until an output from said neural network is similar or identical to the acceptable output generated by said existing controller;
wherein;xp is dynamic state variables of the process, y is a process output signal, yr is a reference input signal, and u is a control adjustment signal and, coupling said modified neural network to a second process similar to a first process previously controlled by the existing controller; and applying said neural network to the second process using inputs that the existing controller would have used to control the first process so that the neural network controls the second process. - View Dependent Claims (2, 3, 4, 5, 6, 7)
- control parameters pc, process parameters pp, and disturbance parameters pd (which may collectively be called P) wherein said controller comprises a neural network;
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8. A controller of a process which accepts modification of its behavior through input signals representative of parameters that are members of a set of parameters including:
- control parameters pc, process parameters pp, and disturbance parameters pd (which may collectively be called P) wherein said controller comprises a neural network;
wherein said inputs received by said controller are from the set of inputs including all P inputs or any subset thereof (except only P,I,D inputs), and xp, y, yr, u and all algebraic, differential and integral operators of these xp, y, yr and u inputs; wherein training is done off-line over a variety of process models, and further comprising; an input controller processor means for passing data to other controllers and wherein said input controller processor means further comprises an output controller processor that reviews the outputs of other controllers and determines which of those is used as input to the process, wherein; xp is dynamic state variables of the process, y is a process output signal, yr is a reference input signal, and u is a control adjustment signal; and wherein the output controller processor provides an output signal that is used as input to the process as determined by said output controller processor.
- control parameters pc, process parameters pp, and disturbance parameters pd (which may collectively be called P) wherein said controller comprises a neural network;
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9. A controller of a process which accepts modification of its behavior through input signals representative of parameters that are members of a set of parameters including:
- control parameters pc, process parameters pp, and disturbance parameters pd (which may collectively be called P) wherein said controller comprises a neural network;
wherein said inputs received by said controller are from the set of inputs including all P inputs or any subset thereof (except only P,I,D inputs), and xp, y, yr, u and all algebraic, differential and integral operators of these xp, y, yr and u inputs; wherein said controller comprises an input controller processor and a plurality of other controllers, at least one of which other controllers is a neural network; and
wherein;xp is dynamic state variables of the process, y is a process output signal, yr is a reference input signal, and u is a control adjustment signal; and wherein said controller provides a control signal to at least one actuator associated with the process so that a detectable physical change occurs in the characteristics of the process.
- control parameters pc, process parameters pp, and disturbance parameters pd (which may collectively be called P) wherein said controller comprises a neural network;
Specification