Neural Network Based Control

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# Neural Network Based Control - PowerPoint PPT Presentation

Neural Network Based Control. Dan Simon Cleveland State University. Neural Control Architectures. Inverse model approach Direct control (derivative-free training) Reference control learning Direct model reference adaptive control Indirect model reference adaptive control

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### Neural Network Based Control

Dan SimonCleveland State University

Neural Control Architectures
• Inverse model approach
• Direct control (derivative-free training)
• Reference control learning
• Direct model reference adaptive control
• Indirect model reference adaptive control
• Fixed stabilizing control
Inverse Model Approach

Two-step approach:

• Train a neural dynamic system model
• Train an inverse model to be the controller

System

+

First we train a neural network to model the dynamic system.

Error

Input

NeuralModel

Step 1

Learning

Inverse Model Approach
• Two neural nets in series  one neural network
• Backprop: Compute derivative of error w/r to controller weights
• Backprop: Change controller weights to minimize tracking error
• Although we backpropagate derivatives through the neural model, we do not modify the weights of the neural model

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Reference

NeuralController

Neural Model

Error

Step 2

Learning

Direct Control

Derivative-free training to minimize tracking error e

Learning

e

Reference

+

Neural Controller

System

Reference Control Learning

Learning

The ANN learns to mimic the optimal controller. Then the ANN can replace the optimal controller.

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Neural Controller

e

Ref.

OptimalController

System

+

We already have an optimal controller, so why would we want to train the ANN? Because ANN’s often have the built-in ability to generalize. So the ANN may be more robust than the optimal controller.

Model Reference

Use derivative-free optimization to adjust the controller parameters so the closed loop system behaves like the model (desired rise time, overshoot, etc.)

+

Learning

e

Ref.

NeuralController

System

+

System: yk+a1yk1+…+anykn = b1uk1+…+bmukm

Learning

System structure is given.ANN estimates parameters.Parameters used in controller.

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ANN Model and System ID

e

u

y

Ref.

StandardController

System

+

ANN Model and System ID:

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uk1

ukm

yk1

ykn

yk

+

Fixed Stabilizing Control

The standard controller stabilizes the system

The ANN (inverse model) adjusts its weights until the standard controller output is zero, which means that tracking error e = 0.

The ANN gradually “takes over” the control function.

InverseModel

Learning

e

Reference

+

Standard Controller

+

System

References
• M. Hagan and H. Demuth, Neural Networks for Control
• K. Astrom and B. Wittenmark, Adaptive Control
• W. Zhang, System Identification Based on Generalized ADALINE Neural Network
• B. Kosko, Neural Networks and Fuzzy Systems