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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

Neural Network Based Control

Dan SimonCleveland State University

neural control architectures
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
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 approach4
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

+

Reference

NeuralController

Neural Model

Error

Step 2

Learning

direct control
Direct Control

Derivative-free training to minimize tracking error e

Learning

e

Reference

+

Neural Controller

System

reference control learning
Reference Control Learning

Learning

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

+

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.

direct model reference adaptive control
Direct Model Reference Adaptive Control

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

+

indirect model reference adaptive control
Indirect Model Reference Adaptive Control

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

Learning

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

+

ANN Model and System ID

e

u

y

Ref.

StandardController

System

+

indirect model reference adaptive control9
Indirect Model Reference Adaptive Control

ANN Model and System ID:

+

uk1

ukm

yk1

ykn

yk

+

Weight adjustment

fixed stabilizing control
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
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