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Neural Network Based Control

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

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  1. Neural Network Based Control Dan SimonCleveland State University

  2. 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

  3. 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

  4. 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

  5. Direct Control Derivative-free training to minimize tracking error e Learning e Reference + Neural Controller System 

  6. 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.

  7. 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 + 

  8. 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 + 

  9. Indirect Model Reference Adaptive Control ANN Model and System ID: + uk1 … ukm yk1 … ykn … yk  + … Weight adjustment

  10. 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 

  11. 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

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