Paper: Intelligent Control Systems Methods – Neural Networks

Intelligent Control Systems Methods – Neural Networks

Prof Ka C Cheok Dept of Electrical and Systems Engineering Oakland University Rochester MI 48309

Summer Technical Workshop Series NDIA 2nd Annual Intelligent Vehicle Systems Symposium Grand Traverse Resort & Spa Travers City, MI June 3-5, 2002

http://lossofgenerality.devzing.com/files/Intellgnt%20Cntr_cheok_s1.pdf

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Paper: Neural Network For Control

Neural Networks for Control

Martin T. Hagan School of Electrical & Computer Engineering Oklahoma State University mhagan@ieee.org

Howard B. Demuth Electrical Engineering Department University of Idaho hdemuth@uidaho.edu

Abstract

The purpose of this tutorial is to provide a quick overview of neural networks and to explain how they can be used in control systems. We introduce the multilayer perceptron neural network and describe how it can be used for function approximation. The backpropagation algorithm (including its variations) is the principal procedure for training multilayer perceptrons; it is briefly described here. Care must be taken, when training perceptron networks, to en- sure that they do not overfit the training data and then fail to generalize well in new situations. Several techniques for improving generalization are dis- cused. The tutorial also presents several control ar- chitectures, such as model reference adaptive control, model predictive control, and internal model control, in which multilayer perceptron neural net- works can be used as basic building blocks.

http://lossofgenerality.devzing.com/files/NeuralNetworkForControl.pdf

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Paper: Quad Copter Mechanics and NN Control

ROBUST NEURAL NETWORK CONTROL OF A QUADROTOR HELICOPTER

C. Nico,C.J.B. Macnab, A. Ramirez-Serrano

Schulich School of Engineering, University of Calgary  Department of Electrical and Computer Engineering  Department of Mechanical and Manufacturing Engineering 2500 University Dr. NW, Calgary, Alberta, Canada, T2N 1N2

ABSTRACT

This paper proposes a new adaptive neural network control to stabilize a quadrotor helicopter against modeling error and considerable wind disturbance. The new method is compared to both deadzone and e-modification adaptive techniques and through simulation demonstrates a clear improvement in terms of achieving a desired attitude and reducing weight drift.

http://lossofgenerality.devzing.com/files/adaptive_quadcopter.pdf

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NN Control Example: Takeoff in Wind

Below is an example of learnt flying i.e. the algorithm for the flight was not programmed into the UAV, rather it learned from off-line experiments how to control its engines for vertical take off.

In order to make the matters more challenging, a strong gust was introduced with high rpm for the motors to make the quad-copter highly unstable.

The off-line training did not include any wind and the algoritm for take off had no explicit coding for the wind. However as you can see the quad-copter fought against the wind with any a priori programming:

http://lossofgenerality.devzing.com/blog/2012/03/28/nn-control-takeoff-in-wind/

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