1 research outputs found
Learning in Dynamic Systems and Its Application to Adaptive PID Control
Deep learning using neural networks has revolutionized machine learning and
put artificial intelligence into everyday life. In order to introduce
self-learning to dynamic systems other than neural networks, we extend the
Brandt-Lin learning algorithm of neural networks to a large class of dynamic
systems. This extension is possible because the Brandt-Lin algorithm does not
require a dedicated step to back-propagate the errors in neural networks. To
this end, we first generalize signal-flow graphs so that they can be used to
model nonlinear systems as well as linear systems. We then derive the extended
Brandt-Lin algorithm that can be used to adapt the weights of branches in
generalized signal-flow graphs. We show the applications of the new algorithm
by applying it to adaptive PID control. In particular, we derive a new
adaptation law for PID controllers. We verify the effectiveness of the method
using simulations for linear and nonlinear plants, stable as well as unstable
plants