Closed-loop control using a backpropagation algorithm: a practicable approach for energy loss minimisation in electrical drives.

In general, optimal controls are computed off line and subsequently applied in real time
but this approach is impracticable due to lack of robustness with respect to the plant modelling errors
and unknown external disturbances. Closed loop versions of these optimal controls could circumvent
this problem but are only available in the analytical form for very simple cases, not including
minimisation of frictional energy loss in motion control systems, which is the aim of the research
programme. The approach suggested by Matieni and Dodds (2009), however, overcomes this
obstacle by training an artificial neural network (ANN) to reproduce the optimal control values
computed off-line from given states and reference inputs, thereby yielding a closed loop solution. The
purpose of this paper is to present the results of an initial simulation experiment to assess the
capability of a Multilayered Perceptron (MLP), in the backpropagation mode, to perform a direct state
feedback function, which, to the authors‘ knowledge, is new. A known linear state feedback
controller for a double integrator plant is used for this purpose. The control law is used to train the
MLP. Then a simulation of the closed loop system formed using this MLP is compared with a
simulation of the known linear state feedback control system. The results show that the closed loop
step response with the MLP closely follows that of the conventional system.

Citation:
Matieni, X., Dodds, S.J. and Lee, S.W. (2010) ‘Closed-loop control using a backpropagation algorithm: a practicable approach for energy loss minimisation in electrical drives.’, Proceedings of Advances in Computing and Technology, (AC&T) The School of Computing and Technology 5th Annual Conference, University of East London, pp.72-78..