Issues in the application of neural networks for tracking based on inverse control

Abstract

Since 1990 a substantial amount of research has been reported in the literature concerning the identification and control of nonlinear dynamical systems using artificial neural networks. Various methods for tracking based on inverse control have been proposed, and constitute one of the main thrusts of this research effort. A significant part of this work has been heuristic in nature, and the conclusions drawn are generally justified using computer simulations. The general success of the simulation studies has also resulted in the increased use of artificial neural networks as controllers in industrial applications. As a result, there is a real need for a better understanding of the questions and problems that can arise in such contexts. This paper attempts to provide the theoretical foundations as well as insights that are essential for the efficient design of neural network controllers based on inverse control.

Fuzzy Feedback Linearizing Controller and Its Equivalence With the Fuzzy Nonlinear Internal Model Control Structure

“…In many cases, the control signals are computed by numerically inverting the neural network and/or the fuzzy model, which induces the restrictions mentioned previously in implementing the corresponding control strategies. One way of avoiding the problems associated with the use of iterative inversion algorithms consists in suppressing the on-line inversion by applying an inverse control learning method (Cabrera and Narendra, 1999;Park and Han, 2000;Rivals and Personnaz, 2000). In that case, the inverse system is trained by minimizing the deviation between the controlled system output and the desired one by means of adaptive techniques.…”

“…Inverting systems is an important issue in engineering applications, especially in linear and nonlinear control problems (Baoming et al, 2002;Boukezzoula et al, 2001;Boukezzoula et al, 2003;Boukezzoula et al, 2006;Cabrera and Narendra, 1999;Devanathan et al, 2000;Li and Deng, 2006;Rivals and Personnaz, 2000). The underlying principle of inverse control is based on the following remark: Since a plant model can be viewed as a mapping from control inputs to future outputs according to the process history, one can use the inverse mapping from the desired outputs to the inputs as a design control procedure.…”

Model Inversion using Extended Gradual Intervals Arithmetic

“…Inverting systems is an important issue in engineering, especially in control and diagnostic problems [4], [5], [6]. However, in many applications the model of a system may contain parameters whose values are not precisely known.…”

Neural Network Control of Discrete-Time Non-Affine Mimo Systems With Disturbances

“…The universal approximation ability of neural networks makes it one of the effective tool in nonlinear system identification and control (Cabrera and Narendra, 1999;Narendra and Parthasarathy, 1990;Polycarpou, 1996). Most often used neural networks include Radial Basis Function (RBF) neural networks (Lewis et al, 1999;Ge et al, 2001), HONNs (Kosmatopoulos et al, 1995) and Multi-layer Neural Networks (MNNs) (Lewis et al, 1999;Ge et al, 2001).…”