Observer-Based Iterative Fuzzy and Neural Network Model Inversion for Measurement and Control Applications

Nowadays model based techniques play very important role in solving measurement and control problems. Recently for representing nonlinear systems fuzzy and neural network (NN) models became very popular. For evaluating measurement data and for controller design also the inverse models are of considerable interest. In this paper, different observer based techniques to perform fuzzy and neural network model inversion are presented. The methods are based on solving a nonlinear equation derived from the multiple-input single-output (MISO) forward fuzzy model simple by interchanging the role of the output and one of the inputs. The utilization of the inverse model can be either a direct compensation of some measurement nonlinearities or a controller mechanism for nonlinear plants. For discrete-time inputs the technique provides good performance if the iterative inversion is fast enough compared to system variations, i.e., the iteration is convergent within the sampling period applied. The proposed method can be considered also as a simple nonlinear state observer which reconstructs the selected input of the forward (fuzzy or NN) model from its output using an appropriate strategy and a copy of the fuzzy or neural network model itself. Improved performance can be obtained by introducing genetic algorithms in the prediction-correction mechanism. Although, the overall performance of the suggested technique is highly influenced by the nature of the non-linearity and the actual prediction-correction mechanism applied, it can also be shown that using this observer concept completely inverted models can be derived. The inversion can be extended towards anytime modes of operation, as well, providing short response time and flexibility during temporal loss of computational power and/or time.