Feedforward neural network based non-linear dynamic modelling of a TRMS using RPROP algorithm

摘要

Purpose - To develop a non-linear modelling technique for modern air vehicles with an application to a twin rotor multi-input-multi-output system (TRMS) which resembles the dynamics of a helicopter to a certain extent and presents formidable control challenges. Design/methodology/approach - A Non-linear Auto Regressive process with external input (NARX) approach with a feedforward neural work and a resilient propagation (RPROP) algorithm is used to model the system. The RPROP algorithm possesses direct weight update capability without considering the size of the partial derivative. The obtained model is shown to be adequate by carrying out convincing tests such as correlations, cross-validations and prediction based on predicted output and, therefore, is deemed to be reliable. Findings - It is shown that the combination of the feedforward neural networks and RPROP algorithms is very useful and effective in modelling systems with high non-linearity and other complex characteristics. It is always important to attain a model with minimum number of neurons in different layers of the network by overcoming the possibility of getting stuck in the shallow local minimum of error function by using RPROP algorithm. Research limitations/implications - The system is modelled off-line. On-line modelling will be required for real-time control purpose. Practical implications - The non-linear modelling approach presented in this study is shown to be appropriately applicable to model new generations' air vehicles and other complex mechatronic systems such as TRMS. So, the approach will be appealing to industrial applications. Originality/value - This paper addresses the problems of modelling modern sophisticated non-linear systems with complex characteristics and uncertain dynamics.