Nonlinear identification of a small scale unmanned helicopter using optimized NARX network with multiobjective differential evolution

The need for a high fidelity model for design, analysis and implementation of an unmanned helicopter system (UHS) in various emerging civil applications cannot be underestimated. However, going by a first principle approach based on physical laws governing the dynamics of the system, this task is noted to be highly challenging due to the complex nonlinear characteristics of the helicopter system. On the other hand, the problem of determining network architecture for optimal/sub-optimal performances has been one of the major challenges in the use of the nonparametric approach based on Nonlinear AutoRegressive with eXogenous inputs Network (NARX-network). The performance of the NARX network in terms of complexity and accuracy is largely dependent on the network architecture. The current approach in the literature has been largely based on trial and error, while most of the reported optimization approaches have limited the domain of the problem to a single objective problem. This study proposes a hybrid of conventional back propagation training algorithm for the NARX network and multiobjective differential evolution (MODE) algorithm for identification of a nonlinear model of an unmanned small scale helicopter from experimental flight data. The proposed hybrid algorithm was able to produce models with Pareto-optimal compromise between the design objectives. The performance of the proposed optimized model is benchmarked with one of the previously reported architectures for a similar system. The optimized model outperformed the previous model architecture with up to 55% performance improvement. Apart from the effectiveness of the optimized model, the proposed design algorithm is expected to facilitate timely development of the nonparametric model of the helicopter system.