Entropy fuzzy system identification for the heave flight dynamics of a model-scale helicopter

This article studies nonlinear system identification of a small scale and flybar-free unmanned helicopter, the Trex450 chopper, built using commercial off-the-shelf components. We employ the real-time input-output data, obtained from human-controlled flight tests, operating the aircraft under severe ground effects during the vertical flight maneuvers. We highlight the efficacy of the entropy fuzzy system identification method with respect to the performance of several well-known nonlinear system identification techniques (i.e., a Takagi-Sugeno Fuzzy system, an adaptive neuro-fuzzy inference system (ANFIS), and a nonlinear autoregressive with exogenous (NARX) model) as our benchmarks. Our research confirms the benefits of the entropy fuzzy identification technique. Despite being nonlinear, the proposed fuzzy model is relatively simple, transparent, and highly accurate to represent the complex non-linear dynamic behaviors of our unmanned helicopter under severe ground effects. Another major advantage of the proposed system identification technique is its ability to avoid overfitting, an essential requirement in modeling. Overall, the fuzzy system is also capable of achieving a delicate balance between maximizing the accuracy while minimizing the complexity of the acquired model.