Resumen
This paper focuses on robust controller design for a generic helicopter model and terrain avoidance problem via artificial intelligence (AI). The helicopter model is presented as a hybrid system that covers hover and forward dynamics. By defining a set of easily accessible parameters, it can be used to simulate the motion of different helicopter types. A robust control structure based on reinforcement learning is proposed to ensure the system is robust against model parameter uncertainties. The developed generic model can be utilized in many helicopter applications that have been attempted to be solved with sampling-based algorithms or reinforcement learning approaches that take the dynamical constraints into consideration. This study also focuses on the helicopter terrain avoidance problem to illustrate how the model can be useful in these types of applications and provide an artificial intelligence-aided solution to terrain avoidance.