  An important issue in quadcopter control is that an accurate dynamic model of
the system is nonlinear, complex, and costly to obtain. This limits achievable
control performance in practice. Gaussian process (GP) based estimation is an
effective tool to learn unknown dynamics from input/output data. However,
conventional GP-based control methods often ignore the computational cost
associated with accumulating data during the operation of the system and how to
handle forgetting in continuous adaption. In this paper, we present a novel
Dual Gaussian Process (DGP) based model predictive control strategy that
improves the performance of a quadcopter during trajectory tracking. The
bio-inspired DGP structure is a combination of a long-term GP and a short-term
GP, where the long-term GP is used to keep the learned knowledge in memory and
the short-term GP is employed to rapidly compensate unknown dynamics during
online operation. Furthermore, a novel recursive online update strategy for the
short-term GP is proposed to successively improve the learnt model during
online operation in an efficient way. Numerical simulations are used to
demonstrate the effectiveness of the proposed strategy.
