In this article we present a flatness-based controller that solves the trajectory tracking problem for an unmanned four-rotor aircraft. A simple version of the dynamical system obtained from a suitable coordinate transformation of the Euler–Lagrange model of the system is used. This model, combined with a suitable input saturation nonlinearity, also avoids the inconvenient system singularities. The corresponding tracking error is shown to be globally asymptotically stable but only locally exponentially stable, while the controller maintains the four-rotor system motions inside a feasible region. Numerical simulations are presented to assesses the effectiveness of the proposed control strategy.

Since the fuzzy cerebellar model articulation controller (FCMAC) uses linguistic variables, it is highly intuitive and easily comprehended. Despite the FCMAC's good local generalization capability for approximating nonlinear functions and fast learning, a normal FCMAC requires huge memory, and its dimension increases exponentially with the number of inputs. In order to overcome the memory explosion problem, this paper proposes two types of hierarchical FCMAC (HFCMAC). Another contribution of the paper is that we give stable learning algorithms for these two HFCMACs. Backpropagation-like approach is applied to train each block with a time-varying learning rate, which is obtained by the input-to-state stability technique.