Adaptive neural network control of a 2-DOF helicopter system considering input constraints and global prescribed performance

In this study, an adaptive neural network (NN) control is proposed for nonlinear two-degree-of-freedom (2-DOF) helicopter systems considering the input constraints and global prescribed performance. First, radial basis function NN (RBFNN) is employed to estimate the unknown dynamics of the helicopter system. Second, a smooth nonaffine function is exploited to approximate and address nonlinear constraint functions. Subsequently, a new prescribed function is proposed, and an original constrained error is transformed into an equivalent unconstrained error using the error transformation and barrier function transformation methods. The analysis of the established Lyapunov function proves that the controlled system is globally uniformly bounded. Finally, the simulation and experimental results on a constructed Quanser’s test platform verify the rationality and feasibility of the proposed control.