Adaptive Control of 2-DOF Helicopter with Full State Constraints and Input Saturation

This paper investigates an adaptive controller for a 2-degrees of freedom helicopter with full state constraints and input saturation affected by time varying external disturbances. The unknown nonlinear functions in the helicopter dynamics are estimated using a radial basis function neural network (RBFNN) for controller design. To incorporate the constraints on the states, a barrier Lyapunov function is utilized. Further, Lyapunov stability analysis is performed to ascertain the boundedness of all the signals in the closed loop control system of the helicopter. A disturbance observer is used to estimate the uncertain disturbances and approximation errors of the NN. The ability of the system to track desired trajectories for the pitch and yaw angles demonstrate the effectiveness of the proposed controller to incorporate full state constraints and saturation of the control inputs.