Radial Basis Function Model-Based Adaptive Model Predictive Control for Trajectory Tracking of a Clapping-Wing Micro Air Vehicle

Abstract

Clapping-wing micro air vehicles (CWMAVs) face many control problems due to their lightweight design and susceptibility to disturbances. This study proposes a radial basis function (RBF) model-based adaptive model predictive control (AMPC) for trajectory tracking to solve the control problem in the presence of internal uncertainties and external disturbances. First, a method for calculating the desired attitude is given. Second, a control optimization model is used by adjusting future control inputs to minimize the difference between the future and desired outputs. Third, a nonlinear predictive linearization is used to transform the nonlinear optimization model into a quadratic programming problem. Two observers are introduced to estimate the internal uncertainties and the external disturbances. Finally, the control assignment method is combined with the trajectory tracking method to obtain the design variables of actuators (flapping frequency, pitch angle, and yaw angle). Validation studies were performed to verify the effectiveness and accuracy in the presence of constant and time-dependent disturbances. The comparison of RAMPC with classical methods shows that RAMPC has better control performance with smaller errors. The proposed RAMPC framework can be well used for CWMAV control and provides an excellent basis for accurate navigation and autonomous obstacle avoidance.