Nonaffine Helicopter Control Design and Implementation Based on a Robust Explicit Nonlinear Model Predictive Control

Control design for a helicopter is a challenging problem because of its nonaffine inputs, its underactuated characteristics, and highly coupled dynamics. To solve a control problem of the helicopter under model uncertainties and disturbance present environments, an explicit nonlinear model predictive control (ENMPC), a dynamic inversion (DI), and an extended high-gain observer (EHGO) are combined in a multi-time-scale fashion. The multi-time-scale scaled structure and the ENMPC provide the framework of the control design, the DI deals with nonaffine control inputs, and the EHGO estimates the unmeasured system states and uncertainties. The proposed control design is discretized to be implemented on a small-scale helicopter. The successful outdoor experiments with the proposed control implemented on autopilot hardware demonstrate the validity of our approach in the presence of model uncertainties and wind disturbances.

I. Introduction

With the advance of computation power of embedded computers, battery capacities, and motor performances, unmanned aerial vehicle (UAV) industries are growing and applied rapidly in everyday life. Especially, helicopters have their superiority over quadrotors on agile movement and the faster response despite its unstable dynamics [1]–[3]. The control design for high-performance autonomous helicopters is challenging. This is because helicopters are underactuated mechanical and highly nonlinear systems. In addition, the exogenous disturbances and model uncertainties make the control design of a helicopter more difficult [4], [5].