Learning-based fast nonlinear model predictive control for custom-made 3D printed ground and aerial robots
In this work, our goal is to use an online learning-based nonlinear model predictive control (NMPC) for systems with uncertain and/or time-varying parameters. We have deployed it for two robotic applications in real-time: an agricultural off-road ground vehicle and an aerial robotic system, namely a tilt-rotor tricopter unmanned aerial vehicle. Nonlinear moving horizon estimation (NMHE) is used to estimate the traction parameters in the former and the mass parameter in the latter. Thanks to its learning capability, NMHE makes the proposed framework adaptive – and therefore robust – to time-varying operational conditions. The experimental results for the trajectory tracking problem of the unmanned ground and aerial vehicles demonstrate a robust learning controller that provides an accurate tracking. The experimental results also show that the proposed framework provides a fast and computationally efficient methodology which can easily be implemented in ground and aerial robotic applications with reasonable computation power, where working conditions are time-varying and the modeling of the system is tedious.