A Computationally Efficient Learning-Based Model Predictive Control for Multirotors under Aerodynamic Disturbances

Babak Akbari,Melissa Greeff

CoRR(2024)

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Abstract
Neglecting complex aerodynamic effects hinders high-speed yet high-precision multirotor autonomy. In this paper, we present a computationally efficient learning-based model predictive controller that simultaneously optimizes a trajectory that can be tracked within the physical limits (on thrust and orientation) of the multirotor system despite unknown aerodynamic forces and adapts the control input. To do this, we leverage the well-known differential flatness property of multirotors, which allows us to transform their nonlinear dynamics into a linear model. The main limitation of current flatness-based planning and control approaches is that they often neglect dynamic feasibility. This is because these constraints are nonlinear as a result of the mapping between the input, i.e., multirotor thrust, and the flat state. In our approach, we learn a novel representation of the drag forces by learning the mapping from the flat state to the multirotor thrust vector (in a world frame) as a Gaussian Process (GP). Our proposed approach leverages the properties of GPs to develop a convex optimal controller that can be iteratively solved as a second-order cone program (SOCP). In simulation experiments, our proposed approach outperforms related model predictive controllers that do not account for aerodynamic effects on trajectory feasibility, leading to a reduction of up to 55
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Key words
Model Predictive Control,Aerodynamic Disturbances,Linear Model,Optimal Control,Control Input,Nonlinear Dynamics,Drag Force,Gaussian Process,Convex Optimization,Tracking Error,Aerodynamic Forces,Vector In Frame,World Frame,Second-order Cone,Second-order Cone Programming,Flat State,Time Step,Optimization Problem,Linear Function,Posterior Probability,Cone Constraints,Feasibility Constraints,Planning Algorithm,Mediator Variable,Optimal Control Problem,Unknown Disturbances,Prediction Horizon,Trajectory Optimization,Linear Control,Gaussian Process Model
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