Controlling an unmanned aerial vehicle by re-training a sub-optimal controller

A method of controlling an unmanned aerial vehicle, comprising:

• defining a nonlinear dynamic control system by a set of equations that include a state vector z and one or more control inputs u, the nonlinear dynamic control system comprising a physical plant of the unmanned aerial vehicle, the physical plant comprising three motors and at least one other motor that all provide thrust for the unmanned aerial vehicle;
• via a machine learning method, deriving a sub-optimal controller that stabilizes the nonlinear dynamic control system at an equilibrium point;
• and
• retraining the sub-optimal controller to be a stabilizing controller for the nonlinear dynamic control system under operating conditions of the physical plant, wherein retraining the sub-optimal controller comprises a model predictive control (MPC) approach in which a parameterized, state-dependent control map is learned by solving a sequence of finite horizon optimal control problems;
• and
• using the stabilizing controller to provide closed loop control of the three motors when the unmanned aerial vehicle is affected by the at least one other motor losing thrust.

A system comprising:

• an unmanned aerial vehicle comprising a physical plant, the physical plant comprising three motors and at least one other motor that all provide thrust for the unmanned aerial vehicle;
• and
• a computer comprising a memory coupled to a processor, the memory comprising instructions that cause the processor to:
• define a non-linear dynamic control system by a set of equations that include a state vector z of the unmanned aerial vehicle and one or more control inputs u to the physical plant of the unmanned aerial vehicle;
• via a machine learning method, derive a sub-optimal controller that stabilizes the unmanned aerial vehicle at an equilibrium point, the machine learning method optimizing controller parameters by manipulating spectral properties of a Jacobian of the state vector at the equilibrium point;
• and
• retrain the sub-optimal controller to be a stabilizing controller for the unmanned aerial vehicle under operating conditions of the physical plant of the unmanned aerial vehicle, wherein the stabilizing controller is transferred to the unmanned aerial vehicle, the stabilizing controller used by the unmanned aerial vehicle to provide closed loop control of the three motors when the unmanned aerial vehicle is affected by the at least one other motor losing thrust.