Fast nonlinear model predictive control via partial enumeration

In this work, we consider the problem of fast, accurate control of a robot with constrained dynamics. We present a new nonlinear model predictive control (MPC) technique, Nonlinear Partial Enumeration (NPE), that combines online and offline computation in a nonlinear version of the partial enumeration method for MPC, thereby dramatically decreasing the compute time per control iteration. We apply NPE to the problem of MAV flight and demonstrate through a set of simulation trials that NPE outperforms other fast control methodologies during aggressive motion and enables the system to learn a reusable set of local feedback controllers that enable more efficient operation over time.

Authors

• Vishnu R. Desaraju
• Nathan Michael

Keywords

• Robots
• Feedback control
• Predictive control
• Standards
• Quadratic programming
• Computational modeling
• Nonlinear Model
• Model Predictive Control
• Nonlinear Control
• Nonlinear Model Predictive Control
• Partial Enumeration
• Local Control
• Micro Air Vehicles
• Optimal Control
• Nonlinear Systems
• Nonlinear Dynamics
• Nonlinear Programming
• Constant Term
• Tracking Error
• Learning Control
• Roll Angle
• Local Approximation
• Slack Variables
• KKT Conditions
• Intermediate Control
• Online Optimization
• Nominal State
• Sequential Quadratic Programming
• Active Constraints
• Feedback Control Strategy
• Order Of Milliseconds
• Regions Of The State Space
• Valid Region
• Angular Velocity