Learning Nonlinear Model Predictive Controllers and Virtual Sensors with Koopman Operators

Model-Based Control Using Koopman Operators

This paper explores the application of Koopman operator theory to the control of robotic systems. The operator is introduced as a method to generate data-driven models that have utility for model-based control methods. We then motivate the use of the Koopman operator towards augmenting modelbased control. Specifically, we illustrate how the operator can be used to obtain a linearizable data-dri...

Weighted Frobenius-Perron and Koopman operators

Nonlinear Model Predictive Swing-Up and Stabilizing Sliding Mode Controllers

In this paper, a nonlinear model predictive swing-up and stabilizing sliding controller is proposed for an inverted pendulum-cart system. In the swing up phase, the nonlinear model predictive control is formulated as a nonlinear programming problem with energy based objective function. By solving this problem at each sampling instant, a sequence of control inputs that optimize the nonlinear obj...

Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems

The Koopman operator has recently garnered much attention for its value in dynamical systems analysis and data-driven model discovery. However, its application has been hindered by the computational complexity of extended dynamic mode decomposition; this requires a combinatorially large basis set to adequately describe many nonlinear systems of interest, e.g. cyber-physical infrastructure syste...

Discontinuous Feedback Stabilization Using Nonlinear Model Predictive Controllers

We propose a Model Predictive Control (MPC) framework to generate feedback controls for time-varying nonlinear systems with input constraints. One of the main features of this framework is to allow the feedback laws to be discontinuous and thereby enlarge the class of nonlinear systems that can be stabilized by continuous-time MPC. We consider a continuous-time MPC framework and perform a conti...