Alexandria Digital Research Library

Simultaneous nonlinear model predictive control and state estimation : theory and applications

Copp, David A.
Degree Grantor:
University of California, Santa Barbara. Mechanical Engineering
Degree Supervisor:
Joao Hespanha
Place of Publication:
[Santa Barbara, Calif.]
University of California, Santa Barbara
Creation Date:
Issued Date:
Engineering, Electrical engineering, and Mechanical engineering
Adaptation and Learning
Unmanned Aerial Vehicles
Moving Horizon Estimation
Numerical Optimization
Artificial Pancreas
Model Predictive Control
Dissertations, Academic and Online resources
Ph.D.--University of California, Santa Barbara, 2016

As computational power increases, online optimization is becoming a ubiquitous approach for solving control and estimation problems in both academia and industry. This widespread popularity of online optimization techniques is largely due to their abilities to solve complex problems in real time and to explicitly accommodate hard constraints. In this dissertation, we discuss an especially popular online optimization control technique called model predictive control (MPC). Specifically, we present a novel output-feedback approach to nonlinear MPC, which combines the problems of state estimation and control into a single min-max optimization. In this way, the control and estimation problems are solved simultaneously providing an output-feedback controller that is robust to worst-case system disturbances and noise. This min-max optimization is subject to the nonlinear system dynamics as well as constraints that come from practical considerations such as actuator limits. Furthermore, we introduce a novel primal-dual interior-point method that can be used to efficiently solve the min-max optimization problem numerically and present several examples showing that the method succeeds even for severely nonlinear and non-convex problems.

Unlike other output-feedback nonlinear optimal control approaches that solve the estimation and control problems separately, this combined estimation and control approach facilitates straightforward analysis of the resulting constrained, nonlinear, closed-loop system and yields improved performance over other standard approaches. Under appropriate assumptions that encode controllability and observability of the nonlinear process to be controlled, we show that this approach ensures that the state of the closed-loop system remains bounded. Finally, we investigate the use of this approach in several applications including the coordination of multiple unmanned aerial vehicles for vision-based target tracking of a moving ground vehicle and feedback control of an artificial pancreas system for the treatment of Type 1 Diabetes. We discuss why this novel combined control and estimation approach is especially beneficial for these applications and show promising simulation results for the eventual implementation of this approach in real-life scenarios.

Physical Description:
1 online resource (230 pages)
UCSB electronic theses and dissertations
Catalog System Number:
Inc.icon only.dark In Copyright
Copyright Holder:
David Copp
File Description
Access: Public access
Copp_ucsb_0035D_13301.pdf pdf (Portable Document Format)