MORE | Summer 2023
Ensuring Feasibility and Stability of Model Predictive Control in Nonlinear Systems with Non-Overshooting Constraints
This research addresses the feasibility and stability challenges of Model Predictive Control (MPC) in highly nonlinear systems, focusing on the use of terminal constraints and cost. Through a comprehensive literature review, it explores various approaches to guaranteeing MPC stability and feasibility. The study includes theoretical analysis and practical simulations using MATLAB and Simulink. The findings reveal that while equilibrium terminal constraints can lead to chattering, regional terminal sets with Lyapunov function terminal costs offer improved stability without the need for controllability at equilibrium points. This research contributes valuable insights into enhancing MPC’s effectiveness for nonlinear systems, promoting safer and more reliable control strategies.
Robotics and autonomous systems
Hometown: Tempe, Arizona, United States
Graduation date: Spring 2024