Doctoral Defense - Amin Vahidimoghaddam

About the Event

The Department of Mechanical Engineering

Michigan State University

Ph.D. Dissertation Defense

Thursday, December 5th, 2024 at 8:30 AM EST

Engineering Building Room 3540 and via Zoom

Contact Department or Advisor for Zoom Information

ABSTRACT

COMPUTATIONALLY EFFICIENT NONLINEAR OPTIMAL CONTROL USING NEIGHBORING EXTREMAL ADAPTATIONS

By: Amin Vahidimoghaddam

Advisor: Dr. Zhaojian Li

Nonlinear optimal control schemes have achieved remarkable performance in numerous engineering applications; however, they typically require high computational cost, which has limited their use in real-world systems with fast dynamics and/or limited computation power. To address this challenge, neighboring extremal (NE) has been developed as an efficient optimal adaption strategy to adapt a pre-computed nominal control solution to perturbations from the nominal trajectory. The resulting control law is a time-varying feedback gain that can be pre-computed along with the original optimization problem, which makes negligible online computation. This thesis focuses on reducing the computational cost of the nonlinear optimal control problems using the NE in two parts. In Part I, we tackle model-based nonlinear optimal control and propose an extended neighboring extremal (ENE) to handle model uncertainties and reduce computational cost. Nonlinear Model predictive control (NMPC), which explicitly deals with system constraints, is considered as the case study due to its popularity but the ENE can be easily extended to other model-based nonlinear optimal control schemes. In Part II, we address data-driven nonlinear optimal control and introduce a data-enabled neighboring extremal (DeeNE) to remove parametric model requirement and reduce the computational cost. As a pure data-driven optimal and safe controller, data-enabled predictive control (DeePC) makes a transition from the model-based optimal control to a data-driven one such that it seeks an optimal control policy from raw input/output (I/O) data without encoding them into a parametric model and requiring system identification prior to control deployment. The DeePC is considered as the case study, but the DeeNE can be easily extended to other data-driven nonlinear optimal control approaches. We also develop an adaptive DeePC and implement the DeeNE on a real-world arm robot.

Persons with disabilities have the right to request and receive reasonable accommodation. Please call the Department of Mechanical Engineering at 355-5131 at least one day prior to the seminar; requests received after this date will be met when possible.