Zongli Lin

Bio: Zongli Lin is an academic researcher from University of Virginia. The author has contributed to research in topics: Linear system & Nonlinear system. The author has an hindex of 67, co-authored 524 publications receiving 19644 citations. Previous affiliations of Zongli Lin include State University of New York System & Washington State University.

Control Systems with Actuator Saturation: Analysis and Design

Abstract: From the Publisher: "Control Systems with Actuator Saturation Analysis and Design examines the problem of actuator saturation depth. The overall approach takes into account the saturation nonlinearities at the outset of the control design. In the case that a control law is designed a priori to meet either the performance or stability requirement, it analyzes the closed loop system under actuator saturation systematically and redesigns the controller in such a way that the performance is retained while stability is improved. It also presents some related results on systems with state saturation or sensor saturation." "This book is a resource for professionals, researchers, practitioners, graduate students in control, electrical, and mechanical engineering, and all scientists and engineers interested in control systems with actuator saturation. Some first-year graduate courses in linear systems and multivariable control or some background in nonlinear control systems would greatly facilitate the reading of this book."--BOOK JACKET.

Flocking of Multi-Agents With a Virtual Leader

Abstract: All agents being informed and the virtual leader traveling at a constant velocity are the two critical assumptions seen in the recent literature on flocking in multi-agent systems. Under these assumptions, Olfati-Saber in a recent IEEE Transactions on Automatic Control paper proposed a flocking algorithm which by incorporating a navigational feedback enables a group of agents to track a virtual leader. This paper revisits the problem of multi-agent flocking in the absence of the above two assumptions. We first show that, even when only a fraction of agents are informed, the Olfati-Saber flocking algorithm still enables all the informed agents to move with the desired constant velocity, and an uninformed agent to also move with the same desired velocity if it can be influenced by the informed agents from time to time during the evolution. Numerical simulation demonstrates that a very small group of the informed agents can cause most of the agents to move with the desired velocity and the larger the informed group is the bigger portion of agents will move with the desired velocity. In the situation where the virtual leader travels with a varying velocity, we propose modification to the Olfati-Saber algorithm and show that the resulting algorithm enables the asymptotic tracking of the virtual leader. That is, the position and velocity of the center of mass of all agents will converge exponentially to those of the virtual leader. The convergent rate is also given.