Sam Coogan received the B.S. degree in Electrical Engineering from Georgia Tech and the M.S. and Ph.D. degrees in Electrical Engineering from the University of California, Berkeley. In 2015, he was a postdoctoral research engineer at Sensys Networks, Inc., and in 2012, he was a research intern at NASA's Jet Propulsion Lab. Before joining Georgia Tech in 2017, he was an assistant professor in the Electrical Engineering department at UCLA from 2015–2017.
Dr. Coogan's research is in the area of dynamical systems and autonomy and focuses on developing scalable tools for verification and control of networked, cyber-physical systems. He is especially interested in applying these tools to create efficient, intelligent, and autonomous transportation systems. His research contributes to and draws from domains including control theory, nonlinear and hybrid systems theory, formal methods, learning in probabilistic systems, and optimization.
Coogan received a Young Investigator Award from the Air Force Office of Scientific Research in 2018, a CAREER Award from the National Science Foundation in 2018, the IEEE Transactions on Control of Network Systems Outstanding Paper Award in 2017, the best student paper award at the 2015 Hybrid Systems: Computation and Control conference, the Eli Jury Award from UC Berkeley EECS in 2016 for "outstanding achievement in the area of systems, communications, control, or signal processing," and the Leon O. Chua Award from UC Berkeley EECS in 2014 for "outstanding achievement in an area of nonlinear science."
Areas of Expertise (5)
Selected Accomplishments (3)
Eli Jury Award from EECS Department, UC Berkeley for “outstanding achievement in the area of systems, communications, control, or signal processing”
Best Student Paper, Conference on Hybrid Systems: Computation and Control
Leon O. Chua Award from EECS Department, UC Berkeley for “outstanding achievement in an area of nonlinear science”
University of California, Berkeley: Ph.D., Electrical Engineering 2015
University of California, Berkeley: M.S., Electrical Engineering 2012
Georgia Institute of Technology: B.S., Electrical Engineering 2010
- Institute for Robotics and Intelligent Machines
- Supply Chain and Logistics Institute
Selected Articles (5)
Mohit Srinivasan, Samuel Coogan
2019 In this paper, we propose a framework for the control of mobile robots subject to temporal logic specifications using barrier functions. Complex task specifications can be conveniently encoded using linear temporal logic (LTL). In particular, we consider a fragment of LTL which encompasses a large class of motion planning specifications for a robotic system. Control barrier functions (CBFs) have recently emerged as a convenient tool to guarantee reachability and safety for a system. In addition, they can be encoded as affine constraints in a quadratic program (QP). In the case of complex system specifications, we show that following QP based methods in existing literature can lead to infeasibility and hence we provide a method of composition of multiple barrier functions in order to mitigate infeasibility. A scheme to prioritize different barrier functions which allows the user to encode the notion of priority based control, is also introduced. We prove that the resulting system trajectory synthesized by the proposed controller satisfies the given specification. Robotic simulation and experimental results are provided in addition to the theoretical framework.
Matthew Abate, Eric Feron, Samuel Coogan
2019 This paper introduces the safety controller architecture as a runtime assurance mechanism for system specifications expressed as safety properties in Linear Temporal Logic (LTL). The safety controller has three fundamental components: a performance controller, a backup controller, and an assurance mechanism. The assurance mechanism uses a monitor, constructed as a finite state machine (FSM), to analyze a suggested performance control input and search for system trajectories that are bad prefixes of the system specification. A fault flag from the assurance mechanism denotes a potentially dangerous future system state and triggers a sequence of inputs that is guaranteed to keep the system safe for all time. A case study is presented which details the construction and implementation of a safety controller on a non-deterministic cyber-physical system.
2019 Monotone systems preserve a partial ordering of states along system trajectories and are often amenable to separable Lyapunov functions that are either the sum or the maximum of a collection of functions of a scalar argument. In this paper, we consider constructing separable Lyapunov functions for monotone systems that are also contractive, that is, the distance between any pair of trajectories exponentially decreases. We assume the system evolves in a forward invariant rectangular domain, and the distance between trajectories is defined in terms of a Finsler type metric characterized by a possibly state-dependent norm. When this norm is a weighted one-norm, we obtain conditions which lead to sum-separable Lyapunov functions, and when this norm is a weighted infinity-norm, symmetric conditions lead to max-separable Lyapunov functions. In addition, we consider two classes of Lyapunov functions: the first class is separable along the system’s state, and the second class is separable along components of the system’s vector field. The latter case is advantageous for many practically motivated systems for which it is difficult to measure the system’s state but easier to measure the system’s velocity or rate of change.
Samuel Coogan, Murat Arcak
2015 We present an efficient computational procedure for finite abstraction of discrete-time mixed monotone systems by considering a rectangular partition of the state space. Mixed monotone systems are decomposable into increasing and decreasing components, and significantly generalize the well known class of monotone systems. We tightly overapproximate the one-step reachable set from a box of initial conditions by computing a decomposition function at only two points, regardless of the dimension of the state space. We apply our results to verify the dynamical behavior of a model for insect population dynamics and to synthesize a signaling strategy for a traffic network.
Dorsa Sadigh, Eric S Kim, Samuel Coogan, S Shankar Sastry, Sanjit A Seshia
2014 We propose to synthesize a control policy for a Markov decision process (MDP) such that the resulting traces of the MDP satisfy a linear temporal logic (LTL) property. We construct a product MDP that incorporates a deterministic Rabin automaton generated from the desired LTL property. The reward function of the product MDP is defined from the acceptance condition of the Rabin automaton. This construction allows us to apply techniques from learning theory to the problem of synthesis for LTL specifications even when the transition probabilities are not known a priori. We prove that our method is guaranteed to find a controller that satisfies the LTL property with probability one if such a policy exists, and we suggest empirically that our method produces reasonable control strategies even when the LTL property cannot be satisfied with probability one.