Paulo Tabuada

Bio: Paulo Tabuada is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Control system & Control theory. The author has an hindex of 60, co-authored 288 publications receiving 20444 citations. Previous affiliations of Paulo Tabuada include University of California, Berkeley & Instituto Superior Técnico.

An Approximate Simulation Approach to Symbolic Control

Abstract: This paper introduces a methodology for the symbolic control of nonlinear systems based on an approximate notion of simulation relation. This notion generalizes existing exact notions of simulation and is completely characterized in terms of known stabilizability concepts. Equipped with this notion we show how, under certain stabilizability assumptions, we can construct finite or symbolic models for nonlinear control systems. Synthesizing controllers for the original control system can then be done by using supervisory control techniques on the finite models and by refining the resulting finite controllers to hybrid controllers enforcing the specification on the original continuous control system. The proposed design methodology can be seen as a correct-by-design way of obtaining both the feedback control laws as well as the control software responsible for deciding which law is executed and when.

On self-triggered control for linear systems: Guarantees and complexity

Abstract: Typical digital implementations of feedback controllers periodically measure the state, compute the control law, and update the actuators. Although periodicity simplifies the analysis and implementation, it results in a conservative usage of resources. In this paper we drop the periodicity assumption in favor of self-trigger strategies that decide when to measure the state, execute the controller, and update the actuators according to the current state of the system. In particular, we develop a general procedure leading to self-triggered implementations of feedback controllers, that highly reduces the number of controller executions while guaranteeing a desired level of performance. We also analyze the inherent trade-off between the computational resources required for the self-triggered implementation and the resulting performance. The theoretical results are applied to a physical example to show the benefits of the approach.

Feasible formations of multi-agent systems

Abstract: Formations of multi-agent systems, such as satellites and aircraft, require that individual agents satisfy their kinematic equations while constantly maintaining inter-agent constraints. In this paper, we develop a systematic framework for studying formations of multiagent systems. In particular, we consider undirected formations for centralized formations and directed formations for decentralized formations. In each case, we determine differential geometric conditions that guarantee formation feasibility given the individual agent kinematics. Our framework also enables us to extract a smaller control system that describes the formation kinematics while maintaining,all formation constraints.

Correctness Guarantees for the Composition of Lane Keeping and Adaptive Cruise Control

Abstract: This paper develops a control approach with correctness guarantees for the simultaneous operation of lane keeping and adaptive cruise control. The safety specifications for these driver assistance modules are expressed in terms of set invariance. Control barrier functions (CBFs) are used to design a family of control solutions that guarantee the forward invariance of a set, which implies satisfaction of the safety specifications. The CBFs are synthesized through a combination of sum-of-squares program and physics-based modeling and optimization. A real-time quadratic program is posed to combine the CBFs with the performance-based controllers, which can be either expressed as control Lyapunov function conditions or as black-box legacy controllers. In both cases, the resulting feedback control guarantees the safety of the composed driver assistance modules in a formally correct manner. Importantly, the quadratic program admits a closed-form solution that can be easily implemented. The effectiveness of the control approach is demonstrated by simulations in the industry-standard vehicle simulator Carsim. Note to Practitioners —Safety is of paramount importance for the control of automated vehicles. This paper is motivated by the problem of designing controllers that are provably correct for the simultaneous operation of two driver assistance modules, lane keeping and adaptive cruise control. This is a challenging problem partially, because the lateral and longitudinal dynamics of the vehicles are coupled, with few results known to exist that provide formal guarantees. In this paper, we employ an assume-guarantee formalism between these two subsystems, such that they can be considered individually; based on that, we use optimization to design safe sets that serves as “supervisors” for vehicle behavior, such that the trajectories of the closed-loop system are confined within the safe sets using predetermined bounds on wheel force and steering angle. The feedback controller is constructed by solving convex quadratic programs online, which can also be given in closed form, making the implementation much easier. One particular advantage of this control approach is that the safety set and the performance controller can be designed separately, which enables the integration of a legacy controller into a correct-by-construction solution.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Information flow and cooperative control of vehicle formations

Abstract: We consider the problem of cooperation among a collection of vehicles performing a shared task using intervehicle communication to coordinate their actions. Tools from algebraic graph theory prove useful in modeling the communication network and relating its topology to formation stability. We prove a Nyquist criterion that uses the eigenvalues of the graph Laplacian matrix to determine the effect of the communication topology on formation stability. We also propose a method for decentralized information exchange between vehicles. This approach realizes a dynamical system that supplies each vehicle with a common reference to be used for cooperative motion. We prove a separation principle that decomposes formation stability into two components: Stability of this is achieved information flow for the given graph and stability of an individual vehicle for the given controller. The information flow can thus be rendered highly robust to changes in the graph, enabling tight formation control despite limitations in intervehicle communication capability.

Event-Triggered Real-Time Scheduling of Stabilizing Control Tasks

Abstract: In this note, we revisit the problem of scheduling stabilizing control tasks on embedded processors. We start from the paradigm that a real-time scheduler could be regarded as a feedback controller that decides which task is executed at any given instant. This controller has for objective guaranteeing that (control unrelated) software tasks meet their deadlines and that stabilizing control tasks asymptotically stabilize the plant. We investigate a simple event-triggered scheduler based on this feedback paradigm and show how it leads to guaranteed performance thus relaxing the more traditional periodic execution requirements.

Coverage control for mobile sensing networks

Abstract: This paper describes decentralized control laws for the coordination of multiple vehicles performing spatially distributed tasks. The control laws are based on a gradient descent scheme applied to a class of decentralized utility functions that encode optimal coverage and sensing policies. These utility functions are studied in geographical optimization problems and they arise naturally in vector quantization and in sensor allocation tasks. The approach exploits the computational geometry of spatial structures such as Voronoi diagrams.

Coverage control for mobile sensing networks

Abstract: This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.