Controller synthesis for parameterized discrete event systems

Les systèmes à événements discrets sont des systèmes dynamiques particuliers. Ils changent d’état de fa¸con discrète et le terme événement est utilisé afin de représenter l’occurrence de changements discontinus. Ces systèmes sont principalement construits par l’homme et on les retrouve surtout dans les secteurs manufacturier, de la circu- lation automobile, des bases de données et des protocoles de communication. Cette thèse s’intéresse au contrôle des systèmes paramétrés à événements discrets où les spécifications sont exprimées à l’aide de prédicats et satisfont une condition de similarité. Des conditions sont données afin de déduire des propriétés, en observation partielle ou totale, pour un système composé de n processus similaires à partir d’un système com- posé de n0 processus, avec n ≥ n0. De plus, il est montré comment inférer des politiques de contrôle en présence de relations d’interconnexion entre les processus. Cette étude est principalement motivée par la faiblesse des méthodes actuelles de synthèse pour le traitement des problèmes industriels de taille réelle.Discrete event systems are a special type of dynamic systems. The state of these systems changes only at discrete instants of time and the term event is used to represent the occurrence of discontinuous changes. These systems are mostly man-made and arise in the domains of manufacturing systems, traffic systems, database management systems and communication protocols. This thesis investigates the control of parameterized discrete event systems when specifications are given in terms of predicates and satisfy a similarity assumption. For systems consisting of similar processes under total or partial observation, conditions are given to deduce properties of a system of n processes from properties of a system of n0 processes, with n ≥ n0. Furthermore, it is shown how to infer a control policy for the former from the latter’s, while taking into account interconnections between processes. This study is motivated by a weakness in current synthesis methods that do not scale well to huge systems

Deep Reinforcement Learning for Event-Triggered Control

Event-triggered control (ETC) methods can achieve high-performance control with a significantly lower number of samples compared to usual, time-triggered methods. These frameworks are often based on a mathematical model of the system and specific designs of controller and event trigger. In this paper, we show how deep reinforcement learning (DRL) algorithms can be leveraged to simultaneously learn control and communication behavior from scratch, and present a DRL approach that is particularly suitable for ETC. To our knowledge, this is the first work to apply DRL to ETC. We validate the approach on multiple control tasks and compare it to model-based event-triggering frameworks. In particular, we demonstrate that it can, other than many model-based ETC designs, be straightforwardly applied to nonlinear systems

Dynamically Stable 3D Quadrupedal Walking with Multi-Domain Hybrid System Models and Virtual Constraint Controllers

Hybrid systems theory has become a powerful approach for designing feedback controllers that achieve dynamically stable bipedal locomotion, both formally and in practice. This paper presents an analytical framework 1) to address multi-domain hybrid models of quadruped robots with high degrees of freedom, and 2) to systematically design nonlinear controllers that asymptotically stabilize periodic orbits of these sophisticated models. A family of parameterized virtual constraint controllers is proposed for continuous-time domains of quadruped locomotion to regulate holonomic and nonholonomic outputs. The properties of the Poincare return map for the full-order and closed-loop hybrid system are studied to investigate the asymptotic stabilization problem of dynamic gaits. An iterative optimization algorithm involving linear and bilinear matrix inequalities is then employed to choose stabilizing virtual constraint parameters. The paper numerically evaluates the analytical results on a simulation model of an advanced 3D quadruped robot, called GR Vision 60, with 36 state variables and 12 control inputs. An optimal amble gait of the robot is designed utilizing the FROST toolkit. The power of the analytical framework is finally illustrated through designing a set of stabilizing virtual constraint controllers with 180 controller parameters.Comment: American Control Conference 201

A Formal Framework for Concrete Reputation Systems

In a reputation-based trust-management system, agents maintain information about the past behaviour of other agents. This information is used to guide future trust-based decisions about interaction. However, while trust management is a component in security decision-making, many existing reputation-based trust-management systems provide no formal security-guarantees. In this extended abstract, we describe a mathematical framework for a class of simple reputation-based systems. In these systems, decisions about interaction are taken based on policies that are exact requirements on agents’ past histories. We present a basic declarative language, based on pure-past linear temporal logic, intended for writing simple policies. While the basic language is reasonably expressive (encoding e.g. Chinese Wall policies) we show how one can extend it with quantification and parameterized events. This allows us to encode other policies known from the literature, e.g., ‘one-out-of-k’. The problem of checking a history with respect to a policy is efficient for the basic language, and tractable for the quantified language when policies do not have too many variables