Introduction to Discrete Event Systems

Cassandras, Christos G.; Lafortune, Stéphane

doi:10.1007/978-1-4757-4070-7

Cited by 1,530 publications

(1,599 citation statements)

References 13 publications

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“…Let φ [t1,t2] (x(0), a) := t∈[t1,t2] φ t (x(0), a). Our objective is to design a supervisor [1], [2] for system (1) that prevents trajectories from entering the bad set. This requirement can be formally expressed using the concept of -safe trajectory:…”

Section: Problem Statementmentioning

confidence: 99%

“…System Σ DT has an infinite number of states. We use the concept of bisimulation (see definition in [2]) to prove the an equivalence of Σ DT and a suitably constructed, finite discrete event system Σ DE . Consider the equivalence relation defined by z 1 z 2 if (z 1 ) = (z 2 ), with defined in (4), and the partition L of Y that it induces.…”

Section: A Supervisor Of the Trivial Systemmentioning

confidence: 99%

“…For the supervisor to enforce safety, this map must ensure that outputs y = h(φ [0,t] (x(0), a)) of (1) follow closely the trajectories φ [0,t] (χ(0), u) of system (3). Here, we exploit the fact that y is a flat output for system (1), hence by virtue of (2) we can design a path in y, and map it onto signals a and x satisfying (1) and (2).…”

Section: B Solution Of Problem 21mentioning

confidence: 99%

“…The problem of controlling multi-agent systems with safety specifications is often addressed in the framework of supervisory control of discrete event systems [1], [2]. The advantage of casting the control problem in this framework lies in the relative simplicity of formally verifying discrete event systems, with respect to dealing with geometric constraints on sets of differential equations.…”

Section: Introductionmentioning

confidence: 99%

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Supervisory control of differentially flat systems based on abstraction

Colombo

Vecchio

2011

IEEE Conference on Decision and Control and European Control Conference

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Abstract-The limiting factor in most implementations of safety enforcing controllers is the model's complexity, and a common work-around includes the abstraction of the physical model, based on differential equations, to a finite symbolic model. We exploit the specific structure of a class of systems (the differentially flat systems) to perform the abstraction. The objective is to construct a supervisor enforcing a set of safety rules, while imposing as little constraints as possible on the system's functionality. An example -a collision avoidance algorithm for a fleet of vehicles converging to an intersection -is presented. Our approach improves on previous results by providing a deterministic symbolic model irrespective of the stability properties of a system, and by addressing explicitly the problem of safety enforcing.

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Section: Problem Statementmentioning

confidence: 99%

Section: A Supervisor Of the Trivial Systemmentioning

confidence: 99%

Section: B Solution Of Problem 21mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Supervisory control of differentially flat systems based on abstraction

Colombo

Vecchio

2011

IEEE Conference on Decision and Control and European Control Conference

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“…In this sense, the scheduling can be a class of Discrete Event Systems (DES) [2]. A variety of different solutions have been proposed to model the system using DES.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective meta-heuristic product scheduling for multi-machine manufacturing systems

Afshar

Wang

2011

IEEE Conference on Decision and Control and European Control Conference

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Abstract-Flow line are one of the most commonly encountered layouts in manufacturing industries, where several product types (grades) are manufactured using a sequence of sub-systems or machinery with different tasks. With increasing prices of energy and specific customer demands employing effective product scheduling strategies has become essential for manufacturing industries to maintain their business viability. In this paper, a new product scheduling method is proposed for multi-machine, multi-product flow lines. The objective here is to control the production start time for each grade so that the product delivery time errors are minimised. It is also desired to minimise the overall makespan variability caused by nonGaussian uncertainties formulated by the entropy of the delivery time errors. Therefore, the proposed product scheduling strategy is a nonlinear multi-objective optimisation problem with non-Gaussian uncertainties. To solve this problem, the nonlinear dynamic flow line model is converted to a linear dynamic equivalent using a (M ax, +) algebraic approach. Then, a Proportional-Integral (PI) scheduling controller is used to control the production start time for each grade. The scheduling controller coefficients are tuned by a MultiObjective Differential Evolution (MODE) algorithm. Simulation results show the effectiveness of the proposed technique and a comparison is made between MODE, Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO).

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