Continuous flow Systems and Control Methodology Using Hybrid Petri nets

International audienceIn this paper, we consider the controller synthesis for continuous flow systems. These lasts are a sub-class of hybrid dynamic systems. Their main characteristics are positiveness and linearity. Transport, manufacturing, communication and biological systems are examples of continuous flow systems. Numerous tools and techniques exist in the literature for modelling and analyzing such systems. As positiveness is a hard constraint, an appropriate tool integrating naturally this constraint is strongly needed. Hybrid Petri Nets are an elegant modeling tool of positive systems, while Hybrid Automata are a powerful tool giving formally the reachable dynamic space. Combining these two tools aim to a sound approach for control synthesis of continuous flow systems. We start by considering the process to control and compute its reachable state space using specialized software like PHAVer. Algebraic inequalities define this reachable state space. The constrained behaviour is obtained by restricting this state space into a smaller desired space. This reduction is expressed in term of linear constraints only over the continuous variables; while the control is given by the discrete transitions (occurrence dates of controllable events). The controller synthesis methodology is based on the control of a hybrid system modelled by a D-elementary hybrid Petri Net. The control consists in modifying the guard of the controllable transitions so as the reachable controlled state space is maximally permissive

Modeling and analysis using hybrid Petri nets

This paper is devoted to the use of hybrid Petri nets (PNs) for modeling and control of hybrid dynamic systems (HDS). Modeling, analysis and control of HDS attract ever more of researchers' attention and several works have been devoted to these topics. We consider in this paper the extensions of the PN formalism (initially conceived for modeling and analysis of discrete event systems) in the direction of hybrid modeling. We present, first, the continuous PN models. These models are obtained from discrete PNs by the fluidification of the markings. They constitute the first steps in the extension of PNs toward hybrid modeling. Then, we present two hybrid PN models, which differ in the class of HDS they can deal with. The first one is used for deterministic HDS modeling, whereas the second one can deal with HDS with nondeterministic behavior. Keywords: Hybrid dynamic systems; D-elementary hybrid Petri nets; Hybrid automata; Controller synthesi

Continuous Petri Nets and Hybrid Automata:Two Bisimilar Models for the Simulation of Positive Systems

International audiencePetri nets (PNs) are a well-known modelling tool for discrete event systems. Continuous PN were introduced in order to avoid the combinatory explosion of the number of states, when considering real life systems. The constant speed continuous Petri net (CCPN) can be used to model discrete events systems; in that case, they constitute an approximation, which is often satisfactory. They can also model positive continuous systems. Hybrid automata (HA) are a less compact and expressive model, but, they can be used to perform powerful analysis. In this paper, we first present deeply the continuous PN and its modelling advantages. Then we present the main contribution of this paper, that is a structural translation algorithm from a CCPN into a HA. The translation algorithm is structural in the sense that it does not depend on the initial marking of the Petri net. We prove the timed bisimilarity between both models

DETERMINATION OF AN EMPIRICAL MODEL OF AVERAGE RANK FOR MULTI-DEEP AS/RS BASED ON SIMULATION

International audienceWe consider in this paper multi-deep automated storage/retrieval systems, where the cells capacity is strictly greater than one load. The main advantage of this class of AS/RS is a better use of space. Its main drawback is that, in order to retrieve a desired load, it is necessary to move all the loads in front of it. This is a common characteristic of all the multi-deep automated storage/retrieval systems. The number of loads to move is given by the rank of the load to retrieve. The mean value of the average access time to a given load is an important performance index for the design of such systems. This time depends on several parameters among which the average retrieval rank is the hardest to compute. It is still an open problem, even with random storage and retrieval heuristics. In this paper, our objective is to provide an empirical formula of the average retrieval rank in a multi-deep automated storage/retrieval system with random heuristics for both storage and retrieval. With this formula, it is then easy to deduce the mean retrieval time. This computation is based on multiple simulations of various AS/RS models and a regression on the obtained data. The particular case of the flow-rack automated storage/retrieval system will be considered to illustrate our contribution. It will then be possible to use this formula for other multi-deep systems

H²CM-based holonic modeling of a gas pipeline

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Solving a Job Shop Scheduling Problem Using Q-Learning Algorithm

International audienceJob Shop Scheduling Problem (JSSP) is among the combinatorial optimization and Non-Deterministic Polynomial-time (NP) problems. Researchers have contributed in this area using several methods, among the methods we have machine learning algorithms, more precisely Reinforcement Learning (RL). The reason why the scientists resort to RL is the adequacy of the algorithm for this type of problem. The results of the RL approach tend toward optimal or nearoptimal solutions. In this paper, we deal with the JSSP, using the RL algorithm, more specifically a Q-learning algorithm. We propose a new representation of the state of the environment. We introduce two evaluations of the agent using two different methods. The actions selected by the agent are the dispatching rules. Finally, we compared the results obtained by the approach with the literature

H²CM-based holonic modeling of a gas pipeline

International audienceA gas pipeline is a relatively simple physical system, but the optimality of the control is difficult to achieve. When switching from one kind of gas to another , a volume of useless mixture is generated. Therefore, the control needs to both respond to the demand and minimize the volume of lost gas. In case of stable and perfectly known demand, scheduling techniques can be used, but in other cases , calculation times are incompatible with an industrial application. This article introduces the application of H²CM (Holonic Hybrid Control Model) generic architecture on this specific case. The study case is extensively presented. Then, the defined holonic architecture (H²CM compatible) is detailed, and the role and functions of each holon are presented. Finally, a tentative general control algorithm is suggested, which gives an insight on the actual algorithms that will be developed in perspective of this work