SAIA: safe deployment of sensors based real time application

International audienceSAIA is a model based architecture for the development of sensors based real time applications. This paper presents the mandatory concepts to manage the extra-functional properties relating to the communication with the physical environment. Moreover, it proposes an implementation of these concepts and then a way to realize a safe application deployment

Model driven engineering method for SAIA architecture design

National audienceABSTRACT. SAIA is an architectural style for the development of systems dedicated to process control. Designing an architecture that conforms to a style implies the manipulation of a lot of entities and the respect of numerous constraints. The approaches based on models and models transformations are well adapted to manage the complexity and to enforce the separation of concerns. This paper presents a model driven engineering method for the development and the validation of systems that conform to SAIA. Moreover, a tool supporting the method allows a systematic use of models and transformations.RÉSUMÉ. SAIA est un style architectural destiné au développement de systèmes dédiés au contrôle de procédés. Construire une architecture conforme à un style architectural donné nécessite la manipulation de nombreuses entités et le respect de nombreuses contraintes. Les approches basées sur les modèles et les transformations de modèles permettent de gérer cette complexité et d'imposer une séparation des préoccupations. Notre objectif est alors de fournir une méthode basée sur les concepts définis par l'ingénierie dirigée par les modèles pour le développement et la validation de systèmes conformes à SAIA. Enfin, un outil implémente la méthode proposée afin d'aider le concepteur à respecter les modèles et leurs transformations

Control and Real-time Scheduling Co-design : Application to Robust Robot Control

International audienceControl systems running on a computer are subject to timing disturbances coming from implementation constraints. Fortunately closed-loop systems behave robustly w.r.t. modelling errors and disturbances, and the controller design can be performed to explicitly enhance robustness against specific uncertainties. On one hand robustness in process controllers can be used to comply with weakly modelled timing uncertainties. On the other hand the principle of robust closed-loop control can also be applied to the real-time scheduler to provide on-line adaption of some scheduling parameters, with the objective of controlling the computing resource allocation. The control performance specification may be set according to both control and implementation constraints. The approach is illustrated through several examples using simulation and an experimental feedback scheduler is briefly described

Distributed dispatchers for partially clairvoyant schedulers

This work focuses on the empirical evaluation of distributed dispatching strategies on shared and distributed memory architectures for hard real-time systems. The dispatching model accommodates process parameter variability and analyzes the effect of variable execution times.;Hard real-time systems are modeled in the E-T-C scheduling framework and dispatched if a valid schedule exists. We examine the dispatchability of Partially Clairvoyant schedules of different sizes and varying deadlines under reasonable assumptions. The effect of scaling up the number of processors used by the dispatcher is also studied. The results validate the superiority of the distributed strategies over sequential dispatching and scalability of the distributed strategies. Certain system limitations which lead to Loss of Dispatchability in the experiments were pointed out.;The model finds applications in diverse areas like safety critical systems, robotics and machine control, real-time data management, and this approach is targeted at powering up the controllers

Design of real-time periodic control systems through synchronization and fixed priorities

Control systems are often designed using a set of co-operating periodic modules running under control of a real-time operating system. A correct behaviour of the closed-loop controller requires that the system meets timing constraints like periods and latencies, which can be expressed as deadlines. The control system timing requirements are captured through a partition in control paths by which priorities are assigned according to their relative urgency. Latencies are managed through precedence constraints and more or less tight synchronization between modules. The implementation uses the fixed-priority based pre-emption service of an off-the-shelf real-time operating system. Such a system can be modelled with timed event graphs, and its temporal behaviour can be analysed using the underlying (max, plus) algebra. Examples coming from a uni-processor robot controller are provided

FeedNetBack-D04.03 - Design of Robust Variable Rate Controllers

A consequence of the execution of control algorithms on digital distributed platforms is inducing delays, jitter and various limitations in sampling rate from different sources in the control loops. These disturbances should be taken into account in the control algorithms design and tuning. Control systems are often cited as examples of "hard real-time systems" where jitter and deadline violations are strictly forbidden. In fact experiments show that this assumption may be false for closed-loop control. Any practical feedback system is designed to obtain some stability margin and robustness w.r.t. the plant parameters uncertainty. This also provides robustness w.r.t. timing uncertainties: closed-loop systems are able to tolerate some amount of sampling period and computing delays deviations, jitter and occasional data loss without loss of stability or integrity. Hence the design of dependable distributed control systems may rely on robust controllers, i.e. controllers which are slightly sensitive to both process model and execution resource uncertainties, or on controllers which are made adaptive w.r.t. the variations of the control intervals and other implementation induced disturbances. Section 2 provides new results concerning the control of systems with delays. A novel analysis of linear systems under asynchronous sampling is provided. This approach is based on the discrete-time Lyapunov Theorem applied to the continuous-time model of the sampled-data systems. Tractable conditions are derived to ensure asymptotic stability and also to obtain an estimate of the exponential rate of the solutions. Examples show the efficiency of the method and the reduction of the conservatism compared to other results from the literature. Moreover the methodology addresses the stability analysis of systems under several sampling periods. We show that a sampled-data system can be stable even if one of the sampling period leads to instability. This has been treated by a continuous-time approach and allows considering uncertain or time-varying systems. An extension of the method includes transmission delays in the control loop. As the variations of the control intervals can be both a consequence of network induced delays and a control variable to manage the CPU and/or network load, robust variable sampling control design is investigated in section 3. Here it is assumed that the control interval is itself a control parameter, e.g. which can be adapted at run-time by a feedback scheduler to cope with operating conditions in a varying environment. The control design is stated using the formulation of Linear Parameters Varying (LPV) systems, where the sampling interval is considered as a varying and measurable parameters of the system. Previous results using a polytopic model of a discretized plant are recalled. A new design using a Linear Fractional Transform (LFT) is developed, where the control interval is considered as a system's uncertainty. This new approach is expected to be more tractable that the polytopic one when the system has several varying parameters. Both designs are assessed and compared using as testbed the control of Autonomous Underwater Vehicles using scheduled ultrasonic sensors for control and navigation.