Leila Zouaghi

Modified particle petri nets for hybrid dynamical systems monitoring under environmental uncertainties

A real-time system in its environment constitutes a complex dynamic system (in general hybrid), which requires the development of new methods of modeling and monitoring. This paper presents the Modified Particle Petri nets (MPPN) approach, which combines Petri nets and particle filtering in order to model and monitor Hybrid Dynamical Systems (HDS) and their interaction with the environment. The approach considers the uncertainty of the process on both discrete and continuous variables as well as the environmental uncertainties. It presents relevant concepts of Petri nets within a probabilistic framework and how uncertainty is introduced (i) in the hybrid marking of the Petri net in order to represent an uncertain knowledge about all next possible hybrid states (ii) as well as in the process noise, which is incorporated into the filter. A marking updating of the Petri nets according to noisy measurements made by the system and a decision making algorithm are then used in order to estimate the hybrid system state, to detect inconsistencies and to attribute these to different kinds of faults. An example of mobile robot application modeled using MPPN is presented to show the usability of the approach.

Hybrid, recursive, nested monitoring of control systems using Petri nets and particle filters

In this paper we propose a recursive and nested hybrid monitoring and diagnosis architecture for systems with Recursive Nested Behaviour-based Control structure. Such systems consist of behavioural levels, which use several models on different levels of abstractions. In this architecture scheme, the monitors of each subsystem use recursively the output of the monitors of the next lower level in order to get an estimate of the global status of the system at each time and having the advantage of low dimensionality for each level. The proposed monitoring structure is based on hybrid Petri Nets and particle filters. The advantages of the approach are illustrated by an example for a Heating Control System.

Probabilistic online-generated monitoring models for mobile robot navigation using modified Petri net

This paper presents a generic hybrid online monitoring approach allowing the detection of inconsistencies in the navigation of autonomous mobile robots. The originalities of this work are (i) the association of classic state estimation based on particle filter to a special class of Petri net in order to deliver an estimation of the next robot state (position) as well as the environment state (graph nodes) and to use both information to distinguish between external noise influences, internals component faults or global behaviour inconsistency (ii) the integration of the geometrical and the logical environment representation into the monitor model in order to generate online the corresponding navigation monitoring model for the present mission trajectory while the system is running. The model takes simultaneously into account the uncertainty of the robot and of the environment through a unified modeling of both. To show the feasibility of the approach we apply it to an Intelligent Wheelchair (IWC) as a special type of autonomous mobile robot.

Mission-based online generation of probabilistic monitoring models for mobile robot navigation using Petri nets

This paper presents a generic hybrid monitoring approach, which allows the detection of inconsistencies in the navigation of autonomous mobile robots using online-generated models. A mission on the context of the navigation corresponds to an autonomous navigation from a start to an end mission point. The operator defines this mission by selecting a final goal point. Based on this selection the monitoring models for the current mission must be generated online. The originalities of this work are (i) the association of classic state estimation based on a particle filter with a special class of Petri net in order to deliver an estimation of the next robot state (position) as well as the environment state (graph nodes) and to use both pieces of information to distinguish between external noise influences, internal component faults and global behaviour inconsistency (ii) the integration of the geometrical and the logical environment representation into the monitor model (iii) the online generation of the corresponding monitoring model for the present mission trajectory while the system is running. The model takes simultaneously into account the uncertainty of the robot and of the environment through a unified modelling of both. To show the feasibility of the approach we apply it to an intelligent wheelchair (IWC) as a special type of autonomous mobile robot.

Monitoring of Hybrid Systems using Behavioural System Specification

Abstract This paper proposes hybrid monitoring of lower level actions for dependable (mobile) control systems including closed-loop control. We address a special class of hybrid dynamical systems, called switched systems. Here, the notion of switching is used to describe the state change when reaching a given bound or due to an external action that may be performed by a human operator. The approach is based on the consideration of both discrete and continuous aspects within a single unified framework by combining Petri-net and numerical filter theory. The model uses a specification of the desired system behaviour during the system design in order to deliver an on-line estimation of the current system state. The main goal of this approach is to get a generic formal method to create monitors for hybrid systems using the behavioural specification and a special class of mixed numerical and symbolic Petri-Net. To show the feasibility of the approach we apply it in the mobile robot area to an intelligent wheelchair. Such system can be best represented by hybrid models and presents a number of interesting new challenges for monitoring and diagnostic methodologies, which could be easily generalized to any autonomous mobile robot.

A unified design approach for component-based dynamic systems monitoring

A design approach is proposed for the efficient specification and realization of dynamic system monitoring components based on a unified system component model. Starting form the formal specification of the component under monitoring and a generic monitoring model, runtime executable monitor components are constructed in a straightforward way. Hybrid continuous-discrete system descriptions and a state estimator with Modified Particle Petri Nets are utilized in order to cover many computer controlled systems. The design approach is demonstrated on a heating control system, which serves as a standard process control example. System implementation and experimental results demonstrate the high potential of the method.

Associative memory for modified Petri-net based monitoring of mobile robot navigation

This paper presents an extension of a generic hybrid online monitoring approach for the navigation of autonomous mobile robots through an associative memory. Due to this extension, when a fault occurs the navigation process does not have to stop running in order to keep the system in a safe state and to ensure a high dependability. The associative memory enables the robot to remember the previously traversed paths inside a known environment and to learn new, unknown paths on-line. This long-term memory makes the monitor more dependable and more robust with respect to the fault detection, diagnosis and handling. To show the feasibility of the approach we apply it on an example of a mobile robot application.

An integrated distributed monitoring for mission-based systems: On the example of an autonomous unmanned helicopter

This paper presents a new approach of an integrated distributed monitoring system for an autonomous unmanned helicopter. The navigation mission consists of reaching areas of interest on a path composed of specific waypoints. The task of such a monitoring system is to determine whether the helicopter behaves as intended in order to detect faults in the mission execution early and to enable a re-planning of the mission or a failure recovery in due time. This is a very important issue for autonomous systems as they have to cope not only with ordinary subsystems failures, but also with unexpected situations which make the mission goals unattainable. The approach uses a special combination of Petri Nets and Monte Carlo Methods (particle filter) and integrates different layers of the system architecture such as control actions (path-planning, low-level control), environment perception (vision-based recognition of landmarks), state observation (estimation of speed, position, attitude) and decision making (mission planning and re-planning) in a unified model.