Time-driven Dynamics Research Articles

Fault diagnosis for a class of nonlinear uncertain hybrid systems

This paper presents a fault diagnosis architecture for a class of hybrid systems with nonlinear uncertain time-driven dynamics, measurement noise, and autonomous and controlled mode transitions. The proposed approach features a hybrid estimator based on a modified hybrid automaton framework. The fault detection scheme employs a filtering approach that attenuates the effect of the measurement noise and allows tighter mode-dependent thresholds for the detection of both discrete and parametric faults while guaranteeing no false alarms due to modeling uncertainty and mode mismatches. Both the hybrid estimator and the fault detection scheme are linked with an autonomous guard events identification (AGEI) scheme that handles the effects of mode mismatches due to autonomous mode transitions and allows effective mode estimation. Finally, the fault isolation scheme anticipates which fault events may have occurred and dynamically employs the appropriate isolation estimators for isolating the fault by calculating suitable thresholds and estimating the parametric fault magnitude through adaptive approximation methods. Simulation results from a five-tank hybrid system illustrate the effectiveness of the proposed approach.

A discrete-event and hybrid traffic simulation model based on SimEvents for intelligent transportation system analysis in Mcity

An intelligent transportation systems (ITS) is a typical cyber-physical system (CPS) in which physical components, for example, Connected Automated Vehicles (CAVs), are monitored and controlled through a network of cyber and physical components. Such systems, therefore, contain event-driven dynamics along with time-driven dynamics. The proposed discrete-event and hybrid simulation framework based on SimEvents facilitates testing for safety and performance evaluation of an ITS and has been used to build a traffic simulation model of the Mcity test facility. It is specifically designed for testing CAVs and contains various road/lane configurations and a complete instrumentation system. This enables users to study traffic at the microscopic level, including the effectiveness of new control algorithms for CAVs under different traffic scenarios, the event-driven aspects of transportation systems, and the effects of communication delays. The framework spans multiple toolboxes including MATLAB$^{{\circledR }}$, Simulink$^{{\circledR }}$, and SimEvents$^{{\circledR }}$.

A Discrete-Event and Hybrid Simulation Framework Based on SimEvents for Intelligent Transportation System Analysis

Intelligent transportation systems combine physical elements with cyber components based on information and communication technologies and the use of control methodologies for Connected Automated Vehicles (CAVs). Intelligent transportation systems, therefore, contain event-driven dynamics along with time-driven dynamics. The hybrid nature of such systems motivates the development of new simulation platforms in order to test and evaluate their effectiveness. A discrete-event and hybrid simulation framework based on SimEvents is introduced within which these systems can be studied at the microscopic level. This framework enables users to apply different control strategies as well as communication protocols for CAVs and to carry out performance analysis of proposed algorithms by authoring customized discrete-event and hybrid systems that include various design paradigms such as entity flow, graphical programming, and object-oriented programming in MATLAB®. These paradigms provide users with the flexibility to select or combine modeling elements for achieving complex goals as the demonstrated scenarios in the paper illustrate. The framework spans multiple toolboxes including MATLAB, Simulink®, and SimEvents®.

Performance regulation of event-driven dynamical systems using infinitesimal perturbation analysis

This paper presents a performance-regulation method for a class of stochastic timed event-driven systems aimed at output tracking of a given reference setpoint. The systems are either Discrete Event Dynamic Systems (DEDS) such as queueing networks or Petri nets, or Hybrid Systems (HS) with time-driven dynamics and event-driven dynamics, like fluid queues and hybrid Petri nets. The regulator, designed for simplicity and speed of computation, is comprised of a single integrator having a variable gain to ensure effective tracking under time-varying plants. The gain’s computation is based on the Infinitesimal Perturbation Analysis (IPA) gradient of the plant function with respect to the control variable, and the resultant tracking can be quite robust with respect to modeling inaccuracies and gradient-estimation errors. The proposed technique is tested on examples taken from various application areas and modeled with different formalisms, including queueing models, Petri-net model of a production-inventory control system, and a stochastic DEDS model of a multicore chip control. Simulation results are presented in support of the proposed approach.

Guest editorial: hybrid systems, part II

We consider it a great privilege to introduce this two-part issue on Hybrid Systems to Discrete Event Dynamic Systems: Theory and Applications. Hybrid dynamical systems constitute an abstract modeling framework for systems with bi-layer dynamics: time-driven dynamics at the lower layer, and discrete-event dynamics at the upper layer. This framework provides useful analysis techniques and simulation tools for planning and control of systems in several application areas such as mobile robotics, automotive powertrains, switching circuits, manufacturing, and telecommunications. Consequently, hybrid systems have been extensively investigated by our research community, with emphases on the problems of stability, observability, control, and optimization. The hybrid-systems framework is diverse and includes linear and nonlinear models, deterministic and stochastic systems, and centralized systems as well as distributed networks. One of their unifying themes is the bi-level structure of their dynamics, and the exploration of the interactions between their time-driven dynamics and discrete-event dynamics has been a focus of extensive research. The purpose of this special issue is to bring together papers representing some of the main standing problems in the area of hybrid systems. The contributions comprising the first part of the special issue are summarized in the following paragraphs. The first paper in this issue, On Fluidization of Discrete Event Models: Observation and Control of Continuous Petri Nets, by M. Silva, J. Julvez, C. Mahulea, and C.R. Vazquez, provides a survey of hybrid Petri networks. It starts with a tutorial on the broad topic of fluidization of discrete event dynamic systems and its use in circumventing the problem of the state-space explosion. It then considers Petri networks in detail, where it describes how fluid and hybrid models can be used in

Optimisation of fresh-food supply chains in uncertain environments, Part I: Background and methodology

The design of the supply chain, and in particular of the distribution phase, for fresh-food products, such as fresh and fresh-cut produce, fruit or meat, cannot be achieved without considering the perishable nature and the variability of the products entering the chain. Motivated by these considerations, this paper presents a novel approach for the optimisation of fresh-food supply chains that manages a trade-off between logistic costs and some indices measuring the quality of the food itself as perceived by the consumer, such as ripeness, microbial charge or internal temperature. The supply chain and the behaviour of the product during its delivery are described using a hybrid model consisting of two specific parts. The first part takes into account event-driven dynamics (typically product handling) while the second one describes time-driven dynamics (the dynamics of some parameters characterising the food product in the supply chain). The performance of the supply chain, expressed in terms of both logistic costs and final product quality, are then enhanced using a specific optimisation algorithm that uses the model to assure the feasibility of the proposed optimal solutions. In a companion paper [Dabbene F; Gay P; Sacco N (2008). Optimisation of Fresh-Food Supply Chains in Uncertain Environments, Part II: a Case Study. Biosystems Engineering, accepted], this new methodology is applied to a real-world example concerning meat refrigeration.

Hybrid systems for planning and optimization of virtual assembly

Virtual assembly is the simulation of parts assembly processes by computer, analysing, evaluating and optimizing the feasibilities and procedures of assembly. It can thus avoid the potential problems and risks from designing to assembling. In this way, we can achieve the global optimization of the products and timely respond to the needs of the market. This paper presents a modelling framework for virtual assembly paths design and optimization of two objects on the basis of a class of hybrid system, which is applicable in many manufacturing environments. We propose an elementary hybrid machine containing time-driven and event-driven dynamics. We describe in detail a method of assembly paths design. The objective of optimization is evaluated in terms of time in the transition dynamics so as to make the problem more tractable. An explicit algorithm for deriving optimal assembly policies is developed. The optimal results indicate the feasibility and efficacy of the model and control algorithms.

Statechart-based representation of hybrid controllers for vehicle automation

Recently, designs for the automated highway systems (AHS) to improve the safety and efficiency of highways have attracted much attention. However, such large-scale and complex dynamic systems present a number of control issues. Part of the complication comes from the hybrid nature of these systems, which consists of both time-driven and event-driven dynamics. In this paper, an object-oriented representation towards a hybrid controller design for automated vehicles in an AHS is proposed. The traditional discrete statechart of unified modelling language is extended to model the continuous dynamics of hybrid controllers in a hierarchical and natural representation. The proposed modelling approach provides a clear and natural representation of the interaction between the discrete and continuous behaviours.(A)

TRUETIME: SIMULATION OF NETWORKED COMPUTER CONTROL SYSTEMS

Computer-based control systems and networked control systems are hybrid systems where continuous time-driven dynamics and discrete event-driven dynamics interact. The temporal non-determinism introduced by computing and communication in the form of delays and jitter can lead to significant performance degradation. TrueTime is a MATLAB/Simulink-based simulation tool that takes a completely different approach. Using TrueTime it is possible to simulate the temporal aspects of multi-tasking real-time kernels and wired or wireless networks within Simulink together with the continuous-time dynamics of the controlled plant. TrueTime consists of a block library with a computer kernel block and wired and wireless network blocks. The blocks are variable-step, discrete, MATLAB S-functions written in C++. The kernel block executes user-defined tasks and interrupts handlers. The network blocks distribute messages between computer nodes according to a chosen network model. The blocks are connected with ordinary continuous-time Simulink blocks to form a real-time control system (Less)

Optimal control of a class of hybrid systems

We present a modeling framework for hybrid systems intended to capture the interaction of event-driven and time-driven dynamics. This is motivated by the structure of many manufacturing environments where discrete entities (termed jobs) are processed through a network of workcenters so as to change their physical characteristics. Associated with each job is a temporal state subject to event-driven dynamics and a physical state subject to time-driven dynamics. Based on this framework, we formulate and analyze a class of optimal control problems for single-stage processes. First-order optimality conditions are derived and several properties of optimal state trajectories (sample paths) are identified which significantly simplify the task of obtaining explicit optimal control policies.

Modelling and simulation of manufacturing systems with first-order hybrid Petri nets

First-order hybrid Petri nets are models that consist of continuous places holding fluid, discrete places containing a non-negative integer number of tokens, and transitions, either discrete or continuous. In the first part of the paper, we provide a framework to describe the overall hybrid net behaviour that combines both time-driven and event-driven dynamics. The resulting model is a linear discrete-time, time-varying state variable model that can be directly used by an efficient simulation tool. In the second part of the paper, we focus on manufacturing systems. Manufacturing systems are discrete-event dynamic systems whose number of reachable states is typically very large, hence approximating fluid models have often been used in this context. We describe the net models of the elementary components of a flexible manufacturing system (machines and buffers) and we show in a final example how these modules can be put together in a bottom-up fashion.

Optimal control of hybrid systems in manufacturing

Hybrid systems combine time-driven and event-driven dynamics. This is a natural framework for manufacturing processes: The physical characteristics of production parts undergo changes at various operations described by time-driven models, while the timing control of operations is described by event-driven models. Accordingly, in the framework we propose, manufactured parts are characterized by physical states (e.g. temperature, geometry) subject to time-driven dynamics and by temporal states (e.g., operation start and stop times) subject to event-driven dynamics. We first provide a tutorial introduction to this hybrid system framework and associated optimal control problems through a single-stage manufacturing process model. We then show how the structure of the problem can be exploited to decompose what is a hard nonsmooth, nonconvex optimization problem into a collection of simpler problems. Next, we present extensions to multistage manufacturing processes for which we develop solution algorithms that make use of Bezier approximation techniques. Emphasis is given to the issue of deriving solutions through efficient algorithms, and some explicit numerical results are included.