Discrete Event and Hybrid Systems

Research on Discrete Event and Hybrid Systems.

The main challenges we face in designing and controlling discrete event and hybrid systems come from:

  • Inherent complexity (combinatorial explosion in many cases)
  • Unpredictability, uncertainty
  • Increasingly ambitious “high-performance” technological requirements

CODES Lab activities cover a wide spectrum, from basic research to the development of software tools.

These activities include:

  • Design and real-time control of communication and sensor networks, manufacturing systems and transportation systems
  • Decision support systems for quality-of-service guarantees or optimal performance
  • Software testing and verification
  • Strategic planning: getting information to decision makers fast and in a comprehensive form
  • Developing a new generation of concurrent and parallel simulation tools
  • New methods for cooperative control of wirelessly networked devices
  • Autonomously reconfigurable systems
  • New methods for energy-aware systems
  • Developing an infrastructure for “smart cities”
Examples of modeling frameworks for Discrete Event Systems and Hybrid Systems

From Time-Driven to Event Driven

In the day-to-day life of our “man-made” and increasingly computer-dependent world, we notice:

  • First, that many of the quantities we deal with are discrete, typically involving counting integers (how many parts in an inventory, how many planes in a runway, how many telephone calls are active).
  • Second, that what drives many of the processes we use and depend on are instantaneous events such as the pushing of a button, hitting a keyboard key, or a traffic light turning green.

In fact, much of the technology we have invented and rely on (especially where digital computers are involved) is event-driven: communication networks, manufacturing systems, or the execution of a computer program are common examples. Not only must these systems act as “event coordinators”, but they are also expected to swiftly react to unpredictable events, rapidly adapt to changing conditions, and guarantee their users satisfactory – if not optimal – performance.

In short, all activity in these systems is due to asynchronous occurrences of discrete events, some controlled (like hitting a keyboard key) and some not (like a spontaneous equipment failure). This feature lends itself to the term Discrete Event System (DES). When systems combine both time-driven and event-driven behavior, we then deal with Hybrid Systems.


Historically, scientists and engineers have concentrated on studying and harnessing natural phenomena systems which are well modeled by the laws of gravity, classical and nonclassical mechanics, electromagnetics, physics, chemistry, etc. In doing so, we typically deal with quantities such as displacement, velocity, and acceleration of particles, or the pressure, temperature, and flow rates of fluids and gases. These are continuous variables in the sense that they can take on any real value as time itself continuously evolves. Based on this fact, a vast body of mathematical tools and techniques has been developed to model, analyze, and control the systems around us. It is fair to say that the study of ordinary and partial differential equations currently provides the main infrastructure for system analysis and control.

The rapid evolution of technology has brought new dynamic systems, mostly “man-made” and highly complex. Examples abound: computer networks, sensor networks, cyber-physical systems, automated manufacturing systems, traffic control systems, integrated command-control-information systems, etc. Their complexity can be overwhelming. This is the main motivation to analyze and study Discrete Event and Hybrid Systems.