Supervisory Control of Discrete Event Systems

A physical process can be modelled as a DES when it is of interest its behavior in terms of forced and generated events. The control problem is, known the occurred events, forcing events in such a manner that a desired behavior is obtained. In DES control theory it is assumed that a symbol is associated to each event and the behavior of a DES can be described in terms of generated sequences of events/symbols that form a language. A more efficient approach is to split the control problem in two parts: the command of subsystems and their coordination. This is known in the literature as the supervised control approach. The physical system plus the forcing controller is regarded as an extended process for which events are considered as spontaneously generated. Supervisory control theory provides powerful results for Discrete Event Systems (DESs) where events are spontaneously generated (event occurrence cannot be forced) and, some events, named controllable events, can be disabled, while the other ones are called uncontrollable events. As for uncontrollable events, their occurrence can be just observed. An external agent, called supervisor, disables the occurrence of controllable events in order to enforce logical constraints on the sequence of events generated by the system. The advantage of this approach is that the supervisor must only manage coordination of (controlled) subsystems without considering direct control of the subsystems (delegated to the forcing controller). Noticeably, if coordination specification are changed only supervisor must be re-designed, while the subsystems’ controller is not affected. At the same time the abstraction of supervisory control theory that assumes events spontaneously generated by the physical process, useful for simplifying theory, is preserved and the implementation is not ambiguous (control and supervision concepts are not mixed). The main research activity of our group focuses essentially on the supervisory control based on Petri nets with logical and timed specifications.

Implementation of Discrete event control systems

Supervisory control theory is nowadays well developed, but its use in industrial applications can be limited by a set of problems arising from implementation of supervisors on common control machines. As a matter of fact, the asynchronous nature of supervisors and the fact that they are non-deterministic models make their implementation on synchronous devices like Programmable Logic Controllers and personal computers not a simple task. The supervised control architecture consisting of a controller and a supervisor must be completed by a dispatcher to obtain a closed loop deterministic behavior. The main research activity of our group focuses essentially on the design and implementation of supervisors on industrial control machines starting from a formal definition of the controlled system’s behaviour and from the control specification but at same time as much as possible compliant with IEC standards for the implementation of control systems (IEC 61131 and 61499).

Fault diagnosis of Discrete event systems

In DES faults are usually represented as unobservable events, because their diagnosis would be trivial if they were observable. Fault events are not the only unobservable ones. In words, fault detection consists of on-line monitoring the system using the record of observed events to timely provide the set of faults that could have happened. A problem strongly related to fault diagnosis is that of diagnosability, that is equivalent to determine if, once a fault has occurred in the system, its occurrence can be detected in a finite number of steps. Diagnosability of DES deals with the possibility of detecting, within a finite delay, the occurrences of fault events using the record of observed events. The main research activity of our group focuses actually on fault diagnosis of timed DES.

Identification of Discrete Event Systems

The interest for the identification of DESs usually comes from reverse engineering for (partially) unknown systems, fault diagnosis, or system verification. The data collected from the observation of a discrete-event system are usually given in terms of behavioural sequences that may be fixed or may be increased in the course of the system operation (e.g., due to new experiments or simply to the system running). An identification algorithm of DESs produce a mathematical model expressed as a Petri net (PN) or a finite state automaton model of the system behavior from sequences observed during the system. The main research activity of our group focuses actually on the identification of timed DES.

Simulation Based Control

Optimization methods attempt to determine the values for the controllable variables of a system so that a certain outcome of the system is optimized. Many of these systems can be formulated analytically and optimized by various techniques of mathematical programming. However, there are more complicated systems, often stochastic in nature, for which the objective function cannot be expressed analytically. For many of these problems, the analyst must resort to computer simulation to evaluate the system. If a decision policy is defined as a set of values for several quantitative variables, it is easy to see that simulation can be efficiently used as a means of optimization of the corresponding system. In other words, simulation response can be used as an objective function of an optimization problem for which the decision variables represent the variables that have to be optimized. The main research activity of our group focuses essentially on simulation control approach for logistic and automated warehouse control systems.

Control of cooperative manipulators

Among the biggest challenges facing robotics in the near future are those of cooperation robotics (multiple robots cooperating to perform a task) and collaborative robotics (robots and men who must work together to perform a task). Typical robotic work cells are mainly employed in the presence of high volumes of production, that can justify conspicuous investments of money and time: it is not unusual that setting up a painting cell for cars can take several days, hence the cell becomes convenient only if it will work for a long period of time in the same condition. On the other hand, in the world of small and medium enterprises (SMEs) processing of type "craft" on small volumes (to the limit, even on individual pieces) are often required. The objectives of the research project are the development of control solutions for multiple robotic systems in tight cooperation, also with human beings eventually to execute different task, and the focus is on industrial tasks that require explicit physical contact and exchange of forces between the robot and /or between robots and humans.

Control of multi-robot systems

Cooperation and coordination of multi-robot systems has been object of considerable research in the last decades. The reason behind this is that systems composed by multiple robots can achieve tasks more efficiently than a single robot or can accomplish tasks not executable by a single one. Moreover, multi-robot systems have advantages like increasing tolerance to possible vehicle fault, providing flexibility to the task execution or taking advantage of distributed sensing and actuation. Such systems can be used in many applications like, e.g., exploration of an unknown environment, navigation and formation control, demining, object transportation, up to playing team games (e.g., soccer). Within this framework, control laws for handling the intrinsic redundancy of these systems were designed and tested on several platforms constitued by underwater, mobile ground, marine surface and fixed-based robots.

Distributed control of multi-agent systems

Applications involving multi-agent systems are day by day increasing, since they allow to accomplish complex tasks otherwise impossible for a single unit. Moreover, the system robustness to failures and reliability are potentially increased with respect to a single unit by distributing the tasks across the agents. A common approach to control such complex systems is the adoption of a central unit (centralized strategy), which may coincide with one teammate, able to collect the overall system information and compute the control input for all the agents. However, limited communication capabilities, for example in the case of robotic vehicles with a short communication range or a limited bandwidth, and limited computational power could make impossible the communication among all the agents and the adoption of a centralized strategy. Moreover, the central unit may represent a weak point for system reliability, since its failure may compromise the whole mission. Most recent approaches for controling these systemsl mainly focus on distributed or decentralized techniques for networked robots, where each robot has limited sensing and communication ranges and it can only use data from its onboard sensors or information received from its direct neighbours. In this case, the main control problem that has been faced concerns how to move the single robots using only local information and in order to achieve global tasks that may depend on the overall system configuration.

Fault detection and accomodation of distributed multi-robot systems

Thus, most recent research in the field of multi-robot systems focuses on the development of decentralized control strategies where each robot computes its own motion control only based on local information and on information from a subset of its teammates, usually named neighbors. Graph theoretical methods, that allow extracting analytical properties of the system on the base of the connectivity properties of the communication network formed by the agents, are often used to the purpose. In this context, one of the most popular problem is the consensus, that consists in reaching an agreement regarding a specific variable. The intrinsic redundancy of networked robots may allow to accomplish the assigned mission also in the case of failure of one or more teammates. Nevertheless, when dealing with distributed control of multi-robot systems aimed at achieving a global task, this feature, without the explicit adoption of a suitable Fault Detection and Isolation (FDI) schema, is only potential and the fault of one robot may jeopardize the proper execution of the task. In this framework, Distributed FDI algorithms for networked robots that allows each robot of the team to detect faults occurring on any other robot, even if not directly connected have been developed; this can be used in the case where the control input of each robot depends on the overall state of the system or on an estimate of it.