Modern engineering systems are very complex and comprise a high number of interconnected sub-components which, thanks to the remarkable development of communications and electronics, can be spread over broad areas and linked through data networks. The control of those complex sys- tems can only be achieved in a decentralized mode, by appropriately designing local controllers for each individual component or small group of components. Control and computational capabilities being distributed over the system, a steady exchange of data among the components is required, in order for the system to behave properly. While control, information theory and communication are to some extent mature disciplines, little effort has been put so far in understanding how issues in information theory and communication constraints affect the performance of a large-scale control system distributed over a communication network. The simplest case in which this situation may occur is when the control functions are performed by a local (decentralized) controller, situated at the same physical location of the controlled plant, and by a remote (centralized) controller, situated at a different location. Typical, in this scenario, is the situation in which a subsystem is required to perform a task which depends on the state of a system residing at a remote physical location. In this case, the local controller must embed some mechanism of prediction of the tasks to be performed which, depending on the states of remote subsystems, may be quite uncertain. The ability of successfully handle large uncertainties is one of the main, if not the single most important, reasons of being of automatic control. Little attention, though, has been paid so far to the fact that uncertainties may also affect the task that the control system is asked to accomplish. One of the main intellectual purposes of the proposed research activity is to show how problems in which control tasks are uncertain can be successfully addresses by embedding, into the controller itself, a tunable internal model of the systems on which the tasks to be performed may depend. The other main intellectual purpose is to show how this design philosophy is particularly suited to the control of large complex systems distributed over communication networks. In fact, the role of an internal model is that of generating, in real time, the precise feed-forward input which would be required to perform the actual control task. If the local controller contains an internal model of those tasks, the role of the remote controller would only be that of resetting (once in a while, possibly using little amount of information) the state of the internal model on the basis of the actual value of the regulated variable. This would provide a design philosophy which would much better cope with restrictions typical of a control system distributed over a communication network, such as bandwidth constraints, delays, loss of packets. Progresses in internal-model-based control design are expected to have a broad impact in var- ious areas of control technology. Experimental results have already shown, for instance, how self a tuned internal-model-based controller can automatically compensate for faults occurring in a ro- tating electrical machine, and the benefits which may derive to manufacturing industry from this specific application are quite clear. Another broad area of interest is that of satellite control for telecommunications. Typical problems occurring in this area are to have a communication satellite tracking a smaller, beacon satellite at a fixed distance in a circular orbit, or to have networks of small satellites ying in coordinated motion. Another application, in which preliminary simulations have shown impressive results, is the design of an autopilot to assist the landing of a rescue heli- copter on a ship in distress in high seas. Similar applications are automatic ship loading/unloading in adverse atmospheric conditions or remote control of submarine vehicles for automatic undersea cable or pipeline inspection.
