13.1.   INTRODUCTION

Today, the role of a control algorithm is evolving from static designs,
synthesized offline, to dynamic algorithms that adapt in real-time to changes
in the controlled system and its environment. The paradigm for control
system design and implementation is also shifting from a centralized, single
processor framework to a decentralized, distributed processor implementation
framework. Distribution and decentralization of services and components
is driven by the falling cost of hardware, increasing computational
power, increasingly complex control algorithms, and development of new,
low-cost micro sensors and actuators. A distributed, modular hardware architecture
offers the potential benefit of being highly reconfigurable, faulttolerant,
and inexpensive. Modularity can also accelerate development time
of products, since groups can work in parallel on individual system components.
These benefits come with a price - that is, the need for sophisticated,
reliable software to manage the distributed collection of components and
tasks.

Communication within a distributed, decentralized environment becomes
a significant issue. Hardware components and software processes operate
in synchronous and asynchronous modes. These processes have to communicate
with one another with a well-defined protocol to effectively control the system.
Software tools needed for a distributed, real-time control architecture
include: real-time execution, adaptive task scheduling, task synchronization,
communication protocols and adaptive resource management. Hence software
and its interaction with the controlled system will play a significantly
larger role in the control of real-time systems. This drive toward distributed,
dynamic control systems requires establishment of tighter ties between the
controls and computer science communities for these systems to be successful.
DARPA initiated the Software Enabled Control (SEC) program, in part,
to address these issues.

A central theme of the DARPA SEC program is to develop software-based
control technologies that use dynamic information about the controlled
system to adapt, in real time, to changes in the subsystems and its operating
environment. This software should be flexible to reconfiguration, allocate
sensor capability and actuator control authority, achieve stable and robust
operation across complex mode transitions, integrate and coordinate subsystem
operations, and enable large-scale distribution of control [1]. The open
controls platform 9OCP) [2], being developed by Boeing St. Louis, Honeywell
Laboratories, and the Georgia Institute of Technology under the DARPA
SEC program, provides a software infrastructure to enable control engineers
to work seamlessly, in real time and simulation time, within a distributed
control environment. The OCP software is built upon RT-CORBA and is an
extension to the Bold Stroke software architecture developed by Boeing
St. Louis to support aircraft avionics system integration. The OCP is middleware
that consists of a set of services that allow multiple processes running
on one or more machines interacting across a network.