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A Hierarchical control system is a form of Control System, often a Networked control system, in which set of devices and governing software, are arranged in a tree-shaped hierarchy.
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[edit] Introduction
A human-built system with complex behavior is often organized as a hierarchy. For example a command hierarchy has among its notable features the organizational chart of superiors, subordinates, and lines of organizational communication. Hierarchical control systems are organized similarly to divide the decision making responsibility. Each element of the hierarchy is referred to here as a node. Commands, tasks and goals to be achieved flow down the tree from superior nodes to subordinate nodes, whereas sensations and command results flow up the tree from subordinate to superior nodes. Nodes may also exchange messages with their siblings. The two distinguishing features of a hierarchical control system are related to its layers.
- Each higher layer of the tree operates with a longer interval of planning and execution time than its immediately lower layer.
- The lower layers have local tasks, goals, and sensations, and their activities are planned and coordinated by higher layers which do not generally override their decisions. A hierarchical task network is a good fit for planning in a hierarchical control system.
[edit] Application in robotics and automated vehicles
Among the robotic paradigms is the hierarchical paradigm in which a robot operates in a top-down fashion, heavy on planning, especially motion planning. Automated manufacturing has been a research focus at NIST since the 1980's. Its Automated Manufacturing Research Facility was used to develop a five layer production control model. In the early 1990's DARPA sponsord research to develop distributed (i.e. networked) intelligent control systems for applications such as military command and control systems. NIST built on earlier research to develop its Realtime Control System (RCS) which is a generic hierarchical control system that has been used to operate a manufacturing cell, a robot crane, and an automated vehicle.
[edit] Application in artificial intelligence
Subsumption architecture is a methodology for developing artificial intelligence that is heavily associated with behavior based robotics. This architecture s a way of decomposing complicated intelligent behavior into many "simple" behavior modules, which are in turn organized into layers. Each layer implements a particular goal of the agent (i.e node), and higher layers are increasingly more abstract. Each layer's goal subsumes that of the underlying layers, e.g. the decision to move forward by the eat-food layer takes into account the decision of the lowest obstacle-avoidance layer.
Reinforcement learning has been used to acquire behavior in a hierarchical control system in which each node can learn to improve its behavior with experience.
James Albus, while at NIST, developed a theory for intelligent system design. Albus defines each node to contain these components.
- Behavior generation is responsible for executing tasks received from the superior, parent node. It also plans for, and issues tasks to, the subordinate nodes.
- Sensory perception is responsible for receiving sensations from the subordinate nodes, then grouping, filtering, and otherwise processing them into higher level abstractions that update the local state and which form sensations that are sent to the superior node.
- Value judgment is responsible for evaluating the updated situation and evaluating alternative plans.
- World Model is the local state that models the controlled system, controlled process, or environment at the abstraction level of the subordinate nodes.
[edit] References
- Albus, J. S. A Reference Model Architecture for Intelligent Systems Design. In Antsaklis, P.J., Passino, K.M. (Eds.) (1993) An Introduction to Intelligent and Autonomous Control. Kluwer Academic Publishers, 1993, Chapter 2, pp27-56. ISBN 0-7923-9267-1
- Albus, J. S. The engineering of mind. In Pattie Maes, Maja j. Mataric, JeanArcady Meyer, Jordan B. Pollack, and Stewart W. Wilson, editors, Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pages 23--32. MIT Press, 1996.
- Albus, J. S. 4-D/RCS reference model architecture for unmanned ground vehicles. In G Gerhart, R Gunderson, and C Shoemaker, editors, Proceedings of the SPIE AeroSense Session on Unmanned Ground Vehicle Technology, volume 3693, pages 11--20, Orlando, FL, April 1999.
- Findeisen, W., Bailey, F. N., Bryds, M., Malinowski, K, Tatjewski, P., and Wozniak, A, Control and Coordiation in Hierarchical Systems, John Wiley & Sons, New York, 1980
- Hayes-Roth, F., Erman, L. D., Terry, A., and Hayes-Roth, B., Distributed Intelligent Control and Management (DICAM) Applications and Support for Semi-Automated Development. In Proceedings of AAAI-92 Workshop on Automating Software Development, San Jose, CA, 1992.
- Kumar, R., and Stover, J. A. . A behavior-based intelligent control architecture. In IEEE International Symposium on Intelligent Control, pages 549--553, Gaithersburg, MD, September 1998.
- Jones, A. T., and McLean, C. R., A Proposed Hierarchical Control Model for Automated Manufacturing Systems In Journal of Manufacturing Systems, Vol. 5, No. 1, p. 15, 1986.
- Takahashi, Y. , and Asada, M., Behavior Acquisition by Multi-Layered Reinforcement Learning. In Proceeding of the 1999 IEEE International Conference on Systems, Man, and Cybernetics, pages 716-721
[edit] External links
- The RCS (Realtime Control System) Libary
- Texai] An open source project to create artificial intelligence using an Albus hierarchical control system
Category:Control engineering Category:Control theory Category:Artificial intelligence
