This project is centered around the idea of incorporating general implementation constraints and requirements in the theory of robust controller design. One aspect is the design of sampled-data controllers with continuous-time performance objectives (hybrid systems), specifically, the design and analysis of single- and multirate control systems in the l ¹ and H ^Infin. norms. Among the issues considered are design algorithms and nonconservative conditions for robustness in time-invariant, time-varying, and/or nonlinear unmodeled dynamics. The second aspect is to develop systematic and computable methods for the design of low-order controllers, through various types of model reduction in conjunction with robust stability and closed-loop performance analysis.

Advances in communications and computer engineering and in solid-state sensor technology provide the opportunity for distributed information (signal) processing and decentralized decision making in large-scale systems operating in dynamic uncertain environments. Typical applications for such a paradigm are data acquisition and signal processing from multiple sensors as in surveillance systems, networks of computers and human decision makers, and smart materials utilizing integrated sensors, actuators, and controllers. The principal objective is to address some fundamental issues in modeling, optimization, and decision making in the context of distributed and decentralized systems, operating in a decentralized information environment.

This project seeks a new approach to designing complex systems in which advanced techniques are integrated to produce ``intelligent'' systems of superior performance in the presence of large uncertainties and stringent specifications. The goal is to translate high-level commands or specifications automatically into lower level actions on the environment or plant, while fully utilizing any prior information as well as information contained in the real-time environmental responses. Multilayer decision models for control of subsystems with conflicting objectives, decentralized control, and robust and adaptive control approaches will be developed.

This project involves fundamental research on the modeling, control, and optimization of large-scale systems. It encompasses both linear and nonlinear models, deterministic and stochastic systems with external and internal uncertainty, systems with weak spatial and weak or strong informational links, and dynamic decision models with multiple criteria. The overall goal is the development of new and effective methodologies for robust control, stabilization and optimization of large-scale systems in the presence of static as well as dynamic uncertainty, and the analysis of such systems using concepts of multimodeling, decomposition, and aggregation.

This project is aimed at developing a comprehensive time-domain-based theory for the analysis and synthesis of performance-robust minimax controllers and identifiers for nonlinear systems subject to deterministic and/or partially stochastic disturbances. Performance-robustness is a property that is imposed on top of minimaxity and requires the selected controller to be least sensitive to modeling inaccuracies from neglected fast dynamics and/or weak coupling of subsystems. Dynamic or differential game theory is used to obtain fundamental results on zero-sum and nonzero-sum differential games. We will also explore the relationship with stochastic control problems with exponentiated cost, again from a performance-robustness point of view.

This research addresses the problem of designing efficient scheduling policies to reduce the mean and variance of cycle-time. Comprehensive comparative testing of policies on realistic fabrication models is planned. We also address the problem of performance evaluation of queueing networks, which arise not only in semiconductor manufacturing systems, but also in communication networks and computer systems. Questions of the following type are addressed: Given a system description, in terms of the number of servers, their up and down time statistics, the description of the various flows, and parameters such as throughput rates, routes, and processing times at each server, how does one predict the performance of the system?

The goal of this project is to develop an applicable theory for analysis and control of manufacturing systems. Manufacturing systems are composed of a complex interaction of machines and parts. The systems are typically large scale and subject to disruptions such as machine failures. The goal is to control or schedule these systems efficiently to achieve optimal performance in terms of mean manufacturing lead time, variance, ability to meet due dates, cost of work in process, and shortfall costs. The issues are: How does a specific scheduling policy perform? and How does one synthesize good scheduling policies?

The goal of this research is to develop new methods for performance evaluation that can provide bounds on the delay and establish the throughput. This approach provides analytical alternatives to traditional modes of analysis by simulation or approximation.

In this project we consider scheduling policies for large manufacturing systems and the dynamics of these systems under the influence of random breakdowns, fluctuations in demand and yield, and changes in operating conditions.

We consider generalizations of the least squares algorithm for identifying time-varying systems and the performance of adaptive control schemes based upon these estimation algorithms. These controllers are currently being implemented on an arc welder at the U.S. Army Construction Engineering Research Laboratory.