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Chapter and Conference Paper
On optimal decentralized control
In this paper, decentralized robust stabilization and performance of two-channel interconnected systems are studied. Necessary and sufficient conditions for decentralized robust stability and robust performanc...
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Chapter and Conference Paper
Optimal hybrid control with applications to automotive powertrain systems
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Chapter and Conference Paper
Hybrid control of automotive powertrain systems: A case study
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Chapter and Conference Paper
Robust Control of Hybrid Systems: Performance Guided Strategies
In this paper, the problem of robust control of hybrid systems is investigated. The hybrid systems under study are characterized as systems whose inputs contain both analog variables and discrete actions and w...
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Article
Stochastic Prediction of Execution Time for Dynamic Bulk Synchronous Computations
We consider the problem of execution time prediction for non-deterministic multi-phase bulk synchronous computations in multiprocessors. We characterize the computations in two stochastic workload evolution mo...
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Article
Identification Error Bounds and Asymptotic Distributions for Systems with Structural Uncertainties
This work is concerned with identification of systems that are subject to not only measurement noises, but also structural uncertainties such as unmodeled dynamics, sensor nonlinear mismatch, and observation b...
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Article
Information Characterization of Communication Channels for System Identification
This paper studies identification of systems in which the system output is quantized, transmitted through a digital communication channel, and observed afterwards. The concept of the CR Ratio is introduced to ...
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Article
Recursive estimation algorithms for power controls of wireless communication networks
Power control problems for wireless communication networks are investigated in direct-sequence code-division multiple-access (DS/CDMA) channels. It is shown that the underlying problem can be formulated as a c...
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Book
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Chapter
System Settings
This chapter presents basic system structures, sensor representations, input types and characterizations, system configurations, and uncertainty types for the entire book. This chapter provides a problem formu...
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Chapter
Worst-Case Identification Using Quantized Observations
In this chapter, the parameter identification problem under unknown-butbounded disturbances and quantized output sensors is discussed. In Chapter 9, an input sequence in (9.5) was used to generate observation ...
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Chapter
Estimation Error Bounds: Including Unmodeled Dynamics
In Chapter 3, we derived convergent estimators of the system parameters using binary-valued observations. Our aim here is to obtain further bounds on estimation errors from unmodeled dynamics. In this book, un...
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Chapter
Identification of Hammerstein Systems with Quantized Observations
This chapter concerns the identification of Hammerstein systems whose outputs are measured by quantized sensors. The system consists of a memoryless nonlinearity that is polynomial and possibly noninvertible, ...
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Chapter
Quantized Identification and Asymptotic Efficiency
Up to this point, we have been treating binary-valued observations. The fundamental principles and basic algorithms for binary-valued observations can be modified to handle quantized observations as well. One ...
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Chapter
Space and Time Complexities, Threshold Selection, Adaptation
The number m0 of thresholds is a measure of space complexity, whereas the observation length N is a measure of time complexity that quantifies how fast uncertainty can be reduced. The significance of understandin...
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Chapter
Identification of Sensor Thresholds and Noise Distribution Functions
The developments in the early chapters rely on the knowledge of the distribution function F· or its inverse, as well as the threshold C. However, in many applications, the noise distributions are not known, or on...
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Chapter
Introduction
This book studies the identification of systems in which only quantized output observations are available. The corresponding problem is termed quantized identification.
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Chapter
Identification of Wiener Systems with Binary-Valued Observations
This chapter studies the identification of Wiener systems whose outputs are measured by binary-valued sensors. The system consists of a linear FIR (finite impulse response) subsystem of known order, followed b...
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Chapter
Rational Systems
The systems in Chapters 3 and 4 are finite impulse-response models. Due to nonlinearity in output observations, switching or nonsmooth nonlinearity enters the regressor for rational models. A common technique ...
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Chapter
Input Design for Identification in Connected Systems
Input design is of essential importance in system identification to provide sufficient probing capabilities to guarantee the convergence of parameter estimators to their true values; namely, the estimators are...
