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2013
This chapter presents a summary of some classical results on robustness analysis of linear dynamical systems. The chapter includes a discussion of deterministic and stochastic signals, linear time-invariant systems in state space form, linear matrix inequalities, and characterization of the \(\mathcal{H}_{2}\) and \(\mathcal{H}_{\infty}\) norms.
Roberto Tempo +2 more
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This chapter presents a summary of some classical results on robustness analysis of linear dynamical systems. The chapter includes a discussion of deterministic and stochastic signals, linear time-invariant systems in state space form, linear matrix inequalities, and characterization of the \(\mathcal{H}_{2}\) and \(\mathcal{H}_{\infty}\) norms.
Roberto Tempo +2 more
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Sensitivity controller for uncertain systems
Journal of Guidance, Control, and Dynamics, 1988In this paper a new controller design, which we Shall call the "trajectory sensitivity optimization" method, is presented to improve the robustness for parameter variations. The method uses the sensitivity trajectory to model the parameter uncertainty and introduces a special quadratic cost function involving an input and output sensitivity term ...
KENJI OKADA, ROBERT SKELTON
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Deconvolution for uncertain systems
2009 6th International Symposium on Mechatronics and its Applications, 2009The degradation of signals and images can be caused by both natural perturbations and electronic systems, recording linear systems, in which parameters are slowly time-varying such as sensors or other systems of storage. Treatment of the above mentioned systems are discussed. For this purpose, Sekko & al.
Soraya Zenati, Abdelhani Boukrouche
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Stabilization of uncertain systems
1981 20th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, 1981Structural properties necessary to guarantee the existence of a stabilizing feedback control law for systems described by a set of linear time-invariant differential equations with bounded but unknown time-varying uncertanties are investigated. A monotone mapping theorem is used to explore the relation between an optimal regulator problem with and ...
Robert Thomas +2 more
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2019
In this chapter, we are going to discuss about the uncertain linear system that is involved with any uncertainties. First of all, it should be mentioned that an uncertain linear system is indeed an extension of interval linear system. This is why, we will discuss about the systems involving interval analysis approach.
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In this chapter, we are going to discuss about the uncertain linear system that is involved with any uncertainties. First of all, it should be mentioned that an uncertain linear system is indeed an extension of interval linear system. This is why, we will discuss about the systems involving interval analysis approach.
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2013
As discussed in Chap. 1 it is well understood that uncertainties are unavoidable in a real control system. The uncertainty can be classified into two categories: disturbance signals and dynamic perturbations. The former includes input and output disturbance (such as a gust on an aircraft), sensor noise and actuator noise, etc. The latter represents the
Da-Wei Gu +2 more
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As discussed in Chap. 1 it is well understood that uncertainties are unavoidable in a real control system. The uncertainty can be classified into two categories: disturbance signals and dynamic perturbations. The former includes input and output disturbance (such as a gust on an aircraft), sensor noise and actuator noise, etc. The latter represents the
Da-Wei Gu +2 more
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2009
Novel direct adaptive robust state and output feedback controllers are presented for the output tracking control of a class of nonlinear systems with unknown system dynamics and disturbances. Both controllers employ a variable-structure radial basis function (RBF) network that can determine its structure dynamically to approximate unknown system ...
Jianming Lian, Stanislaw H. Żak
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Novel direct adaptive robust state and output feedback controllers are presented for the output tracking control of a class of nonlinear systems with unknown system dynamics and disturbances. Both controllers employ a variable-structure radial basis function (RBF) network that can determine its structure dynamically to approximate unknown system ...
Jianming Lian, Stanislaw H. Żak
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Compensability of uncertain systems
29th IEEE Conference on Decision and Control, 1990The uncertainty of linear discrete-time systems with white stochastic parameters is considered in relation with the properties of mean-square stability and the ability to provide compensation. A measure for the uncertainty in the system matrices is introduced.
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Archives of Neurology, 1969
This well-written book by the Head of the Division of Physiology and Pharmacology of the (British) National Institutes of Medical Research examines the current status and explores possible future trends of the study of the central nervous system as brain.
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This well-written book by the Head of the Division of Physiology and Pharmacology of the (British) National Institutes of Medical Research examines the current status and explores possible future trends of the study of the central nervous system as brain.
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Estimation of Uncertain Systems
1973Publisher Summary This chapter describes the state estimation problem with parameter uncertainties. The Kalman-Bucy filter gives the unbiased, minimum variance estimate of the state vector of a linear dynamic system that is disturbed by additive white noise when measurements of the state vector are linear, but disturbed by white noise.
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