The performance sensitivity of linear systems, with stable feedback perturbations both on the plant and the feedback controller, is discussed. Using - norm, the sufficient condition for the robust stability is derived. Also, the upper bounds for the sensitivity function matrix and the closed-loop transfer function matrix of the perturbed system are presented.
A text surveying perturbation techniques and sensitivity analysis of linear systems is an ambitious undertaking, considering the lack of basic comprehensive texts on the subject. A wide-ranging and global coverage of the topic is as yet missing, despite the existence of numerous monographs dealing with specific topics but generally of use to only a narrow category of people. In fact, most works approach this subject from the numerical analysis point of view. Indeed, researchers in this field have been most concerned with this topic, although engineers and scholars in all fields may find it equally interesting. One can state, without great exaggeration, that a great deal of engineering work is devoted to testing systems' sensitivity to changes in design parameters. As a rule, high-sensitivity elements are those which should be designed with utmost care. On the other hand, as the mathematical modelling serving for the design process is usually idealized and often inaccurately formulated, some unforeseen alterations may cause the system to behave in a slightly different manner. Sensitivity analysis can help the engineer innovate ways to minimize such system discrepancy, since it starts from the assumption of such a discrepancy between the ideal and the actual system.
This book provides a thorough review and explanation of the theory of stochastic max-plus linear systems, which has seen rapid advances in the last decade. The coverage includes modeling issues and stability theory for stochastic max-plus systems, perturbation analysis of max-plus systems, developing a calculus for differentiation of max-plus systems. This leads to numerical evaluations of performance indices of max-plus linear stochastic systems, such as the Lyapunov exponent or waiting times.
Sensitivity Methods in Control Theory is a collection of manuscripts presented as the Third International Symposium of Sensitivity Analysis, held at Dubrovnik, Yugoslavia on August 31-September 5, 1964, sponsored by The Theory Committee of International Federation of Automatic Control. Sensitivity theory or sensitivity analysis concerns the solution of problems associated with parameter variations within the general scope of control theory. This book is organized into five parts encompassing 30 chapters. Part I presents some basic approaches of sensitivity analysis, such the Lyapunov's theory of stability, invariant imbedding, nonlinear sampled data, and linear time-varying systems. This part also looks into the preliminary steps towards the development of game theory and some general applications of sensitivity analysis. Part II treats the problem of accuracy, reliability, self-adjustment, and optimization of sensitivity of automatic control systems, while Part III deals with the functional derivative technique of sensitivity analysis and its applications for designing self-adjusting control systems. Part IV describes the task of synthesizing control systems for linear plants with variable parameters satisfying specified performance criteria. Part V considers the association between sensitivity and optimality in various control systems. This book will prove useful to design and other specialized fields in engineering.
This book provides a thorough review and explanation of the theory of stochastic max-plus linear systems, which has seen rapid advances in the last decade. The coverage includes modeling issues and stability theory for stochastic max-plus systems, perturbation analysis of max-plus systems, developing a calculus for differentiation of max-plus systems. This leads to numerical evaluations of performance indices of max-plus linear stochastic systems, such as the Lyapunov exponent or waiting times.
This book provides a thorough review and explanation of the theory of stochastic max-plus linear systems, which has seen rapid advances in the last decade. The coverage includes modeling issues and stability theory for stochastic max-plus systems, perturbation analysis of max-plus systems, developing a calculus for differentiation of max-plus systems. This leads to numerical evaluations of performance indices of max-plus linear stochastic systems, such as the Lyapunov exponent or waiting times.
