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Three problem areas associated with the design of linear sampled-data systems are considered. The first arises from having the transition and distribution matrices of the system be random variables, i.e., the random parameter problem; the second from having multiplicative noise at the input to the system, this being a special case of the first problem area; and the third from being unable to measure the state vector of the system exactly. In each of these 3 areas, the performance of the system is measured by using either a generalized sum-squared-error, a final-value, or a minimum-time criterion. The design procedures are based either upon minimizing the expected value of the performance index or upon minimiz'ng the performance index in the presence of worst-case variations within the system, e.g., minimizing the expected value of the sumsquared-error. In general, the results are in the form of feedback coefficients which relate the value of the optimum input to the value of the state vector of the system. (Author).
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In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; andmethods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory.As a result, the book represents a blend of new methods in general computational analysis,and specific, but also generic, techniques for study of systems theory ant its particularbranches, such as optimal filtering and information compression.- Best operator approximation,- Non-Lagrange interpolation,- Generic Karhunen-Loeve transform- Generalised low-rank matrix approximation- Optimal data compression- Optimal nonlinear filtering
Advances in Control Systems: Theory and Applications, Volume 6 provides information pertinent to the significant progress in the field of control and systems theory and applications. This book presents the higher level of automata, which represent the embodiment of the application of artificial intelligence techniques to control system design and may be described as self-organizing systems. Organized into four chapters, this volume begins with an overview of the existing technology in learning control system. This text then demonstrates how to apply artificial intelligence techniques to the designs of off-line and on-line learning control systems. Other chapters consider the decomposition methods and the associated multilevel optimization techniques applicable to control system optimization problems. This book discusses as well the complex optimal system control problems applied to the trajectory optimization problem. The final chapter deals with systems described by partial differential equations. This book is a valuable resource for control system engineers.
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