Naresh K. Sinha

Bio: Naresh K. Sinha is an academic researcher from McMaster University. The author has contributed to research in topics: Adaptive control & Linear system. The author has an hindex of 24, co-authored 192 publications receiving 5195 citations.

Modern Control Systems

Abstract: From the Publisher: For more than twenty years, Modern Control Systems has set the standard of excellence for undergraduate control systems textbooks. It has remained a bestseller because Richard Dorf and Robert Bishop have been able to take complex control theory and make it exciting and accessible to students. The book presents a control engineering methodology that, while based on mathematical fundamentals, stresses physical system modeling and practical control system designs with realistic system specifications.

Short-Term Load Demand Modeling and Forecasting: A Review

Abstract: Both the off-line and on-line methods for short-term electric load forecasting are reviewed. Since identifying an adequate model is the most important problem of any forecasting technique, the literature is classified according to the modeling approaches used for representing the load demand. The merits and drawbacks of each approach and how different authors have applied it to the problem under consideration are stressed. Also included is recent work where multivariable identification techniques are used to model the load demand of all the major loading nodes of a large power system. The advantages and the difficulties of applying such techniques are discussed. As a conclusion, directions for future research and future development of available algorithms, which can improve the state of the art, are suggested.

Soft Computing and Intelligent Systems: Theory and Applications

Abstract: From the Publisher: Invited researchers from around the world discuss the principles of and their experience with soft computing, an emerging discipline rooted in a group of technologies that aim to exploit the tolerance for imprecision and uncertainty in achieving solutions to complex problems. Within the broad categories of foundations, theory, implications and applications, and future prospects, the 25 studies consider such topics as parallel and distributed architectures and biologically inspired computing, neural networks for identifying nonlinear systems, the knowledge- based adaptation of a neurofuzzy model in the predictive control of a heat exchanger, and toward intelligent machines. The field is so new that the bibliography claims comprehension.

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Control System Design

Abstract: From the Publisher: A key aspect of the book is the frequent use of real world design examples drawn directly from the authors' industrial experience. These are represented by over 15 substantial case studies ranging from distillation columns to satellite tracking. The book is also liberally supported by modern teaching aids available on both an accompanying CD-ROM and Companion Website. Resources to be found there include MATLAB® routines for all examples; extensive PowerPoint lecture notes based on the book; and a totally unique Java Applet-driven "virtual laboratory" that allows readers to interact with the real-world case studies.

Time-Optimal Control of Robotic Manipulators Along Specified Paths:

Abstract: The minimum-time manipulator control problem is solved for the case when the path is specified and the actuator torque limitations are known. The optimal open-loop torques are found, and a method is given for implementing these torques with a conventional linear feedback control system. The algorithm allows bounds on the torques that may be arbitrary functions of the joint angles and angular velocities. This method is valid for any path and orientation of the end- effector that is specified. The algorithm can be used for any manipulator that has rigid links, known dynamic equations of motion, and joint angles that can be determined at a given position on the path.