TAILIEUCHUNG - QUASILINEAR CONTROL: Performance Analysis and Design of Feedback Systems with Nonlinear Sensors and Actuators

This volume is devoted to the study of feedback control of so-calledlinear plant/nonlinear instrumentation(LPNI) systems. Such systems appear naturally in situations where the plant can be viewed as linear but the instrumentation, that is, actuators and sensors, can not. For instance, when a feedback system operates effectively and maintains the plant close to a desired operating point, the plant may be linearized, but the instrumentation may not, because to counteract large perturbations or to track large reference signals, the actuator may saturate and the nonlinearities in sensors, for example, quantization and dead zones, may be activated | QUASILINEAR CONTROL Performance Analysis and Design of Feedback Systems with Nonlinear Sensors and Actuators This is a textbook on quasilinear control QLC . QLC is a set of methods for performance analysis and design of linear plant nonlinear instrumentation LPNI systems. The approach of QLC is based on the method of stochastic linearization which reduces the nonlinearities of actuators and sensors to quasilinear gains. Unlike the usual - Jacobian linearization -stochastic linearization is global. Using this approximation QLC extends most of the linear control theory techniques to LPNI systems. In addition QLC includes new problems specific for the LPNI scenario. Examples include instrumented LQR LQG in which the controller is designed simultaneously with the actuator and sensor and partial and complete performance recovery in which the degradation of linear performance is either contained by selecting the right instrumentation or completely eliminated by the controller boosting. ShiNung Ching is a Postdoctoral Fellow at the Neurosciences Statistics Research Laboratory at MIT since completing his . in electrical engineering at the University of Michigan. His research involves a systems theoretic approach to anesthesia and neuroscience looking to use mathematical techniques and engineering approaches - such as dynamical systems modeling signal processing and control theory - to offer new insights into the mechanisms of the brain. Yongsoon Eun is a Senior Research Scientist at Xerox Innovation Group in Webster New York. Since 2003 he has worked on a number of subsystem technologies in the xerographic marking process and image registration technology for the inkjet marking process. His interests are control systems with nonlinear sensors and actuators cyclic systems and the impact of multitasking individuals on organizational productivity. Cevat Gokcek was an Assistant Professor of Mechanical Engineering at Michigan State University. His research in the Controls .

• Tự động hoá
• Stochastic Linearization
• Analysis of Reference
• Analysis of Disturbance
• Design of Reference
• Design of Disturbance
• Vietnam Journal of Mechanics
• Weighted dual criterion for stochastic equivalent linearization method
• Piecewise linear functions
• The normalized weighting coefficient
• The least square method
• Frequency response function
• Iteration method
• Mean square
• Stochastic linearization for nonlinear oscillators
• Global local mean square error criterion
• stochastic growth model
• representative firm
• goods market
• steady state
• Linearization
• the balanced growth path
• linearized model
• Impulse response functions
• Stats Practically
• growth model
• Linearization around
• balanced growth path
• Fractional order control system
• FOPID controller
• Disturbance rejection control
• Computer aided optimal controller design
• Disturbance rejection FOPID controller design
• Fractional order control
• 2DOF controller design
• Disturbance rejection
• Improved consensus oriented random
• PIDA controllers according