Description
John Wiley Microcontroller Based Applied Digital Control by Dogan Ibrahim
Combines the theory and the practice of applied digital control This book presents the theory and application of microcontroller based automatic control systems. Microcontrollers are single-chip computers which can be used to control real-time systems. Low-cost, single chip and easy to program, they have traditionally been programmed using the assembly language of the target processor. Recent developments in this field mean that it is now possible to program these devices using high-level languages such as BASIC, PASCAL, or C. As a result, very complex control algorithms can be developed and implemented on the microcontrollers. Presenting a detailed treatment of how microcontrollers can be programmed and used in digital control applications, this book: Introduces the basic principles of the theory of digital control systems. Provides several working examples of real working mechanical, electrical and fluid systems. Covers the implementation of control algorithms using microcontrollers. Examines the advantages and disadvantages of various realization techniques. Describes the use of MATLAB in the analysis and design of control systems.Explains the sampling process, z-transforms, and the time response of discrete-time systems in detail. Practising engineers in industry involved with the design and implementation of computer control systems will find Microcontroller Based Applied Digital Control an invaluable resource. In addition, researchers and students in control engineering and electrical engineering will find this book an excellent research tool. Preface. 1. The need for more than one random-effect term when fitting a regression line. 2. The need for more than one random-effect term in a designed experiment. 3. Estimation of the variances of random-effect terms. 4. Interval estimates for fixed-effect terms in mixed models. 5. Estimation of random effects in mixed models: best linear unbiased predictors. 6. More advanced mixed models for more elaborate data sets. 7. Two case studies. 8. The use of mixed models for the analysis of unbalanced experimental designs. 9. Beyond mixed modelling. 10. Why is the criterion for fitting mixed models called residual maximum likelihood? References. Index.