Control Systems

[tabs bg=”” border=”” color=””] [tab title=”Syllabus”]

UNIT – I INTRODUCTION:
Concepts of Control Systems- Open Loop and closed loop control systems and their differences- Differentexamples of control systems- Classification of control systems, Feed-Back Characteristics, Effects of feedback. Mathematical models – Differential equations, Impulse Response and transfer functions – Translational andRotational mechanical systems

UNIT II TRANSFER FUNCTION REPRESENTATION:
Transfer Function of DC Servo motor – AC Servo motor- Synchro transmitter and Receiver, Block diagramrepresentation of systems considering electrical systems as examples -Block diagram algebra –Representation by Signal flow graph – Reduction using mason’s gain formula.

UNIT-III TIME RESPONSE ANALYSIS:
Standard test signals – Time response of first order systems – Characteristic Equation of Feedback control systems, Transient response of second order systems – Time domain specifications – Steady state response- Steady state errors and error constants – Effects of proportional derivative, proportional integral systems.

UNIT – IV STABILITY ANALYSIS IN S-DOMAIN:
The concept of stability – Routh’s stability criterion – qualitative stability and conditional stability – limitationsof Routh’s stability
Root Locus Technique:
The root locus concept – construction of root loci-effects of adding poles and zeros to G(s)H(s) on the rootloci.

UNIT- V FREQUENCY RESPONSE ANALYSIS:
Introduction, Frequency domain specifications-Bode diagrams-Determination of Frequency domainspecifications and transfer function from the Bode Diagram-Phase margin and Gain margin-Stability Analysisfrom Bode Plots.

UNIT-VI STABILITY ANALYSIS IN FREQUENCY DOMAIN:
Polar Plots, Nyquist Plots Stability Analysis.

UNIT – VII CLASSICAL CONTROL DESIGN TECHNIQUES:

UNIT – VIII State Space Analysis of Continuous Systems:
Concepts of state, state variables and state model, derivation of state models from block diagrams,Diagonalization- Solving the Time invariant state Equations- State Transition Matrix and it’s Properties –Concepts of Controllability and Observability.

[/tab] [tab title=”Videos”]

Speech recognition technology is used more and more for telephone applications like travel booking and information, financial account information, customer service call routing, and directory assistance. Using constrained grammar recognition, such applications can achieve remarkably high accuracy. Research and development in speech recognition technology has continued to grow as the cost for implementing such voice-activated systems has dropped and the usefulness and efficacy of these systems has improved. For example, recognition systems optimized for telephone applications can often supply information about the confidence of a particular recognition, and if the confidence is low, it can trigger the application to prompt callers to confirm or repeat their request. Furthermore, speech recognition has enabled the automation of certain applications that are not automatable using push-button interactive voice response (IVR) systems, like directory assistance and systems that allow callers to “dial” by speaking names listed in an electronic phone book.

Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the supra-segmental features (voice source characteristics and dynamic features spanning several segments). The most common short-term spectral measurements currently used are Linear Predictive Coding (LPC)-derived cepstral coefficients and their regression coefficients. A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients.

Therefore it provides a stabler representation from one repetition to another of a particular speaker’s utterances. As for the regression coefficients, typically the first- and second-order coefficients are extracted at every frame period to represent the spectral dynamics. These coefficients are derivatives of the time functions of the cepstral coefficients and are respectively called the delta- and delta-delta-cepstral coefficients.

[/tab] [tab title=”Class Notes”]

Speech recognition technology is used more and more for telephone applications like travel booking and information, financial account information, customer service call routing, and directory assistance. Using constrained grammar recognition, such applications can achieve remarkably high accuracy. Research and development in speech recognition technology has continued to grow as the cost for implementing such voice-activated systems has dropped and the usefulness and efficacy of these systems has improved. For example, recognition systems optimized for telephone applications can often supply information about the confidence of a particular recognition, and if the confidence is low, it can trigger the application to prompt callers to confirm or repeat their request. Furthermore, speech recognition has enabled the automation of certain applications that are not automatable using push-button interactive voice response (IVR) systems, like directory assistance and systems that allow callers to “dial” by speaking names listed in an electronic phone book.

Speaker identity is correlated with the physiological and behavioral characteristics of the speaker. These characteristics exist both in the spectral envelope (vocal tract characteristics) and in the supra-segmental features (voice source characteristics and dynamic features spanning several segments). The most common short-term spectral measurements currently used are Linear Predictive Coding (LPC)-derived cepstral coefficients and their regression coefficients. A spectral envelope reconstructed from a truncated set of cepstral coefficients is much smoother than one reconstructed from LPC coefficients.

Therefore it provides a stabler representation from one repetition to another of a particular speaker’s utterances. As for the regression coefficients, typically the first- and second-order coefficients are extracted at every frame period to represent the spectral dynamics. These coefficients are derivatives of the time functions of the cepstral coefficients and are respectively called the delta- and delta-delta-cepstral coefficients.

[/tab] [tab title=”Reference Text Books”]

TEXT BOOKS:
1. Automatic Control Systems 8th edition– by B. C. Kuo 2003– John wiley and son’s.,
2. Control Systems Engineering – by I. J. Nagrath and M. Gopal, New Age International(P) Limited, Publishers, 2nd edition.

REFERENCE BOOKS:
1.Modern Control Engineering – by Katsuhiko Ogata – Prentice Hall of India Pvt. Ltd., 3rd edition,1998.
2.Control Systems by N.K.Sinha, New Age International (P) Limited Publishers, 3rd Edition, 1998.
3.Control Systems Engg. by NISE 3rd Edition – John wiley.

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