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Modern Control Systems Design

Code: 05 Module: Time allocation: ACS 682 Optional. Lectures ) Tutorials ) Practicals ) Assignment Private study 40 hours 40 hours 50 hours Revised: 1st Nov

Prerequisites: Weighting: Assessment: Lecturers: Location: Aims

Classical Control Systems Design (ACS 674) 15 credits. By report on assignment. By 1 x 2 hour examination. J Love, G Montague, P Oram, M Tham and M Willis. Newcastle University

To provide a thorough grounding in the state space and sampled data based techniques for the design of control systems, with particular reference to multivariable control systems, and their implementation. Objectives To establish a quantitative foundation to the design and analysis of sampled data systems. To provide a theoretical basis for applying the methods of state space and multivariable systems design. To develop an understanding of how interactions between and within loops can be handled by design. To introduce the concepts and principles of adaptive control. Phasing It is essential that delegates have completed (or be familiar with the material covered in) the Classical Control Systems (ACS 674) module before doing this one. It is desirable, but not essential, that delegates have completed (or have some familiarity with the material covered in) the Modelling and Simulation (ACS680) module before doing this one. Study Modes This module is of one week's full-time intensive study consisting of a variety of lectures, informal tutorials for problem solving and computer based lab sessions. It is followed by an assignment to be carried out in the delegate's own time.

School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne, NE1 7RU, UK

Coursework The time allocation for practical work provides for assignments making use of Matlab and Simulink. These will consist of exercises structured to reinforce the material covered in the lectures and tutorials. Recommended Texts Dabney J B & Harman T L, Mastering Simulink, Prentice Hall, 2004. Dutton K, Thompson S & Barraclough B, The Art of Control Engineering, Addison Wesley, 1997. Hanselman D & Littlefield B, Mastering Matlab, Prentice Hall, 2005. Love J, Handbook on Process Automation, Springer Verlag, to be published 2006. Marlin T, Process Control: Designing Processes and Control Systems for Dynamic Performance, McGraw Hill, 1995. Ogunnaike B A & Ray W H, Process Dynamics, Modelling and Control, Oxford University Press, 1994. Topics Included Sampled data systems: Samplers and holds. Pulse trains. Aliasing. Impulse sampling. Use and properties of Z transforms. Pulse transfer functions. Open and closed loop response. Data extrapolators. Z plane and stability analysis. The unit circle. Root locus in Z plane. Significance of pole positions. Bi-linear transforms. Discrete equivalents of Routh-Hurwitz test and Bode diagrams. Design of impulse compensators: PID, Dahlin's, deadbeat and pole cancellation methods. Realisation of algorithms. State space: Introduction to state-space. State variables, inputs and outputs. Companion matrix. Solution of state-space equations. State transition matrix. Eigenvalues and singularity. State-space analysis of multivariable systems. Multiloop systems and multivariable block diagrams. State-space representation of control systems. Transfer function matrices. Translation from continuous to discrete time representations. Impulse and pulse response matrix. Modal decomposistion of the state space description. State feedback regulators: Constant gain state-feedback design using pole-placement method. Incorporation of reference signal. Full and reduced order observer design. Integration of controller and observer. Multivariable control: Structural properties of systems. Diagonalisation, de-coupling and canonical forms. Controllability & observability. Stability of systems. Design of control systems. Relative gain array. Singular value decomposition. Morari's resilience index. Eigen structure assignment of control system design. Separation principle. Optimum control design. Quadratic regulators. Adaptive control: Concepts of adaptive control. Adaptive control structures: direct and indirect adaptation. Distinction between tune on demand and continuous adaptation. Gain scheduling, model reference adaptive control and adaptive pole placement control. Overview of industrial adaptive controllers. Case study: internal model adaptive control.

School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne, NE1 7RU, UK


Modern Control Systems Design

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