Modern Control Systems Design
Objectives and Syllabus
This module covers the design and analysis of sampled data systems and provides a theoretical basis for applying the methods of state space and multivariable systems design. It also develops an understanding of how interactions between and within loops can be handled by design.
|Code:||CME 8382 (formerly ACS 682)|
|Time Allocation:||Lectures||40 hours|
|Private Study||70 hours|
|Prerequisites:||Classical Control Systems Design (CME 8374)|
|Assessment:||By report on assignment
By 1 x 2 hour examination
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.
- 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.
Prerequisite to this module is the Classical Control Systems (CME 8374) module.
It is desirable, but not essential, that students have completed (or have some familiarity with the material covered in) the Modelling and Simulation (CME 8380) module before doing this one.
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 student’s own time.
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.
- 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, Process Automation Handbook, Springer, 2007.
- Marlin T, Process Control: Designing Processes and Control Systems for Dynamic Performance, 2nd Edition, McGraw Hill, 2000.
- Roffel B & Betlem B, Process Dynamics and Control, John Wiley & Sons, 2006.
- Seborg D, Edgar T, Mellichamp, D and Doyle F, Process Dynamics and Control, 3rd Edition, Wiley, 2011.
- Wilkie, J, Johnson M and Katebi R, Control Engineering: An Introductory Course, McMillan (Palgrave), 2002.
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 decomposition 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.