Dr. Daniel E. Rivera,
Phone: (480) 965-9476,
Email: daniel.rivera@asu.edu
Office: Goldwater Science and Engineering Center Room 568

• This course provides a survey of the field of system identification, which considers the use of plant data to obtain dynamic models useful for simulation, prediction, and control design. Emphasis is placed on fundamentals associated with judicious selection of design variables in system identification techniques. The course makes significant use of Matlab's System Identification Toolbox to solve practical problems on both real and simulated data sets.

• No textbook is required for this course. Class notes corresponding to a textbook in preparation by the instructor will be distributed to all course participants. Students may use as a supplementary text Ljung, L. System Identification: Theory for the User, 2nd Edition, Prentice-Hall, 1999 (ISBN 0-13-656695-2).

• Students are strongly advised to purchase a copy of Release 14 of the Student Version of Matlab with SIMULINK as well as the Control System, Signal Processing, and System Identification toolboxes. For more information on the Matlab Student Version please see (http://www.mathworks.com/student)

• Extensive use will be made of the world-wide web for distributing notes, homeworks, and other course materials

• Undergraduate control course or equivalent from any engineering discipline (e.g., CHE 461 Process Dynamics and Control or EEE 480 Feedback Systems), knowledge of basic linear algebra and complex number arithmetic. Familiarity with discrete-time modeling and control design is helpful but not required.

1. Signals and Systems Overview. Background material on signals and systems concepts which are the key to successful system identification is discussed. The material in this session is revisited (within the context of specific model structures and techniques) throughout the remainder of the course. Specific topics include: differential equations, Laplace transforms, frequency responses, difference equations, stationarity, autocorrelation, crosscorrelation, power spectra.
   
   
2. Input Signal Design and Implementation. The use and design of random and deterministic signals as inputs for system identification is presented. Among the signals presented are pulse, step, Random Binary Sequence (RBS), Pseudo Random Binary (PRBS), and m-level Pseudo Random (m-PRS) inputs. Emphasis is given to the systematic design of "plant-friendly" input signals (i.e., signals that can be introduced while the plant is in normal operation), the effective use of a priori information in input signal design, and real-time implementation aspects.
   
   
3. Nonparametric model estimation. Nonparametric estimation considers the use of correlation and spectral analysis to obtain estimates of the plant impulse, step and frequency responses from identification data. The effectiveness of these methods as a means for getting useful precursor models for parametric system identification is discussed.
   
   
4. Prediction-Error Model Structures, Parameter Estimation and Classical Model Validation. Fundamental requirements for parametric estimation, particularly with regards to identifiability and requirements for consistent (asymptotically unbiased) estimation, are presented. Parametric estimation using one-step ahead prediction error model structures and estimation techniques (ARX, ARMAX, Box-Jenkins, FIR, Output Error) is described. These methods rely on regression (both linear and nonlinear) to compute the model parameters; all are supported by the functionality of the System Identification toolbox in MATLAB. The validation portion of the module presents the myriad of classical techniques (simulation, crossvalidation, residual analysis, etc.) for determining adequacy of the estimated models.
   
   
5. Control-Relevant Identification. This portion of the course emphasizes techniques that incorporate closed-loop performance requirements in the identification procedure (control-relevant identification). Topics in the control-relevant identification discussion include control-relevant parameter estimation using prefiltering, control-relevant input signals (e.g., Schroeder-phased inputs), uncertainty estimation for robust control, and integrated identification with PID and digital controller design.
   
   
6. Closed-Loop Identification. The discussion on closed-loop identification addresses fundamental limitations associated with the presence of feedback in the system. Students will understand why identification from plant normal plant operating records (failing to meet certain fundamental conditions on input design and model structure) is often unsucessful in providing useful models. Topics to be discussed include identifiability requirements for closed-loop identification, signal injection points for closed-loop identification, nonparametric closed-loop identification via correlation and spectral analysis, and considerations involved in using parametric estimation methods with closed-loop data (indirect and direct approaches). If time permits, control-relevant, closed-loop identification via iterative refinement will be discussed.
   
   
7. Identification of Multivariable Systems. Many issues in multivariable identification extend naturally from single-input, single-output concepts. Additional issues involved in multivariable system identification include: 1). experimental /data generation issues: (multivariable Random Binary and Pseudo-Random Binary inputs, "zippered" Schroeder-phased inputs), 2) multivariable parameter estimation (MISO PEM, MIMO ARX, state-space models estimation, model reduction), and 3) integration of identification and control geared to popular industrial multivariable control algorithms, i.e., model predictive control. As before, emphasis will be given to methods that complement industrial practice. Case studies in this module include the Jacobsen-Skogestad high purity distillation column and the Shell Heavy Oil Fractionator benchmark problems.
   
   
8. Issues in Nonlinear and Semiphysical System Identification. Similarities and differences from linear system identification is discussed. Many results from linear system identification still hold when things go nonlinear and, to some extent, this knowledge and intuition from linear system identification can be very useful. We will point out some of the most important changes and pitfalls. Some nonlinear black-box models which are generalizations of linear models (Volterra, NARX, Hammerstein) as well as "trendy" nonlinear identification techniques (neural network-based ID, Model-On-Demand) will be presented. Some simple ways to combine physical knowledge and black-box techniques will be discussed.
   

• Class syllabus (From Spring 2004)
• Introductory course presentation (From Spring 2004)