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Additional resources for Adaptive control strategies for industrial use: proceedings of a workshop held in Kananaskis, Canada, 1988
E. Salgado (1988). Uncertainty, Information and Estimation, Techrttcct~ Report EE8855, Department of Electrical Engineering and Computer Science, University of Newcastle, Australia. L. Payne (1977). Dynamic System Identification, Academic Press, New York. , P. Ramadge and P. Caines. (1980). DiscreLe-time multi- variable adaptive control. 449-456. E. Salgado (1988). A new approach to time-series analysis using restricted complexity model. Technical Report, University of Newcastle, NSW. M. (1963).
IFAG Symposium on Identification and System Parameter Estimation, Washington, DC, USA. C. R. Extended prediction adaptive control. Proc. IFAC Symposium on Identification and System Parameter Estimation, York, UK. C. (1980). Applications of multivariable control control in a refinery and implementation. Proe. JAGC, San Francisco, USA. Favier,G. (1987). Self-tuning long-range predictive controllers. Proe. IFAG World Congress, Munich, FRG. K. and Richalet,J. (1979). Self-adapting IDCOM. Proe. IFAC Symposium on Identification and System Parameter Estimation, Darmstadt, FRG.
C. 2. The uncertainty region is shown around the frcqucncy response of an estimated modcl. ~e see again that the error is zero for o=~0 as discussed above. 4. s: oo 6. 00, Frequency response with uncertainty bound for example 2. 6. Qo PRRT approach to INTEGRATEDCgNTROb estimation outlined above, then we see that the estimator supplies information consistent with that rcquired f o r robust control system design. Thus an adaptive c o n t r o l l e r can be constructed whose performance is at l e a s t as good as a fixed robust design, hll that is necessary is for the information supplied by the estimator to be compared with the information available a - p r i o r i and to base the design on the most informative description.