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Constrained identification for adaptive control: Application to biomedical systems

Timmons, William Donald

Abstract Details

1992, Doctor of Philosophy, Case Western Reserve University, Biomedical Engineering.
For ill defined, time varying, or non-linear systems, adaptive controllers offer an attractive alternative to classical design techniques. These controllers require minimal information. Ironically, the information that is available is often discarded. As a result, control may become unstable. If the available information could be used instead of discarded, the instability might be avoided. We therefore developed a real-time algorithm that imposes linear equality and inequality constraints on a time series model of the process. Thus commonly available information, such as open loop stability, settling time, and steady state gain, can be incorporated into the control. When the information is imposed according to our guidelines, control errors due to mismodeling can be significantly attenuated (in one instance, we reduced the mean squared output error by more than two orders of magnitude). We demonstrate our algorithm in two practical biomedical applications. In the first, we consider second order linear compartmental models (a popular model for pharmacodynamical systems). We develop novel constraints for these models. Then, as an example, we control plasma and tissue concentrations of methotrexate, an antimetabolite used in the treatmen t of certain neoplastic diseases. Our results show that the constraints improve both controller performance and patient safety. In the second biomedical application, we lower mean arterial pressure with a vasodilator. This system is non-linear and time-varying; hence it can be difficult to control. We simulate this system with a series of models that are progressively more complex. For each model, we develop suitable constraints which we then impose during control. As the models become more complex, we show that the constraints become more important for safety and improved control. While pursuing this work, we discovered a simple modification that significantly improves the efficiency and accuracy of positive semi-definite complementary linear programming (a technique for solving quadratic programs). We prove its validity and modify the pivot selection rules to implement least distance programming.
P. Katona (Advisor)
318 p.

Recommended Citations

Citations

  • Timmons, W. D. (1992). Constrained identification for adaptive control: Application to biomedical systems [Doctoral dissertation, Case Western Reserve University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=case1056479596

    APA Style (7th edition)

  • Timmons, William. Constrained identification for adaptive control: Application to biomedical systems. 1992. Case Western Reserve University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=case1056479596.

    MLA Style (8th edition)

  • Timmons, William. "Constrained identification for adaptive control: Application to biomedical systems." Doctoral dissertation, Case Western Reserve University, 1992. http://rave.ohiolink.edu/etdc/view?acc_num=case1056479596

    Chicago Manual of Style (17th edition)