Date of Award
Doctor of Philosophy (PhD)
Naresh K. Sinha
This research investigates the development of a methodology for designing self-tuning feedback linearizing control laws. If the conventional architecture for linear plant self-tuning systems is applied to the feedback linearization case, it is shown that the estimation algorithm gradient vanishes as the parameter estimates approach the true parameter values. Vanishing of the gradient causes the covariance matrix to increase without bound and consequently system failure. A new architecture is presented that eliminates the covariance problem but does not yield a direct estimation of the nonlinear plant parameters. The parameters estimated in the new architecture are composites of the true parameter values of the nonlinear plant and their estimated values. An adaptive law is designed to interpret an error equation formed from the composite parameters and asymptotically converge to the true nonlinear plant parameter values. A stability proof and convergence properties for the adaptive law are given. Sufficient conditions for a nonlinear plant to be capable of self-tuning in the new architecture are specified. The new method is demonstrated with simulations of an arbitrarily chosen nonlinear plant and two plants of practical interest. One plant is a chemical reactor running an exothermic process where reactor temperature is the controlled variable. The other plant is a bioreactor where control of the substrate concentration in an anaerobic digestion process is the objective. In both cases the method developed in this thesis offers performance improvements as compared with previously published results on control of these processes by other methods.
Gebo, Charles H., "Self-tuning feedback linearization" (2002). Open Access Dissertations and Theses. Paper 2539.