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Direct adaptive control using artificial neural networks with parameter projection

Svetlozara Krasteva Tzanzalian, University of Massachusetts Amherst


This research is focused on the development of a stable nonlinear direct adaptive control algorithm. The nonlinearity is realized through a one-hidden-layer forward artificial neural network (ANN) of sigmoidal basis functions. The control scheme incorporates a linear adaptive controller, acting in parallel with the ANN, so that if all nonlinear elements are set to zero, a linear controller results. The control scheme is based on inverse identification. An inherent problem with that scheme is the existence of multiple steady states of the controller. This issue is addressed and sufficient conditions for stability and convergence of the algorithm are derived. In particular, it is shown that if (1) the identification algorithm converges so that the prediction error tends to zero, (2) the plant is stably invertible, (3) parameter projection is applied to prevent singularities, then the tracking error converges to zero as well. The one-step-ahead nonlinear controller with the proposed parameter projection has been tested in simulation studies of a CSTR system and in a pilot distillation column experiment. In both studies the nonlinear adaptive controller showed performance superior to that obtained using linear adaptive control. Applying parameter projection proved to be crucial to the successful operation of the control system. The validity of the approach was investigated further by performing a theoretical robustness analysis and generalized to non-invertible systems by applying ideas from predictive control. In particular, it is shown that if the prediction horizon is increased, the nonlinear adaptive controller can be applied to non-minimum-phase systems.

Subject Area

Chemical engineering|Computer science|Electrical engineering

Recommended Citation

Tzanzalian, Svetlozara Krasteva, "Direct adaptive control using artificial neural networks with parameter projection" (1994). Doctoral Dissertations Available from Proquest. AAI9420696.