Adaptive Regulation of Block-Oriented Nonlinear Systems Using Binary Sensors With Applications to Automotive Engine Control

In this article, adaptive regulation of block-oriented nonlinear systems, i.e., Hammerstein and Wiener systems, with binary-valued measurements of the regulation errors is considered. Compared with the classical framework for stochastic adaptive control, the new feature here is that only binary-valued observations of regulation errors are available to the controller. An adaptive regulator based on the stochastic approximation algorithm is proposed and it is proved that the regulator is optimal in the sense that it minimizes the long-run average of the squared regulation errors almost surely. Numerical examples as well as real applications of the proposed algorithms to automotive engine control are given.