Nonlinear Predictor-Based Output Feedback Control for a Class of Uncertain Nonlinear Systems

The presented work addresses the output feedback control problem for a large class of uncertain nonlinear systems. The control algorithm relies on an output predictor, designed to predict the system’s measured output with arbitrary accuracy, for any admissible control signal. This output predictor is constructed using a derivative estimator, which allows the algorithm to only require limited knowledge of the system’s dynamics in general, and of the input matrix in particular. The output predictor, which is designed to be controllable, is then controlled using a backstepping control algorithm. The output feedback control problem is thus solved by controlling the predictor’s output, as opposed to controlling the actual system’s output, as is more commonly the case in the literature. Ultimately, it is shown that the predictor’s output is made to simultaneously converge to the actual system’s output and to a given desired output trajectory. It follows that the system’s output itself converges to the desired trajectory. Numerical simulation results are provided to illustrate the algorithm performance.