An optimization-based adaptive nonlinear observer

A novel adaptive nonlinear observer on basis of input and output measurements is proposed by using techniques from optimization theory, the calculus of variations and gradient descent dynamics. The formulation allows for general parametrized nonlinear observers, including a co-state (sensitivity) dynamics which propagates forward in time and serves as a filtered version of the measured error signal. It is shown that the observer works well in sense of speed and robustness. Several Matlab simulation examples and DSP-based hardware-in-the-loop (HIL) experiments are given to illustrate the effect of the approach