On Estimation of Plant Dynamics and Disturbance from Input-Output Data in Real Time

Abstract

This paper is concerned with the question of, for a physical plant to be controlled, whether or not its internal dynamics and external disturbances can be realistically estimated in real time from its input-output data. A positive answer would have significant implications on control system design, because it means that an accurate model of the plant is perhaps no longer required. Based on the linear extended state observer (LESO), it is shown that, for a nth order plant, the answer to the above question is indeed yes. In particular, it is shown that the estimation error (1) converges to the origin asymptotically when the model of the plant is given; (2) is bounded and inversely proportional to the bandwidth of the observer when the plant model is mostly unknown. Note that this is not another parameter estimation algorithm in the framework of adaptive control. It applies to a large class of nonlinear, time-varying processes with unknown dynamics. The solution is deceivingly simple and easy to implement. The results of the mathematical analysis are verified in a simulation study and a motion control hardware test.