Based on the techniques of high gain observer and adaptive estimation, an algorithm is proposed in this paper for sensor fault estimation in nonlinear systems. It is essentially assumed that a high gain observer exists for the fault-free system. A high gain adaptive observer is then designed for sensor fault estimation. The convergence of the algorithm is established under a persistent excitati...

Normal industrial PD control of robot has two drawbacks, it needs joint velocity sensors; it cannot guarantee zero steady state error. In this paper we make two modifications to overcome these problems. High-gain observer is applied to estimate the joint velocities, and a RBF neural network is used to compensate gravity and friction. We give a new proof for high-gain observer, which explains a ...

The main weakness of all control methodologies is the dependency of feedbacks to full state measurements. In practical situations, measuring the states of a given system may fail because sometimes the measurements are impossible and sometimes, possible, but too expensive. Observer design for highly nonlinear dynamics is an important issue, particularly when the locally observable dynamics are n...

A state observer is proposed for a class of multi-output nonlinear systems. The gain of this observer involves a design function that has to satisfy some mild conditions which are given. Different expressions of such a function are proposed. Of particular interest, it is shown that high gain observers and sliding mode like observers can be derived by considering particular expressions of the de...

This paper proposes a new interval observer design based on a high/low gain concept, developed for the estimation of the input of a bioreactor. In a first step, a high gain bounded error observer is proposed, which is able to estimate the unknown input of the considered system. This observer is useful when an accurate model and noise–free measurements are available. We show that the error of th...