In this article, the problem of simultaneous leader-following consensus and parameter estimation is studied for a class multiagent systems subject to an uncertain leader system. The system described by sum sinusoids with unknown amplitudes, frequencies, phases. A fully distributed adaptive observer established each agent estimate frequencies without using any global information. It shown that i...

Parameter estimation is considered to be one of the greatest challenges in computational systems biology. Biological experiments can measure only a fraction of the kinetic parameters and the rest has to be estimated in silico. Recently parameter estimation problems have been addressed in the framework of control theory. One of the most successful and widely used methods in control theory for es...

Prognosis and Health Management methodologies require efficient parameter estimation approaches to enable systematic system reconfiguration and adaptive control to accommodate faulty behaviors, and to predict future system states. However, accurate and timely on-line parameter estimation of complex, nonlinear systems is difficult and can be computationally expensive. In this work, we propose a ...

Purpose – This paper is concerned with an online parameter estimation algorithm for nonlinear uncertain time-varying systems for which no stochastic information is available. Design/methodology/approach – The estimation procedure, called nonlinear learning rate adaptation (NLRA), computes an individual adaptive learning rate for each parameter instead of using a single adaptive learning rate fo...

In this paper, a class of single output nonlinear systems with an uncertain parameter is considered. A diffeomorphism is first introduced to simplify the system structure, then, by employing an adaptive approach to identify the unknown parameter, a sliding mode observer is developed to estimate the system state variables. Based on the observer, a fault estimation scheme is proposed based on the...