What is it about?
Most approaches to control and fault detection require an exact knowledge of the state variables of the system. Unfortunately, direct measurements of the state variables are, in general, rarely available; we can have a little sensor on each state variable for technical, technological or financial reasons; therefore some form of reliable reconstructions of the state are necessary. A solution allowing to bypass these difficulties consist in reconstructing the common value of the unmeasured system state, from the information available on only the variables of inputs and outputs, by means of an observer. Such observer is often treated as software sensors, thus it optimizes the number of sensors in an industrial application.
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Why is it important?
We design of robust adaptive H∞ gain neural observer (RAH∞NO) for a large class of nonlinear systems with unknown constant parameters in the presence of bounded external perturbations on the state vector and on the output of the original system. The proposed adaptive observer incorporates radial basis functions (RBFs) neural networks (NN) to approximate the unknown nonlinearities existing in the system. The weight dynamics of every RBFNN are adjusted on-line by using an adaptive projection algorithm.
Perspectives
In reality, most practical systems, for example, robotic systems and power systems, can be modeled as switched systems. Due to the complicated behavior of switched systems, the study of stability becomes very difficult. There have been a lot of methods for overcoming this problem as; the average dwell time method, the barrier Lyapunov function method, multiple Lyapunov function method, and so on. Hence, we plan to extend the results of this paper to the following research problems: (i) In the case of switched nonlinear systems, the issue of the design of a neural observer based on the multiple Lyapunov function method and the LMI technique is interesting. (ii) Taking account of the state or output constraints, the Barrier Lyapunov Function is shown to be promising to design an adaptive neural observer.
Assistant Professor Abd-El-Kader Krifa
Higher Institute of Computer Science of Mahdia, Tunisia
Read the Original
This page is a summary of: Robust Adaptive H-infty Gain Neural Observer for a Class of Nonlinear Systems , IET Control Theory and Applications, January 2017, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/iet-cta.2015.1340.
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