The robust L2-gain estimation is investigated for general uncertain systems with structured uncertainties. A new estimation structure is introduced: the Augmented-Gain Observer which encompasses both filters and observers and allows robust estimation even for some classes of unstable systems. Our approach is based on a separation of graphs theorem using frequency dependent Integral Quadratic Co...

Under stress conditions, microalgae are known to accumulate large amounts of neutral lipids and carbohydrates, which can be used for biofuel production. However, on-line measurement of microalgal biochemical composition is a difficult task which makes the microalgal process rather difficult to manage. In this paper, we propose a so called adaptive interval observer for the on-line estimation of...

We study how convergence of an observer whose state lives in a copy of the given system’s space can be established using a Riemannian metric. We show that the existence of an observer guaranteeing the property that a Riemannian distance between system and observer solutions is nonincreasing implies that the Lie derivative of the Riemannian metric along the system vector field is conditionally n...

A steering by wired haptic system based on disturbance rejection control techniques is presented. High gain Generalized Proportional Integral (GPI) observers are considered for the estimation of tire and steering wheel dynamic disturbances. These disturbances are on line canceled to ensure efficient tracking between the commanded steering wheel angle and the tire orientation angle. The estimate...

This paper proposes a high-gain disturbance observer (HDOB)-based controller for load frequency control (LFC) of power systems with multiple areas. The main goal of LFC problem is to maintain the frequency to its nominal value. The objective of this paper is to reject frequency variations due to abrupt load changes and diverse uncertainties (e.g., inertia and damping parameters, and interconnec...

A class of nonlinear stochastic processes satysfying a Lipschitz-type strip condition and supplied by a linear output equation, is considered. Robust asymptotic (high-gain) state estimation for nonlinear stochastic processes via di erential neural networks is discussed. A new type learning law for the weight dynamics is suggested. By a stochastic Lyapunov-like analysis (with Ito formula impleme...

We use a high-gain adaptive observer and a trend filtering algorithm to detect early stages that lead to terminal voltage collapses in Li-ion batteries. This approach allows accurate detection without having sophisticated battery models. Theoretical analysis proves that the physical Li-ion battery becomes unstable when the estimated states of the observer enters instability. The trend filtering...

The problem of state estimation in induction motors is considered in this paper. In most previous works, motor observers have been designed using standard models, neglecting the saturation and hysteretic effects in the machine magnetic circuit. As a matter of fact, these effects cannot be ignored especially when considering (speed, torque) control strategies involving wide range flux variations...

In this paper, a High-gain Observer (HGO)-based identification technique is used to identify the parameters for electromechanical servo systems. The HGO is used for estimating the system states, disturbances due to uncertainty or parameter changing, and output noise. Then, a new model is presented using QR factorization. The estimated observer states show good agreement with the system actual s...