Adaptive High-gain Observer Research Articles

Adaptive observers for speed sensorless IMs

In this paper, one proposes an approach which allows the design adaptive observers for a class of uniformly observable nonlinear MIMO systems with linear parameterisations. The structure of the proposed observer is simple and it is able to give rise to different observers: 1 adaptive high gain observer for linearly parameterised; 2 adaptive sliding mode observer for linearly parameterised with tanh commutation function; 3 adaptive sliding mode observer for linearly parameterised with arctan commutation function. These observers are applied to jointly estimate states rotor flux, rotating speed and torque load and unknown constant parameters stator resistance.

Adaptive observers for a class of uniformly observable systems with nonlinear parametrization and sampled outputs

In this paper, we propose an adaptive observer for a class of uniformly observable nonlinear systems with nonlinear parametrization and sampled outputs. A high gain adaptive observer is first designed under the assumption that the output is continuously measured and its exponential convergence is investigated, thanks to a well defined persistent excitation condition. Then, we address the case where the output is available only at (non uniformly spaced) sampling instants. To this end, the continuous-time output observer is redesigned leading to an impulsive observer with a corrective term involving instantaneous state impulses corresponding to the measured samples and their estimates. Moreover, it is shown that the proposed impulsive observer can be put under the form of a hybrid system composed of a continuous-time observer coupled with an inter-sample output predictor. Two design features are worth to be emphasized. Firstly, the observer calibration is achieved through the tuning of a scalar design parameter. Secondly, the exponential convergence to zero of the observation and parameter estimation errors is established under a well defined condition on the maximum value of the sampling partition diameter. More specifically, the observer design is firstly carried out in the case of linear parametrization before being extended to the nonlinear one. The theoretical results are corroborated through simulation results involving a typical bioreactor.

Adaptive High-Gain observer for joint state and parameter estimation: A comparison to Extended and Unscented Kalman filter

An adaptive High-Gain observer (AHG) as well as an Extended (EKF) and Unscented Kalman filter (UKF) are implemented for joint state and parameter estimation of a novel multi-axial electromagnetically actuated punch. These observers are compared in terms of convergence and response time to erroneous parameter and state initialization, as well as parameter modifications during operation. The AHG is further analyzed proposing an adaptive gain, which reduces the observer's high sensibility with respect to noise. Simulation results show that AHG is more suitable compared to state-of-the-art EKF and UKF.

An Adaptive High Gain Observer Design for Nonlinear Systems

This paper studies an adaptive high gain observer design for nonlinear systems which have lower triangular nonlinearity with Lipschitz coefficient, depending on the control input. Because the gain of the proposed observer is tuned automatically by a simple update law, our design approach doesn't need any information about the Lipschitz constant. Also, it is shown that under some assumptions, the dynamic gain of the proposed observer is bounded and its estimation error converges to zero asymptotically. Finally, a numerical example is given to verify the effectiveness of our design approach.

A high-gain adaptive observer for detecting Li-ion battery terminal voltage collapse

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 enter instability. The trend filtering algorithm is able to detect such instability under large perturbations from the discharge current. Extensive simulation and experimental results demonstrate the effectiveness of the algorithms and its robustness under realistic perturbations.

Adaptive fuzzy decentralized dynamics surface control for nonlinear large-scale systems based on high-gain observer

This paper discusses the adaptive fuzzy decentralized output-feedback control problem for a class of nonlinear large-scale systems. The systems under study have unknown nonlinearities, mismatched interconnections in control inputs, and unmeasurable state variables. Therefore, fuzzy logic systems are employed to approximate the unknown nonlinear functions in the control design; and an adaptive high-gain observer is established to estimate the unmeasured states. Based on the backstepping design technique and the dynamic surface control (DSC) approach, a novel adaptive fuzzy decentralized output-feedback control scheme is developed. It is proved that the proposed control approach can ensure that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded (SUUB) and the observer error and tracking error can all converge to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated by simulation examples with comparisons with several existing methods.

Adaptive high gain observer based output feedback predictive controller for induction motors

An accurate estimation of both rotor and stator resistances is usually required to achieve high performance control in induction machine drive systems. In our study, an output feedback predictive controller which is adaptive with respect to these parameters uncertainties is proposed. Such design attempts to a fully decoupling in speed and flux magnitude when conjoint estimation of the state and the critical parameters is performed. Precisely, two-stage of high gain observer are used to provide on-line update of rotor and stator resistances as well as following time evolution of the rotor flux and the load torque. Then, the instantaneous estimates are exploited in a constrained predictive controller for achieving the admissible tracking objective. The little number of the synthesis parameters and the ability of the control scheme to handle input constraints constitute the two main benefits of our design. Simulation results show that the proposed controller is suitable for high dynamics performance applications.

Adaptive fuzzy decentralized control for nonlinear large-scale systems based on high-gain observer

An adaptive fuzzy decentralized backstepping output-feedback control approach is proposed for a class of nonlinear large-scale systems with completely unknown functions, the interconnections mismatched in control inputs, and without the measurements of the states. Fuzzy logic systems are employed to approximate the unknown nonlinear functions, and an adaptive high-gain observer is developed to estimate the unmeasured states. Using the designed high-gain observer, and combining the fuzzy adaptive control theory with backstepping approach, an adaptive fuzzy decentralized backstepping output-feedback control scheme is developed. It is proved that the proposed control approach can guarantee that all the signals of the closed-loop system are semiglobally uniformly ultimately bounded (SUUB), and that the observer errors and the tracking errors converge to a small neighborhood of the origin by appropriate choice of the design parameters. Finally, a simulation example is provided to show the effectiveness of the proposed approach.

Fuzzy adaptive high-gain-based observer backstepping control for SISO nonlinear systems with dynamical uncertainties

In this paper, a fuzzy adaptive output feedback control approach is developed for a class of SISO strict-feedback nonlinear systems with unmeasured states, unmodeled dynamics, and dynamical disturbances. In the backstepping recursive design, fuzzy logic systems are used to approximate the unknown nonlinear functions, a fuzzy adaptive high-gain observer is designed to estimate the unmeasured states; a dynamic signal is incorporated into the control scheme to dominate the dynamic uncertainties. Using the states estimates and combining the backstepping design technique, a fuzzy adaptive output feedback control is constructed recursively. It is proved that the proposed fuzzy adaptive output feedback control scheme can guarantee the all signals in the closed-loop system are semiglobally uniformly ultimately bounded (SUUB), and the observer and tracking error converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated via an example.

An adaptive high-gain observer for wastewater treatment systems

Abstract The purpose of this paper is twofold: (1) we apply the adaptive observer developed in Boizot et al. [1] to a wastewater system, following two cascade steps. First, we apply it to a simplified model of the system. Second, we use this “simplified” estimation as a measurement for the full system. (2) Although the observability analysis is trivial, the equations contain rather complicated terms. Therefore, it is not reasonable to change coordinates for those of the required observability canonical form. Hence, we have to establish and work with the “unusual” equations of the observer in natural coordinates. Let us point out that the simulations are done taking into account the small number of measurements (three) available in practice.

Fuzzy adaptive high-gain-based observer backstepping control for SISO nonlinear systems

In this paper, a new fuzzy adaptive control approach is developed for a class of SISO strict-feedback nonlinear systems, in which the nonlinear functions are unknown and the states are not available for feedback. By fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy adaptive high-gain observer is designed to estimate the unmeasured states. Under the framework of the backstepping design, fuzzy adaptive output feedback control is constructed recursively. It is shown that the proposed fuzzy adaptive control approach guarantees the semi-global boundedness property for all the signals of the resulting closed-loop system. Simulation results are included to illustrate the effectiveness of the proposed techniques.

An adaptive high-gain observer for nonlinear systems

In this paper the authors provide a solution to the noise sensitivity of high-gain observers. The resulting nonlinear observer possesses simultaneously (1) extended Kalman filter’s good noise filtering properties, and (2) the reactivity of the high-gain extended Kalman filter with respect to large perturbations. The authors introduce innovation as the quantity that drives the gain adaptation. They prove a general convergence result, propose guidelines to practical implementation and show simulation results for an example.

An adaptive observer for sensor fault estimation in a class of uniformly observable non-linear systems

Based on the techniques of high gain observer and adaptive estimation, an adaptive observer is proposed in this paper for sensor fault estimation in a class of uniformly observable non-linear systems. It is first assumed that a high gain observer exists for the fault-free system. With a parametric model of sensor fault, a high gain adaptive observer is then designed for sensor fault estimation. In order to establish the global convergence of the adaptive observer, in addition to the usual conditions for high gain observer convergence, a persistent excitation condition is also required, like in most recursive parameter estimation problems.

Adaptive output feedback controller for a class of uncertain nonlinear systems

This paper addresses the problem of asymptotic tracking of an arbitrary smooth bounded reference output sequence in the presence of step like disturbances for a class of uncertain nonlinear multi-outputs systems, namely systems that could be described by the usual lower triangular representation thanks to an appropriate state transformation. These performances are shown to be achieved by combining an appropriate high gain state feedback control with a high gain adaptive observer under persistent of excitation condition. A filtered integral action is incorporated into the underlying state feedback control design to get a robust compensation of step like disturbances while reducing appropriately the noise control system sensitivity. The persistent excitation condition allows to avoid those useful parameter adaptation robustness design features as parameter projection or parameter adaptation freezing. This makes it possible to put the emphasis on the adaptive control design. As an illustrative example, the proposed adaptive control design is used to address the servo problem of a nonlinear double integrator using only the measurements of the position.

NONLINEAR SYSTEM SENSOR FAULT ESTIMATION

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 excitation condition.

A performance comparison between two design techniques for non-linear output feedback control

For a system possessing a non-linear output feedback normal form, an observer backstepping design is compared to a high gain observer design with respect to non-singular performance cost functional. If the initial error between the initial condition of the state and the initial condition of the observer is large, the high gain observer design is shown to have better performance than the observer backstepping design. An output feedback system with parametric uncertainty is then considered. It is shown that if an a priori estimate for the bound of the uncertain parameter is conservative, then an adaptive observer backstepping design has better performance than the adaptive high gain observer design.