Search results for "Alpha beta filter"
showing 6 items of 6 documents
Adaptive-gain extended Kalman filter: Extension to the continuous-discrete case
2009
In the present article we propose a nonlinear observer that merges the behaviors 1) of an extended Kalman filter, mainly designed to smooth off noise , and 2) of high-gain observers devoted to handle large perturbations in the state estimation. We specifically aim at continuous-discrete systems. The strategy consists in letting the high-gain self adapt according to the innovation. We define innovation computed over a time window and justify its usage via an important lemma. We prove the general convergence of the resulting observer.
Descriptor-type Robust Kalman Filter and Neural Adaptive Speed Estimation Scheme for Sensorless Control of Induction Motor Drive Systems
2012
Abstract This paper deals with robust estimation of speed and rotor flux for sensorless control of motion control systems which use induction motors as actuators. Due to the observability lack of five and six order Extended Kalman Filters, speed is here estimated by means of a Total Least Square algorithm with Neural Adaptive mechanism. This allows the use of a fourth-order Kalman Filter for estimating rotor flux and to filter stator currents. To cope with motor-load parameter variations, a descriptor-type robust Kalman Filter is designed taking explicitly into account these variations. The descriptor-type structure allows direct translation of parameter variations into variations of the co…
Hybrid nonlinear observer for Inertial Navigation
2016
This paper considers the problem of designing an observer for navigation and localization of inertial systems. Since the measurement systems used in this field have a low update rate with respect to the control algorithm, the design of a suitable observer with sampled measurements is required. Here a hybrid non-linear observer is proposed, combining two different observers with different characteristics. A theoretical treatment is given in order to prove the convergence of the observer and it will be contextualized in the framework of the hybrid systems, providing an elegant setting for the proposed solution. Finally experimental results show the feasibility of the proposed observer and the…
A Nonlinear Observer for Rotor Flux Estimation of Induction Motor Considering the Estimated Magnetization Characteristic
2017
This paper proposes a nonlinear observer for induction machine drives based on space-vector dynamic model of induction machine, expressed in state form, which presents the peculiarity of taking into consideration the magnetic saturation of the iron core. This observer is particularly suitable in order to obtain high accuracy in rotor flux estimation, in both amplitude and phase position, during working conditions characterized by varying flux, among which the most important are those during electrical losses minimization. A Lyapunov-based convergence analysis is proposed in order to suitably compute the numerical observer gain guaranteeing the convergence of the estimation error. The propos…
State feedback control against sensor faults for Lipschitz nonlinear systems via new sliding mode observer techniques
2011
This paper investigates the problem of simultaneous state and fault estimation and observer-based fault tolerant controller design for Lipschitz nonlinear systems with sensor failure. A new estimation technique is presented in this paper to deal with this design problem. In the proposed approaches, the original system is first augmented by a descriptor model transformation, then a new Proportional and Derivative sliding mode observer technique is developed to obtain accurate estimations of both system states and sensor faults. The designing observer is generalized from the PD observer in [3], but is not a trivial extension. Based on the state estimates, a observer-based control strategy is …
Descriptor-type Kalman Filter and TLS EXIN Speed Estimate for Sensorless Control of a Linear Induction Motor.
2014
This paper proposes a speed observer for linear induction motors (LIMs), which is composed of two parts: 1) a linear Kalman filter (KF) for the online estimation of the inductor currents and induced part flux linkage components; and 2) a speed estimator based on the total least squares (TLS) EXIN neuron. The TLS estimator receives as inputs the state variables, estimated by the KF, and provides as output the LIM linear speed, which is fed back to the KF and the control system. The KF is based on the classic space-vector model of the rotating induction machine. The end effects of the LIMs have been considered an uncertainty treated by the KF. The TLS EXIN neuron has been used to compute, in …