Alpha beta filter

About: Alpha beta filter is a research topic. Over the lifetime, 5653 publications have been published within this topic receiving 128415 citations.

Full-order Luenberger observer based on fuzzy-logic control for sensorless field-oriented control of a single-sided linear induction motor

Abstract: This paper investigates sensorless indirect field oriented control (IFOC) of SLIM with full-order Luenberger observer. The dynamic equations of SLIM are first elaborated to draw full-order Luenberger observer with some simplifying assumption. The observer gain matrix is derived from conventional procedure so that observer poles are proportional to SLIM poles to ensure the stability of system for wide range of linear speed. The operation of observer is significantly impressed by adaptive scheme. A fuzzy logic control (FLC) is proposed as adaptive scheme to estimate linear speed using speed tuning signal. The parameters of FLC are tuned using an off-line method through chaotic optimization algorithm (COA). The performance of the proposed observer is verified by both numerical simulation and real-time hardware-in-the-loop (HIL) implementation. Moreover, a detailed comparative study among proposed and other speed observers is obtained under different operation conditions.

Invariant particle filtering with application to localization

Abstract: The recently introduced Invariant Extended Kalman Filter (IEKF) is an extended Kalman filter designed for systems admitting symmetries, that possesses interesting convergence properties, and a relative independence of the filter behavior with respect to the system's trajectory. In the present paper, the ideas are extended to a broad class of systems introducing the notion of “conditional invariance”, that is, invariance properties of the system once some of the state variables are known. We exploit this structure by devising an Invariant Rao-Blackwellized Particle Filter: those state variables are sampled, and the rest are marginalized out using IEKFs. The striking property of the obtained particle filter is that the Kalman gains are identical for all particles, leading to a drastic reduction of the computational burden. The strong potential of the method is illustrated by the challenging and realistic problem of localization from noisy inertial sensors and a noisy GPS having a randomly jumping bias.

Observers for linear systems with quantized outputs

Abstract: State estimation via nonlinear observers is described for a linear system with quantized outputs. Although there may be significant quantization error on average, it is possible to design observers with exponentially stable tracking error. Here we show that a Luenberger observer modified, so that feedback is impulsive at the quantizer transitions, is one such observer.