Extended Kalman Filter for the Estimation and Fuzzy Optimal Control of Takagi-Sugeno Model

Jiménez, Agustín; Al‐Hadithi, Basil Mohammed; Matı́a, Fernando

doi:10.5772/13466

Cited by 7 publications

(2 citation statements)

References 51 publications

(35 reference statements)

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“…Since full-state feedback is usually infeasible, due to limitations on the available number of sensors, the LQR is often combined with a linear Kalman filter, which acts as the state estimator, yielding the well-established linear quadratic Gaussian (LQG) control scheme [32,33]. The technique is applicable for the case of linear systems, however less extensive work has dealt with combining the LQR with nonlinear filters [34][35][36][37]. Furthermore, the LQG approach assumes that the system is described by deterministic properties (parameters).…”

Section: Introductionmentioning

confidence: 99%

Semi-active control for vibration mitigation of structural systems incorporating uncertainties

Miah

Chatzi

Weber

2015

Smart Mater. Struct.

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This study introduces a novel semi-active control scheme, where the linear-quadratic regulator (LQR) is combined with an unscented Kalman filter (UKF) observer, for the real-time mitigation of structural vibration. Due to a number of factors, such as environmental effects and ageing processes, the controlled system may be characterized by uncertainties. The UKF, which comprises a nonlinear observer, is employed herein for devising an adaptive semi-active control scheme capable of tackling such a challenge. This is achieved through the real-time realization of joint state and parameter estimation during the structural control process via the proposed LQR-UKF approach. The behavior of the introduced scheme is exemplified through two numerical applications. The efficacy of the devised methodology is firstly compared against the standard LQR-KF approach in a linear benchmark application where the system model is assumed known a priori, and secondly, the method is validated on a joint state and parameter estimation problem where the system model is assumed uncertain, formulated as nonlinear, and updated in real-time.

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Section: Introductionmentioning

confidence: 99%

Semi-active control for vibration mitigation of structural systems incorporating uncertainties

Miah

Chatzi

Weber

2015

Smart Mater. Struct.

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“…Initially, the concept of using observed noisy measurements to update and predict the system response was surfaced by Kalman (1960) [8]. The foregoing filtering technique is widely accepted for linear system where all of the degreeof-freedoms responses are not measurable [2], [6], [9], [12] [13]. An updated version of nonlinear filter so-called the unscented Kalman filter (UKF) was introduced by Julier & Uhlmann (1997) [7] and extended by Merwe & Wan (2004) [13].…”

Section: ___________________________________________mentioning

confidence: 99%

Structural Parameters Uncertainties Effect on Structural Responses

Mohammad¹

2016

Fifth International Conference on Advances in Civil and Structural Engineering - CSE 2016

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Abstract-Smart structures are adopting the sensorization and leading the structural systems into a non-trivial situation. Hence those structures required complex monitoring, controlling and updating techniques. In order to understand the changes over time of such systems it is essential to understand the effect of uncertainties. There are several known and unknown sources of uncertainties such as environmental variation which may alter the frequencies of the structures as a results they might encounter severe problems or even full collapse. In order to get the best out of the implemented technologies it is necessary to understand the uncertainties issues related to structural properties i.e., stiffness, damping and mass. Additionally, the structural systems required proper treatment as they will essentially handle extreme unknown dynamic loads. For adapting with the aforementioned changes system parameters need to be identified and updated/adjusted throughout their life time. As dynamic loads are always serious concern, hence, herein the El Centro 1940 earthquake is employed and the response is evaluated. The response is evaluated for several cases for understanding the uncertainty effects of stiffness, damping and mass of the system. In a nutshell, in this study, the dynamical states and system parameters both are assumed to be unknown simultaneously which forms a nonlinear identification problem. To this end, a nonlinear filter so-called the unscented Kalman filter (UKF) is employed. The numerically obtained results show potential which requires further investigation by combining with the different control techniques.

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A Learning-Based Fault Tolerant Tracking Control of an Unmanned Quadrotor Helicopter

Liu

Chen

Luo

2015

J Intell Robot Syst

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Extended Kalman Filter for the Estimation and Fuzzy Optimal Control of Takagi-Sugeno Model

Cited by 7 publications

References 51 publications

Semi-active control for vibration mitigation of structural systems incorporating uncertainties

Semi-active control for vibration mitigation of structural systems incorporating uncertainties

Structural Parameters Uncertainties Effect on Structural Responses

A Learning-Based Fault Tolerant Tracking Control of an Unmanned Quadrotor Helicopter

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