A clock steering method: using a third-order type 3 DPLL equivalent to a Kalman filter with a delay

The SI engine load torque is key information for many engine and power train control systems. Since the torque is not measured in production vehicles, it needs to be estimated on-line. The paper presents design and analysis of second-order and third-order Luenberger load torque estimators. With the aim to reduce the estimator noise sensitivity without deteriorating its transient performance, an adaptive Kalman filter is proposed and compared with the Luenberger estimator. The adaptation mechanism is based on a load torque change detection algorithm. The estimators are examined by computer simulations and experiments.

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The Nyquist diagram for third-order type 1 systems with poles near the jω axis

Proceedings of the IEEE ◽

10.1109/proc.1965.4313 ◽

1965 ◽

Vol 53 (10) ◽

pp. 1667-1668

Author(s):

N.G. Meadows

Keyword(s):

Order Type ◽

Nyquist Diagram ◽

Third Order

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Tau and Galerkin Operational Matrices of Derivatives for Treating Singular and Emden-Fowler Third-order Type Equations

International Journal of Modern Physics C ◽

10.1142/s0129183122500619 ◽

2021 ◽

Author(s):

W. M. Abd-Elhameed ◽

H. M. Ahmed

Keyword(s):

Order Type ◽

Operational Matrices ◽

Third Order

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Combining Genetic Algorithms and Extended Kalman Filter to Estimate Ankle’s Muscle-Tendon Parameters

Volume 1: Adaptive and Intelligent Systems Control; Advances in Control Design Methods; Advances in Non-Linear and Optimal Control; Advances in Robotics; Advances in Wind Energy Systems; Aerospace Applications; Aerospace Power Optimization; Assistive Robotics; Automotive 2: Hybrid Electric Vehicles; Automotive 3: Internal Combustion Engines; Automotive Engine Control; Battery Management; Bio Engineering Applications; Biomed and Neural Systems; Connected Vehicles; Control of Robotic Systems ◽

10.1115/dscc2015-9781 ◽

2015 ◽

Cited By ~ 4

Author(s):

Luis Enrique Coronado ◽

Raul Chavez-Romero ◽

Mauro Maya ◽

Antonio Cardenas ◽

Davide Piovesan

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Mechanical Model ◽

Postural Stability ◽

Human Subject ◽

Experimental Validation ◽

Quiet Standing ◽

Experimental Measurements ◽

Third Order ◽

Biomechanical Parameter

This work proposes a set of simulation and experimental measurements to estimate muscle biomechanical parameter during human quiet standing. Understanding the mechanisms involved in postural stability is indispensable to improve the knowledge of how humans can regain balance against possible disturbances. Postural stability requires the ability to compensate the movement of the body’s center of gravity caused by external or internal perturbations. This paper describes the implementation of a hybrid parameter-estimation approach to infer the features of the human neuro-mechanical system during quiet standing and the recovery from a fall. The estimation techniques combines a genetic algorithm with the State-Augmented Extended Kalman Filter. These two algorithms running sequentially are utilized to estimate the musculo-skeletal parameters. This paper shows results of the approach when representing human standing as either a second-order or third order mechanical model. Experimental validation on a human subject is also presented.

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Application of Cubature Information Filter for Underwater Target Path Estimation

Pertanika Journal of Science and Technology ◽

10.47836/pjst.29.3.07 ◽

2021 ◽

Vol 29 (3) ◽

Author(s):

Guduru Naga Divya ◽

Sanagapallea Koteswara Rao

Keyword(s):

Kalman Filter ◽

Order Approximation ◽

Third Order ◽

Information Filter ◽

Cubature Kalman Filter ◽

System Nonlinearity ◽

Decentralized Data Fusion ◽

Target Path ◽

Underwater Target ◽

First Order Approximation

Bearings-only tracking plays a pivotal role in passive underwater surveillance. Using noisy sonar bearing measurements, the target motion parameters (TMP) are extensively estimated using the extended Kalman filter (EKF) because of its simplicity and low computational load. The EKF utilizes the first order approximation of the nonlinear system in estimation of the TMP that degrades the accuracy of estimation due to the elimination of the higher order terms. In this paper, the cubature Kalman filter (CKF) that captures the system nonlinearity upto third order is proposed to estimate the TMP. The CKF is further extended using the information filter (IF) to provide decentralized data fusion, hence the filter is termed as cubature information filter (CIF). The results are generated using Matlab with Gaussian assumption of noise in measurements. Monte-Carlo simulation is done and the results demonstrate that the CIF accuracy is same as that of UKF and this indicates the usefulness of the algorithm for state estimation in underwater with the required accuracy.

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