Tracking control of a piezo-hydraulic actuator using input–output linearization and a Cascaded Extended Kalman Filter structure

2018 ◽  
Vol 355 (18) ◽  
pp. 9298-9320 ◽  
Author(s):  
Benedikt Haus ◽  
Harald Aschemann ◽  
Paolo Mercorelli
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Tracking Control ◽  
Input Output ◽  
Hydraulic Actuator ◽  
Filter Structure ◽  
Input Output Linearization

scholarly journals Modeling and Diagnosis of the Inter-Turn Short Circuit Fault for the Sensorless Input-Output Linearization Control of the PMSM

2019 ◽  
Vol 63 (3) ◽  
pp. 159-168 ◽  
Author(s):  
Yacine Maanani ◽  
Arezki Menacer
Keyword(s):  
Kalman Filter ◽  
Fault Detection ◽  
Extended Kalman Filter ◽  
Stationary State ◽  
Short Circuit ◽  
Input Output ◽  
Detection Techniques ◽  
Stator Resistance ◽  
Short Circuit Fault ◽  
Input Output Linearization

The purpose of this paper is the inter-turn short circuit fault Modeling and detection for the sensorless input-output linearization control of the permanent magnet synchronous motor (PMSM) based on the Extended Kalman Filter observer (EKF). The fault detection procedures are based through the estimation of the stator resistance variation by the Extended Kalman Filter observer and the Fast Fourier Transformer (FFT) for the stationary state, and the Discrete Wavelet Transform (DWT) analysis of the electrical characteristics of the PMSM, for the non-stationary state. However, the FFT spectral analysis and the DWT is a useful solution to ensure that the variation of the stator resistance estimation is caused by the inter-turn short circuit fault. The effectiveness of the sensorless control and the fault detection techniques are presented in a simulation in MATLAB/Simulink environment.

scholarly journals Tracking Control of Autonomous Underwater Vehicles with Acoustic Localization and Extended Kalman Filter

2021 ◽  
Vol 11 (17) ◽  
pp. 8038
Author(s):  
Dongzhou Zhan ◽  
Huarong Zheng ◽  
Wen Xu
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Tracking Control ◽  
Autonomous Underwater Vehicles ◽  
Acoustic Signals ◽  
Underwater Vehicles ◽  
Ocean Currents ◽  
Acoustic Localization ◽  
And Control ◽  
Constraints Model

The absence of global positioning system (GPS) signals and the influence of ocean currents are two of the main challenges facing the autonomy of autonomous underwater vehicles (AUVs). This paper proposes an acoustic localization-based tracking control method for AUVs. Particularly, three buoys that emit acoustic signals periodically are deployed over the surface. Times of arrivals of these acoustic signals at the AUV are then obtained and used to calculate an estimated position of the AUV. Moreover, the uncertainties involved in the localization and ocean currents are handled together in the framework of the extended Kalman filter. To deal with system physical constraints, model predictive control relying on online repetitive optimizations is applied in the tracking controller design. Furthermore, due to the different sampling times between localization and control, the dead-reckoning technique is utilized considering detailed AUV dynamics. To avoid using the highly nonlinear and complicated AUV dynamics in the online optimizations, successive linearizations are employed to achieve a trade-off between computational complexity and control performance. Simulation results show that the proposed algorithms are effective and can achieve the AUV tracking control goals.

IDENTIFICATION OF THE NONLINEAR FRICTION CHARACTERISTICS IN A HYDRAULIC ACTUATOR USING THE EXTENDED KALMAN FILTER

2008 ◽  
Vol 32 (2) ◽  
pp. 121-136 ◽  
Author(s):  
Yuvin Chinniah ◽  
Richard Burton ◽  
Saeid Habibi ◽  
Eric Sampson
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Quadratic Function ◽  
Friction Model ◽  
Nonlinear Friction ◽  
Hydraulic Actuator ◽  
Friction Characteristic ◽  
Piston Velocity ◽  
Friction Characteristics ◽  
Custom Made

In this paper, the nonlinear friction characteristic of a custom made symmetrical linear hydraulic actuator is investigated using the Extended Kalman Filter (EKF). A new and very accurate characterization of friction is made by using a quadratic function of the piston velocity. Further to this proposed empirical friction model, the EKF is used to estimate the function coefficients. In this paper, an iterative approach is used to maintain system observability and render the estimation process more reliable. The study is conducted in simulation and by using measured experimental data. The estimated states and parameters by the EKF are found to be convergent to their known values in simulation and, further to experimental results, unique and repeatable. In addition, changes in the friction characteristics, which can occur in the physical system due to wear in the piston seals or degradation in the oil properties, are detected and accurately estimated by the EKF in simulation. This study presents an accurate nonlinear model for the representation of friction in a hydraulic actuator. It paves the way for the implementation of strategies for early fault detection in hydraulic systems.

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