Online Identification of a Mechanical System in Frequency Domain Using Sliding DFT

Nevaranta, Niko; Derammelaere, Stijn; Parkkinen, Jukka; Vervisch, Bram; Lindh, Tuomo; Stockman, Kurt; Niemelä, Markku; Pyrhönen, Olli; Pyrhönen, Juha

doi:10.1109/tie.2016.2574303

Cited by 42 publications

(16 citation statements)

References 38 publications

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“…First, the required amount of data needed for identification is minimal as it consists of a single excited frequency of the system. In contrast, other data-driven classical identification methods used in the literature require extensive data generation [6], [7], [8], [9], [10], [13], [14]. Due to the reduction in data requirements, the proposed methodology does not require human experience for data generation, and the model fitting process is not prone to data bias.…”

Section: Systemmentioning

confidence: 99%

“…A higher imposed value of the phase margin constraint would result in a lower number of discretized processes. A modified version of Nelder-Mead simplex algorithm that accepts constraints on optimization decision variables is used to realize (12) and (13). Then we proceed by findingS which is the set of the discretized processes in S. Once we haveS, we find the scaling parameter α for every process inS as proposed in property 1.…”

Section: B Discretization Of System Parameters' Subspacementioning

confidence: 99%

“…Parametric means are data-driven approaches where model parameters of a Process Under Test (PUT) are identified based on observation data. Such approaches include prediction error methods (PEM) [6], [7], [8], maximum likelihood (ML) methods [9], [10], least square (LS) methods [11], frequency response identification methods [12], [13], [14], and neural network-based methods [15], [16], [17]. Several studies in the literature applied these techniques to UAV operation with accurate identification results [18], [19], [20], [21], [22], [23].…”

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confidence: 99%

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Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

et al. 2020

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System identification is a key discipline within the field of automation that deals with inferring mathematical models of dynamic systems based on input-output measurements. Conventional identification methods require extensive data generation and are thus not suitable for real-time applications. In this paper, a novel real-time approach for the parametric identification of linear systems using Deep Learning (DL) and the Modified Relay Feedback Test (MRFT) is proposed. The proposed approach requires only a single steady-state cycle of MRFT, and guarantees stability and performance in the identification and control phases. The MRFT output is passed to a trained DL model that identifies the underlying process parameters in milliseconds. A novel modification to the Softmax function is derived to better conform the DL model for the process identification task. Quadrotor Unmanned Aerial Vehicle (UAV) attitude and altitude dynamics were used in simulation and experimentation to verify the presented approach. Results show the effectiveness and real-time capabilities of the proposed approach, which outperforms the conventional Prediction Error Method in terms of accuracy, robustness to biases, computational efficiency and data requirements.

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Section: Systemmentioning

confidence: 99%

Section: B Discretization Of System Parameters' Subspacementioning

confidence: 99%

mentioning

confidence: 99%

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Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

et al. 2020

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“…Step 1: In every sampling period, load current i L is acquired by means of analogue-digital conversion. Moreover then, reactive current i Lq1 and the (k + 1)th harmonic current i L_k+1 from i L is extracted by using recursive discrete Fourier transform algorithm [29]. Calculate root-mean-square values I Lq1 of i Lq1 and I L_k+1 of i L_k+1 .…”

Section: Current Reference Generation By Coordination Algorithmmentioning

confidence: 99%

Interaction and coordination between reactive compensation and harmonic suppression for three‐phase buck‐type D‐CAP

Chen

Dai

Zhang

et al. 2019

IET Power Electronics

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Dynamic capacitor (D-CAP) could be designed to compensate reactive power and suppress harmonic currents simultaneously by means of regulating DC and harmonic terms in duty-ratio modulation signal. However, the main constraint is that the amplitudes of DC and harmonic terms are always correlative, leading to the interaction between reactive and harmonic currents of D-CAP. To minimise mutual effects between reactive and harmonic currents, this study presents coordinated control between reactive compensation and harmonic suppression for buck-type D-CAP. The major concern of coordinated algorithm is to pre-estimate and select the DC and harmonic terms reasonably according to their inter-constraint relationship. Thus, D-CAP can optimise the generation of reactive and harmonic current references in different scenarios. Meanwhile, selective current control in multiple synchronous reference frames is employed to regulate reactive and harmonic current effectively. A series of experimental results are provided to verify the correctness of proposed coordinated control.

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“…As a conclusion, the proposed method can be used to estimate frequency responses as Bode or Nyquist (polar) plots on a sample-by-sample basis. It should be noted, that the sample-by-sample recursive calculations are basic properties or options of other well-known online frequency domain identification methods, such as Sliding-DFT (Nevaranta et al, 2016) or Fourier transform regression (Holzel and Morelli, 2011). However, these methods are based on the utilization of a moving window to store predefined amount of samples, whereas the method proposed in this paper, the frequency response is processed online from the current values of the measured input-output signals by synchronizing the Kalman filter to the instantaneous frequency of the excitation signal.…”

Section: Monitoring and Identification Of A Mechanical Systemmentioning

confidence: 99%

Online Identification of a Two-Mass System in Frequency Domain using a Kalman Filter

Nevaranta

Derammelaere

Parkkinen

et al. 2016

MIC

Self Cite

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Some of the most widely recognized online parameter estimation techniques used in different servomechanism are the extended Kalman filter (EKF) and recursive least squares (RLS) methods. Without loss of generality, these methods are based on a prior knowledge of the model structure of the system to be identified, and thus, they can be regarded as parametric identification methods. This paper proposes an on-line non-parametric frequency response identification routine that is based on a fixed-coefficient Kalman filter, which is configured to perform like a Fourier transform. The approach exploits the knowledge of the excitation signal by updating the Kalman filter gains with the known time-varying frequency of chirp signal. The experimental results demonstrate the effectiveness of the proposed online identification method to estimate a non-parametric model of the closed loop controlled servomechanism in a selected band of frequencies.

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Online Identification of a Mechanical System in Frequency Domain Using Sliding DFT

Cited by 42 publications

References 38 publications

Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

Interaction and coordination between reactive compensation and harmonic suppression for three‐phase buck‐type D‐CAP

Online Identification of a Two-Mass System in Frequency Domain using a Kalman Filter

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