In digital predistortion (DPD) design, loop-delay estimation for synchronization is an important work. In this paper, an algorithm for precisely estimating loop-delay in DPD system is proposed. The whole algorithm con...
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In this study, we present an algorithm for system identification for systolic array implementation. With this schema, discrete samples of input and output data of a sys- tem with uncertain characteristics are used to ...
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In this study, we present an algorithm for system identification for systolic array implementation. With this schema, discrete samples of input and output data of a sys- tem with uncertain characteristics are used to determine the parameters of its model. The identification algorithm is based on recursiveleastsquares, QR decomposition, and block processing techniques with covariance resetting. The identification process is based on the use of Givens rotation. Additionally, we want to address the following problems: how the round-off error propagates in time and the implementation in closed loop adaptive control. We will compare the implementation of fixed point arithmetic with the implementation of floating point arithmetic. This is primarily a theoretical in- vestigation to be conducted with computer simulations where numerical results will be investigated.
This paper presents an adaptive PID-like controller(PIDLC) using a modified Neural network(MNN) for learning the characteristics of a dynamic system. The PIDLC can adapt parameters' variation and uncertainty in th...
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ISBN:
(纸本)0780338324;0780338332
This paper presents an adaptive PID-like controller(PIDLC) using a modified Neural network(MNN) for learning the characteristics of a dynamic system. The PIDLC can adapt parameters' variation and uncertainty in the controlled plant through on-line learning. The MNN's learning algorithm is considerably faster because of the introduction of recursiveleastsquares(RLS) algorithm. The simulation results show that this kind of control algorithm is very effective especially when there are variations in the plant dynamics.
Active noise control is used to deal with noise pollution in many fields, and it reduces the noise intensity by generating an antisound which has the same amplitude and opposite phase to the original noise. However, t...
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ISBN:
(纸本)9781728113128
Active noise control is used to deal with noise pollution in many fields, and it reduces the noise intensity by generating an antisound which has the same amplitude and opposite phase to the original noise. However, the antisound is a secondary sound source compared to the original noise, and it influences the performances of the active noise control. The sound sensor detects original noise and the antisound simultaneously, in order to cancel the influence of the antisound, this paper proposes a method to identify the model of the acoustic feedback path between the secondary sound source and the sound error sensor. This paper uses a finite impulse response filter to express the model of the above acoustic feedback path. It designs an offline experiment without original noise to identify the model individually. In the experiment, it generates white noise as the antisound and uses recursive least squares algorithm to identify the parameters of the model. It deduces the formula of the identification and analyzes the results of the experiment. The experiment results show that the proposed method model the acoustic feedback model accurately.
An accurate model is important for the engineer to design a robust controller for the autonomous underwater vehicle. There are two factors that make the identification difficult to get accurate parameters of an AUV mo...
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An accurate model is important for the engineer to design a robust controller for the autonomous underwater vehicle. There are two factors that make the identification difficult to get accurate parameters of an AUV model in practice. Firstly, the autonomous underwater vehicle model is a coupled six-degrees-of-freedom model, and each state of the kinetic model influences the other five states. Secondly, there are more than 100 hydrodynamic coefficients which have different effects, and some parameters are too small to be identified. This article proposes a simplified six-degrees-of-freedom model that contains the essential parameters and employs the multi-innovation leastsquaresalgorithm based on the recursive least squares algorithm to obtain the parameters. The multi-innovation leastsquaresalgorithm leverages several past errors to identify the parameters, and the identification results are more accurate than those of the recursive least squares algorithm. It collects the practical data through an experiment and designs a numerical program to identify the model parameters. Meanwhile, it compares the performances of the multi-innovation leastsquaresalgorithm with those of the recursive least squares algorithm and the least square method, the results show that the multi-innovation leastsquaresalgorithm is the most effective way to identify parameters for the simplified six-degrees-of-freedom model.
To remove parameter dependence in existing sensorless control strategies, a parameter-free model predictive current control is proposed for permanent magnet synchronous motor without any position sensor. First, the cu...
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To remove parameter dependence in existing sensorless control strategies, a parameter-free model predictive current control is proposed for permanent magnet synchronous motor without any position sensor. First, the current variation during one sampling period is analyzed and divided into two elements: natural attenuation and forced response. Second, recursive least squares algorithm is utilized to estimate the future current variation so that the model predictive current control can be successfully executed paying no attention to motor parameters. Meanwhile, the position information is obtained by the arc tangent function according to the estimated forced response of current variation. At last, experimental results verify that the estimation errors of rotor position are reduced to around 0.1 rad with smaller current prediction error even at low speed where no motor parameters are required.
A fast RLS algorithm for second-order Volterra filter is analyzed. And the contradiction between speed and accuracy of convergence is revealed. The modified RLS algorithm is presented by substituting constant forgetti...
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A fast RLS algorithm for second-order Volterra filter is analyzed. And the contradiction between speed and accuracy of convergence is revealed. The modified RLS algorithm is presented by substituting constant forgetting-factor with function of forgetting-factor constructed in this paper. The rules for constructing forgetting-factor function and the method for selecting parameters is discussed. Finally, a numerical example shows that the modified RLS algorithm result in faster convergence speed by solving the contradiction between speed and accuracy.
Due to the excellent advantages of high speed, high precision, and driving force, piezoelectric actuators nanopositioning systems have been widely used in various micro/nanomachining fields. However, the inherent reso...
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Due to the excellent advantages of high speed, high precision, and driving force, piezoelectric actuators nanopositioning systems have been widely used in various micro/nanomachining fields. However, the inherent resonance dynamic of the nanopositioning system generated by the flexure-hinge greatly deteriorates the positioning performance and limits the closed-loop bandwidth. Even worse, the notch filter for eliminating the effect of resonance does not work due to the varying resonant frequency resulting from the external disturbance or mass load. To this end, an adaptive notch filter for piezo-actuated nanopositioning system via position and online estimate dual-mode (POEDM) has been proposed in this paper, which can estimate the varying resonant frequency in real-time and suppress the resonance to improve the closed-loop bandwidth. First, a novel variable forgetting factor recursiveleastsquares (VFF-RLS) algorithm for estimating resonant frequency online is presented, which is robust to the noise and provides the performances of both fast tracking and stability. Then, a POEDM method is proposed to achieve the online identification of the resonant frequency in the presence of noise and disturbance. Finally, a series of validation simulations are carried out, and the results indicate that, the frequency of input signal and the bandwidth have been achieved up to 12.5% and 87.5% of the first resonant frequency, respectively.
In the battery management system (BMS), the state of charge (SOC) is a very influential factor, which can prevent overcharge and over-discharge of the lithium-ion battery (LIB). This paper proposed a battery modeling ...
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In the battery management system (BMS), the state of charge (SOC) is a very influential factor, which can prevent overcharge and over-discharge of the lithium-ion battery (LIB). This paper proposed a battery modeling and online battery parameter identification method based on the Thevenin equivalent circuit model (ECM) and recursiveleastsquares (RLS) algorithm with forgetting factor. The proposed model proved to have high accuracy. The error between the ECM terminal voltage value and the actual value basically fluctuates between +/- 0.1 V. The extended Kalman filter (EKF) algorithm and the unscented Kalman filter (UKF) algorithm were applied to estimate the SOC of the battery based on the proposed model. The SOC experimental results obtained under dynamic stress test (DST), federal urban driving schedule (FUDS), and US06 cycle conditions were analyzed. The maximum deviation of the SOC based on EKF was 1.4112-2.5988%, and the maximum deviation of the SOC based on UKF was 0.3172-0.3388%. The SOC estimation method based on UKF and RLS provides a smaller deviation and better adaptability in different working conditions, which makes it more implementable in a real-world automobile application.
In this study, an improved adaptive Kalman filter based on auxiliary model (IAKF-AM) is proposed for estimating the state of charge (SOC) with random missing outputs. Since the traditional auxiliary model (AM) method ...
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In this study, an improved adaptive Kalman filter based on auxiliary model (IAKF-AM) is proposed for estimating the state of charge (SOC) with random missing outputs. Since the traditional auxiliary model (AM) method is inefficient for systems with scarce measurements, this paper provides an IAKF-AM method. Compared with the AM method, the proposed method uses the measurable data to adjust missing outputs in each interval, thus has higher estimation accuracy. In addition, a recursiveleastsquares (RLS) algorithm is introduced, which can combine the IAKF-AM method to iteratively estimate the SOC and outputs. In the simulation part, the mean absolute errors (MAE) and the root mean squared error (RMSE) is used to evaluate the model performance under different cases. Simulation example verify the effectiveness of the proposed IAKF-AM algorithm.
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