Adaptive Forgetting Factor Research Articles

Control System Design for an Upper-Limb Rehabilitation Robot

Control system implementation is one of the major difficulties in rehabilitation robot design. The purpose of our study is to present newly developed control strategies for an upper-limb rehabilitation robot. The Barrett WAM Arm manipulator is used as the main hardware platform for the functional recovery training of the past-stroke patient. Passive and active recovery training have been implemented on the WAM Arm. A fuzzy-based PD position control strategy is proposed for the passive recovery exercise to control the WAM Arm stably and smoothly to stretch the impaired limb to move along predefined trajectories. An adaptive impedance force controller is employed in the active motion mode in which a fuzzy logic regulator is used to adjust the desired impedance between the robot and impaired limb to generate adaptive force in agreement with the change of the impaired limb's muscle strength. In order to evaluate the change of the impaired limb's muscle power, the impaired limb's mechanical impedance parameters as an objective evaluation index is estimated online by using a recursive least-squares algorithm with an adaptive forgetting factor. Experimental results demonstrate the effectiveness and potential of the proposed control strategies.

RLS Channel Estimation with Adaptive Forgetting Factor for DS-CDMA Frequency-Domain Equalization

Frequency-domain equalization (FDE) based on the minimum mean square error (MMSE) criterion can increase the downlink bit error rate (BER) performance of DS-CDMA beyond that possible with conventional rake combining in a frequency-selective fading channel. FDE requires accurate channel estimation. Recently, we proposed a pilot-assisted channel estimation (CE) based on the MMSE criterion. Using MMSE-CE, the channel estimation accuracy is almost insensitive to the pilot chip sequence, and a good BER performance is achieved. In this paper, we propose a channel estimation scheme using one-tap recursive least square (RLS) algorithm, where the forgetting factor is adapted to the changing channel condition by the least mean square (LMS)algorithm, for DS-CDMA with FDE. We evaluate the BER performance using RLS-CE with adaptive forgetting factor in a frequency-selective fast Rayleigh fading channel by computer simulation.

Adaptive Forgetting Factor Subarray RLS Beamforming for Multipath Environments

This letter presents an efficient adaptive beamformer to deal with the multipath environments created by signal source scatterings. To improve the performance possible with the fixed forgetting factor, the regular adaptive forgetting factor algorithm is derived and applied to the subarray recursive least squares (RLS) beamforming. Simulations confirm that the proposed scheme has better performance than not only the conventional RLS algorithm but also the subarray RLS and adaptive forgetting factor RLS algorithms.

위상 잡음과 직교 불균형이 있는 OFDM 수신 신호의 보상

OFDM(Orthogonal Frequency Division Multiplexing) 시스템에서 직교 불균형 문제는 송수신기의 front-end에서 발생하며, 성상도에 영향을 주게 되어 BER(Bit Error Rate)을 증가시킨다. 또한, 위상 잡음은 송수신시 국부 발진기에서 발생되는 잡음으로 각 부반송파의 직교성을 깨뜨림으로써 시스템 성능을 크게 저하시킨다. 기존 방식인 PNS(Phase Noise Suppression) 알고리즘은 이러한 위상 잡음을 효과적으로 제거하는 방법이지만 직교 불균형 이동시에 적용되면 오히려 성능이 감소된다. 본 논문에서는 OFDM 시스템의 수신기에서 하향 변환 시 발생하는 직교 불균형과 위상 잡음의 영향을 분석하고, 수신기 FFT(Fast Fourier Transform) 후단에서 파일럿 심볼을 사용하여 CPE를 먼저 제거하고 직교 불균형과 위상 잡음의 성분을 검출하여 등화기의 판정 기준으로 사용하여 보상하는 방법을 제시하였다. 또, 다른 기존 방식들은 FFT 후단에서 추정하고 피드백 시키거나 프리엠블과 같은 시퀀스를 사용하는 방식이지만, 본 논문에서는 FFT 후단에서 MMSE 등화기만을 사용하여 제거하므로 기존의 방법보다 복잡도가 줄어든다. 기존의 위상 잡음 제거 방식에 ICI(Inter Carrier Interference) 제거 기능을 추가하고 직교 불균형 성분을 추출하여 MMSE(Minimum Mean Square Error) 과정 중에 적응 forgetting factor를 적용하면 성능 개선과 직교 불균형 성분의 영향이 줄어들며 성능이 개선됨을 보인다. In the OFDM system, IQ imbalance problem happens at the RF front-end of transceiver, which degrades the BER(bit error rate) performance because it affects the constellation in the received signal. Also, phase noise is generated in the local oscillator of transceivers and it destroys the orthogonality between the subcarriers. Conventional PNS algorithm is effective for phase noise suppression, but it is not useful anymore when there are jointly IQ(In-phase and Quadrature) imbalance and phase noise. Therefore, in this paper, we analyze the effect of IQ imbalance and phase noise generated in the down-conversion of the receiver. Then, we estimate and compensate the IQ imbalance and phase noise at the same time. Compared with the conventional method that IQ imbalance after IFFT is estimated and compensated in front of FFT via the feedback, this proposed method extracts and compensates effect of IQ imbalance after FFT stage. In case IQ imbalance and phase noise exist at the same time, we can decrease complexity because it is needless to use elimination of IQ imbalance in time domain and training sequences and preambles. Also, this method shows that it reduces the ICI and CPE component using adaptive forgetting factor of MMSE after FFT.

RLS channel estimation with adaptive forgetting factor in space-time coded MIMO-OFDM systems

Considering that channel estimation plays a crucial role in coherent detection, this paper addresses a method of Recursive-least-squares (RLS) channel estimation with adaptive forgetting factor in wireless space-time coded multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Because there are three different forgetting factor scenarios including adaptive, two-step and conventional ones applied to RLS channel estimation, this paper describes the principle of RLS channel estimation and analyzes the impact of different forgetting factor scenarios on the performances of RLS channel estimation. Simulation results proved that the RLS algorithm with adaptive forgetting factor (RLS-A) outperformed that with two-step forgetting factor (RLS-T) or with conventional forgetting factor (RLS-C) in both estimation accuracy and robustness over the multiple-input multiple-output (MIMO) channel, i.e., a wide-sense stationary uncorrelated scattering (WSSUS) and frequency-selective slowly fading channel. Hence, we can employ the RLS-A method by adjusting forgetting factor adaptively to track and estimate channel state parameters successfully in space-time coded MIMO-OFDM systems.

Damping of Generator Oscillations Using an Adaptive UPFC-based Controller

In this study a UPFC-based non-linear adaptive controller is proposed and designed to damp generator oscillations. The adaptive controller design is based on determination of control signal using a new linear quadratic pole placement control law and identification of power system parameters with recursive least squares method with adaptive directional forgetting factor. A new model of UPFC has been introduced too. The simulation results show that the adaptive controller can damp power oscillation more effective than the conventional PID controller and it is not sensitive to the loading conditions and the changes in power system topology.

On‐line identification and damage detection in non‐linear structural systems using a variable forgetting factor approach

AbstractThis paper presents an adaptive on‐line identification algorithm based on a newly defined variable forgetting factor approach. At each time step, the proposed methodology, a recursive least‐square (Kalman filter) based algorithm, upgrades the adaptation gain matrix using an adaptive forgetting factor that is expressed as the ratio between the minimum value of the diagonal elements of the adaptation gain matrix and a set of pre‐defined threshold values. This approach requires only acceleration measurements and is particularly robust to the integration errors introduced in determining velocities and displacements. Such an algorithm has been implemented for both parametric and non‐parametric identification studies. For the case of no a priori information on the type of the structural model, an analytical method based on a power series of multivariable polynomial expansions has been introduced. The effectiveness and robustness of the proposed approach has been shown in various numerical simulations, considering the effects of excitation amplitude, of measurement noise, of a priori mass estimation error, and of exact‐, under‐, and over‐parameterization of the structural model. General polynomial‐type non‐linear models such as Duffing and Van der Pole oscillators and Bouc–Wen hysteretic model have been analyzed using the proposed modeling process. The methodology presented in this paper leads to a more accurate and controllable on‐line identification either for the estimation of time‐invariant or time‐varying structural parameters (for damage detection purposes) or for the prediction of the future structural response to dynamic loading. Copyright © 2003 John Wiley & Sons, Ltd.

Adaptive step-size algorithms for blind interference suppression in DS/CDMA systems

This paper develops adaptive step-size blind LMS algorithms and adaptive forgetting factor blind RLS algorithms for code-aided suppression of multiple access interference (MAI) and narrowband interference (NBI) in DS/CDMA systems. These algorithms optimally adapt both the step size (forgetting factor) and the weight vector of the blind linear multiuser detector using the received measurements. Simulations are provided to compare the proposed algorithms with previously studied blind RLS and blind LMS algorithms. They show that the adaptive step-size blind LMS algorithm and adaptive forgetting factor blind RLS algorithm field significant improvements over the standard blind LMS algorithm and blind RLS algorithm in dynamic environments where the number of interferers are time-varying.

ADAPTIVE ROBUST WEIGHTING INPUT ESTIMATION METHOD FOR THE 1-D INVERSE HEAT CONDUCTION PROBLEM

This work presents an adaptive weighting input estimation algorithm that efficiently and robustly on-line estimates time-varied thermal unknowns. While providing for the adaptivity, the Kalman filter allows us to derive a regression equation between the bias innovation and the thermal unknown. Based on this regression model, a recursive least-squares estimator weighting by an adaptive forgetting factor is proposed to extract the unknowns that are defined as the inputs. The maximum-likelihood-type estimator( M estimator) combining the Huber psi function is used to construct the adaptive weighting forgetting factor as a function of biased innovation at each time step, thereby allowing us to estimate the unknown in a system involving measurement noise, modeling error, and unpredictable time-varying changes of the unknowns. In addition, Ike superior capabilities of the proposed algorithm are demonstrated in several time-varying estimate cases and two benchmark performance tests in one-dimensional inverse heat conduction problems. Also presented herein are quantitative performance test comparisons of the proposed algorithm with five inverse methods. Finally, the proposed algorithm simply upgrades the conventional input estimation approach, making it appropriate for implementation purposes.

RLS Algorithm with Variable Fogetting Factor for DecisionFeedback Equalizer over Time-Variant Fading Channels

In a high-rate indoor wireless personal communication system, the delay spread due to multipath propagation results in intersymbol interference (ISI) which can significantly increase the transmission bit error rate (BER). Decision feedback equalizer (DFE) is an efficient approach to combating the ISI. Recursive least squares (RLS) algorithm with a constant forgetting factor is often used to update the tap-coefficient vector of the DFE for ISI-free transmission. However, using a constant forgetting factor may not yield the optimal performance in a nonstationary environment. In this paper, an adaptive algorithm is developed to obtain a time-varying forgetting factor. The forgetting factor is used with the RLS algorithm in a DFE for calculating the tap-coefficient vector in order to minimize the squared equalization error due to input noise and due to channel dynamics. The algorithm is derived based on the argument that, for optimal filtering, the equalization errors should be uncorrelated. The adaptive forgetting factor can be obtained based on on-line equalization error measurements. Computer simulation results demonstrate that better transmission performance can be achieved by using the RLS algorithm with the adaptive forgetting factor than that with a constant forgetting factor previously proposed for optimal steady-state performance or a variable forgetting factor for a near deterministic system.

Uncoupling of Adaptive Fading Kalman Observer Applied To Coupled Transport System

An adaptive fading Kalman observer (AFK) is applied to estimate temperatures along a counter-current heat exchanger. The gain is computed from Riccati equations using an adaptive forgetting factor. The analysis of this gain shows that the estimation error of the tvvo fluids could be decorrelated. Therefore correction could be decoupled even if the model equations are coupled. This result leads to the construction of uncoupling adaptive fading Kalman observer (UAFK). The possible extension of UAFK observer to other coupled transport processes is proposed.

Experimental multivariable adaptive optimization of the steady‐state cellular productivity of a continuous baker's yeast culture

A multivariable adaptive optimization algorithm that uses transient data to improve the optimization speed was successfully implemented on-line to maximize the steady-state cellular productivity of a continuous culture of baker's yeast. The algorithm was shown to be stable even during periods of oscillatory growth and was able to reoptimize the culture when planned disturbances were introduced. Although adaptive tuning of the forgetting factor improved the performance, further refinements in the adaptive forgetting factor algorithm are necessary for completely satisfactory results.

Adaptive Forgetting-factor RLS-based Initialisation Per-tone Equalisation in Discrete Multitone Systems

An adaptive forgetting-factor inverse square-root recursive least squares (AF-iQRRLS) with inverse of correlation matrix updating is presented for per-tone equalisation in discrete multitone-based systems. The proposed inverse covariance update of the square-root covariance Kalman filter is introduced to prepare for the signal flow graph (SFG). This reduced derivation of adaptive inverse square-root recursive least squares algorithm can modify via SFG. In order to reduce the computational complexity, the forgetting-factor parameter for each group called per-group forgettingfactor (PGFF) approach based on AF-iQRRLS algorithm is introduced. The forgetting-factor from the middle of each group is selected as a representative in order to find an optimal forgetting-factor parameter by using AF-iQRRLS algorithm. After convergence, it is fixed for remaining tones of whole group. Simulation results reveal that the trajectories of modified PGFF of the proposed algorithm for each individual tone can converge to their own equilibria. Moreover, the performance of the proposed algorithms are improved as compared with the existing algorithm.