Memory Filter Research Articles

High-Definition Dynamic Voltage Restorer Systems Using Equivalent Time Sampling Techniques and Circular Structural Memory Filters

Due to advances in power electronics technology and the evolution of automation devices, the number of electrical devices that are sensitive to power quality is rapidly increasing, and for this reason, users are increasing their demand for high quality. To meet power quality demands, many power conversion devices are used, including dynamic voltage restorers (DVRs). DVRs are recognized as devices that can effectively manage problems such as voltage segments, swells, and harmonics. DVR control requires many samples for harmonic compensation, which has the disadvantage of being complicated to implement due to fast digital signal processing computation and the application of the cyclic discrete Fourier transform. In this paper, a high-precision DVR system configuration is proposed that compensates for harmonics using a periodically equivalent time-interval sampling technique and a novel circular-structured memory filter. The proposed circular-structured multi-pointer memory filter is an effective filter algorithm for high-precision input voltage measurement because it can remove noise and compensate for the delay of the phase angle of the filter in voltage measurement. A simulation and DVR prototype system were built, and the feasibility and effectiveness of the phase angle multi-filter voltage detection method and the compensation method were verified by experiments.

Limited Memory-Based Random-Weighted Kalman Filter.

The Kalman filter is an important technique for system state estimation. It requires the exact knowledge of system noise statistics to achieve optimal state estimation. However, in practice, this knowledge is often unknown or inaccurate due to uncertainties and disturbances involved in the dynamic environment, leading to degraded or even divergent filtering solutions. To address this issue, this paper presents a new method by combining the random weighting concept with the limited memory technique to accurately estimate system noise statistics. To avoid the influence of excessive historical information on state estimation, random weighting theories are established based on the limited memory technique to estimate both process noise and measurement noise statistics within a limited memory. Subsequently, the estimated system noise statistics are fed back into the Kalman filtering process for system state estimation. The proposed method improves the Kalman filtering accuracy by adaptively adjusting the weights of system noise statistics within a limited memory to suppress the interference of system noise on system state estimation. Simulations and experiments as well as comparison analysis were conducted, demonstrating that the proposed method can overcome the disadvantage of the traditional limited memory filter, leading to im-proved accuracy for system state estimation.

Finite-time filtering design with past output measurements for interval type-2 fuzzy systems: A descriptor approach

This paper is interested in the design of full-order H∞ memory filters for discrete-time nonlinear systems within finite-time domain. First, the investigated nonlinear systems are modeled by a Takagi–Sugeno fuzzy technique for the interval type-2 case. And to reduce the size of the model, only the past measurements of systems are involved during the memory filters construction. Then, novel design processes with less conservatism are proposed through executing a decoupling transformation for the filtering error dynamics. Based on Lyapunov methods together with several strategies that apply convex optimization techniques and slack variables, some sufficient conditions are presented and certified to solve the memory filter parameters, such that the augmented error dynamics are singular finite-time bounded with prescribed H∞ performance. Finally, an example is introduced to illustrate the effectiveness of the proposed strategy in contrast to existent works.

Vehicle state and parameter estimation based on improved extend Kalman filter

In order to reduce the influence of historical measurement data errors in the process of vehicle state estimation and improve the accuracy of the vehicle state estimation, a limited memory random weighted extended Kalman filter (LMRWEKF) algorithm is proposed. Firstly, a 3-DOF nonlinear vehicle dynamics model is established. Secondly, the limited memory extended Kalman filter is formed by fusing the limited memory filter and the extended Kalman filter. Then, according to the random weighting theory, the weighting coefficients that obey Dirichlet distribution are introduced to further improve the filtering estimation accuracy. Finally, a virtual test based on the ADAMS/CAR is used for the experimental verification. The results show that the error in the longitudinal velocity and the yaw rate is small, especially in the mean value of estimation error of side slip angle which is different in just 0.015 degrees between the virtual test and the simulation result. And also, the results compared with traditional methods indicate that the proposed LMRWEKF algorithm can solve the problem of vehicle state estimation with the performance of noise fluctuation suppression and higher estimation accuracy. The mean absolute error (MAE) and root mean square error (RMSE) are considered to verify the estimation accuracy of the proposed algorithm. And the comparison results indicate that the estimation accuracy of the LMRWEKF algorithm is significantly higher than those of the EKF and DEKF methods.

Finite-Frequency $\mathcal {H}_{-}/\mathcal {H}_{\infty }$ Memory Fault Detection Filtering Design for Uncertain Takagi–Sugeno Fuzzy Affine Systems

This article is concerned with the finite-frequency <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{-}/\mathcal {H}_{\infty }$</tex-math></inline-formula> memory fault detection filtering problem for discrete-time Takagi&#x2013;Sugeno fuzzy affine systems with norm-bounded uncertainties. The objective is to design a piecewise affine memory filter by using system historical information such that the resulting closed-loop filtering error system is asymptotically stable with the prescribed finite-frequency <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{-}/\mathcal {H}_{\infty }$</tex-math></inline-formula> performance. Based on the generalized Kalman&#x2013;Yakubovi&#x010D;&#x2013;Popov lemma combined with the celebrated <inline-formula><tex-math notation="LaTeX">$\mathcal {S}$</tex-math></inline-formula>-procedure, new sufficient conditions for the fuzzy affine filtering error system to have the finite-frequency <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{-}/\mathcal {H}_{\infty }$</tex-math></inline-formula> performance are given at first. By further using piecewise fuzzy quadratic Lyapunov functions and Projection lemma, the filtering analysis results for the filtering error system to be asymptotically stable with the prescribed finite-frequency <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{-}/\mathcal {H}_{\infty }$</tex-math></inline-formula> performance are obtained. Then, the filtering synthesis is carried out with the aid of matrix inequality convexification techniques, and the synthesis results are described in terms of linear matrix inequalities. It is further shown that a better filtering performance can be achieved by using more system historical information. Finally, simulation is provided to verify the effectiveness of the proposed approach.

Reducing echo state network size with controllability matrices.

Echo state networks are a fast training variant of recurrent neural networks excelling at approximating nonlinear dynamical systems and time series prediction. These machine learning models act as nonlinear fading memory filters. While these models benefit from quick training and low complexity, computation demands from a large reservoir matrix are a bottleneck. Using control theory, a reduced size replacement reservoir matrix is found. Starting from a large, task-effective reservoir matrix, we form a controllability matrix whose rank indicates the active sub-manifold and candidate replacement reservoir size. Resulting time speed-ups and reduced memory usage come with minimal error increase to chaotic climate reconstruction or short term prediction. Experiments are performed on simple time series signals and the Lorenz-1963 and Mackey-Glass complex chaotic signals. Observing low error models shows variation of active rank and memory along a sequence of predictions.

Discrete-Time Signatures and Randomness in Reservoir Computing.

A new explanation of the geometric nature of the reservoir computing (RC) phenomenon is presented. RC is understood in the literature as the possibility of approximating input-output systems with randomly chosen recurrent neural systems and a trained linear readout layer. Light is shed on this phenomenon by constructing what is called strongly universal reservoir systems as random projections of a family of state-space systems that generate Volterra series expansions. This procedure yields a state-affine reservoir system with randomly generated coefficients in a dimension that is logarithmically reduced with respect to the original system. This reservoir system is able to approximate any element in the fading memory filters class just by training a different linear readout for each different filter. Explicit expressions for the probability distributions needed in the generation of the projected reservoir system are stated, and bounds for the committed approximation error are provided.

A Hybrid Visual Tracking Algorithm Based on SOM Network and Correlation Filter.

To meet the challenge of video target tracking, based on a self-organization mapping network (SOM) and correlation filter, a long-term visual tracking algorithm is proposed. Objects in different videos or images often have completely different appearance, therefore, the self-organization mapping neural network with the characteristics of signal processing mechanism of human brain neurons is used to perform adaptive and unsupervised features learning. A reliable method of robust target tracking is proposed, based on multiple adaptive correlation filters with a memory function of target appearance at the same time. Filters in our method have different updating strategies and can carry out long-term tracking cooperatively. The first is the displacement filter, a kernelized correlation filter that combines contextual characteristics to precisely locate and track targets. Secondly, the scale filters are used to predict the changing scale of a target. Finally, the memory filter is used to maintain the appearance of the target in long-term memory and judge whether the target has failed to track. If the tracking fails, the incremental learning detector is used to recover the target tracking in the way of sliding window. Several experiments show that our method can effectively solve the tracking problems such as severe occlusion, target loss and scale change, and is superior to the state-of-the-art methods in the aspects of efficiency, accuracy and robustness.

A Dynamic-Weighted Attenuation Memory Extended Kalman Filter Algorithm and Its Application in the Underwater Positioning

Extended Kalman filter (EKF) plays an important role in the acoustic signal processing of underwater positioning. However, accumulative errors and model inaccuracies lead to divergence. Then, attenuation memory EKF is created in response to this issue which needs to manually select all or part of the parameters. Thus, a dynamic-weighted attenuation memory EKF is proposed. Firstly, several underwater positioning simulations under different conditions are carried out. Results show, with the change of parameter conditions in positioning, the ideal attenuation coefficient changes between 0.5 and 1, but it is difficult to express it in function formula or statistical form. Secondly, a dynamic selection method of attenuation factor is designed. In the later contrast simulation, the proposed method has improved the positioning performance compared with the existing attenuation memory filter algorithm. Finally, the results of physical model verification experiment show that the dynamic-weighted attenuation memory EKF algorithm not only suppresses divergence better but also avoids the subjectivity of attenuation coefficient selection to a certain extent.

Robust finite frequency H∞ distributed memory filter design for networked control systems with polytopic uncertainty

AbstractThis paper addresses the robust finite frequency (FF) distributed filtering problem for uncertain networked control system (NCS) that consists of heterogeneous subsystems. By using past output measurements and considering the frequency information of disturbance signals, the designed distributed memory filter is able to guarantee that the filtering error system is well‐posed, stable and has the FF performance. Based on the extended finite frequency bounded realness lemma for uncertain NCSs, the existence condition of robust FF distributed memory filter is derived. Moreover, it is shown that by taking advantage of more past output measurements of NCS, the proposed design procedure can derive a distributed filter that achieves a lower performance bound. Finally, a practical example is given to show the effectiveness of the proposed filter design method.

Finite-frequency memory filter design for uncertain linear discrete-time systems: A polynomially parameter-dependent approach

This paper investigates the robust filtering problem for linear uncertain systems affected by noises in restricted frequency intervals. Different from traditional filter schemes, a finite-frequency memory filter is designed to generalize conventional memoryless ones in such a way that a sequence of latest output measurements are employed for current estimation. To be specific, a memory filter is sought which ensures that for all admissible uncertainties, the filtering error system is asymptotically stable with a prescribed noise-attenuation level in the restricted frequency range. To accomplish this, the finite-frequency specification is characterized by the generalized Kalman–Yakubovich–Popov (KYP) lemma, aiming at improving the capability of noise-attenuation over the given frequency range. Moreover, the homogeneous polynomially parameter-dependent technique is adopted to facilitate filter design and reduce conservatism further. Based on the proposed scheme, we prove rigorously that additional past output measurements contribute to less conservative results. Finally, a quarter-car model with an active-suspension system is used to validate the proposed scheme.

Fault-tolerant H∞ filtering for fuzzy networked control systems with quantisation effects

ABSTRACTThe issue of memory filter design is investigated for a class of discrete-time Takagi–Sugeno (T-S) fuzzy networked control systems with time-varying delay and quantisation effects. The main objective of this paper is to formulate an memory filter that can guarantee the resulting filtering error system to be finite-time bounded. Further, by considering the communication limitations, the state signals are quantised before transmission through a logarithmic quantiser and the quantisation error is considered in the filter design. In particular, the quantisation effects are dealt by employing the generalised sector bound approach. Based on Lyapunov stability theory, novel sufficient constraints are established in the form of linear matrix inequalities, to ensure the finite-time boundedness of the augmented filtering error system. Then, a new fault-tolerant memory filter is formulated and also the filter gain parameters are attained by solving the derived LMI based constraints. Finally, a numerical example is furnished to support the efficacy and applicability of the developed filter design.

Polarizing Voltage Generating Method for Distance and Directional Protection Elements

Distance and directional elements are widely used in power system protection. One of the used essential signals, having a substantial impact on proper operation, is polarizing voltage. During faults close to a protection relay, memory filters can be used to ensure the availability of this voltage. A basic assumption of these memory filters is that during a fault only an amplitude of a measured voltage changes rapidly and other parameters change very slow. The assumption is correct in power systems with an overwhelming share of traditional rotating generators. However, today, more and more energy sources are connected to power systems through inverters. Algorithms implemented in inverters can control parameters of output voltage so that they can be changed rapidly without any inertia. Therefore, a source of robust polarizing voltage is needed to ensure proper operation of distance and directional elements. The paper presents a method of a reference signal generation based on phase-locked loop (PLL). The algorithm consists of PLL with an adaptive notch filter, and elements adding inertia to the generated signal. The reference signal can be used as a polarizing voltage in protection elements. The paper describes all elements of the algorithm and presets operational tests.

Reward-related distracters and working memory filtering.

Reward-related stimuli capture attention, even when they are task irrelevant. A consequence of attentional prioritization of reward-related stimuli is that they may also have preferential access to working memory like other forms of emotional information. However, whether reward-related distracters leak into working memory remains unknown. Here, using a well-validated change detection task of visual working memory capacity and filtering, we conducted two studies to directly assess the impact of reward-related distracters on working memory. In both studies, the distracters consisted of colored bars or circles that were previously associated with monetary reward. In Experiment 1, results indicated that previously rewarded distracters did not impact behavioral measures of working memory filtering efficiency compared to neutral distracters. In Experiment 2, using ERPs, we measured the contralateral delay activity (CDA), a psychophysiological index of the number of items retained in working memory, to further assess filtering efficiency. We observed that the CDA for high reward distracters was similar to low reward and neutral distracters. However, in early trials, behavioral measures revealed that previously rewarded stimuli negatively impacted working memory capacity, an effect not observed with neutral distracters. This effect, though, was not found for the CDA in early trials. In summary, our findings across two studies suggest that attentional capture by task-irrelevant reward may have minimal impact on visual working memory-findings that have important implications for delineating the boundaries of reward-cognition interactions.

Hybrid Particle/Extended Finite Memory Filter to Improve Target Tracking Accuracy of Radar Measurement in Harsh Environments

Although the particle filter (PF) generally provides accurate estimation, it fails in harsh environments, such as severe signal noise and/or abrupt state change. The PF also requires a number of particles for accurate estimation, causing heavy computational burden. Therefore, it is difficult to use the PF for real applications. To overcome PF drawbacks, we propose the extended finite memory (EFM) filter and hybrid particle filtering algorithm combining the regularized particle filter (RPF) as the main filter with an auxiliary EFM filter. The hybrid particle/EFM filter can detect RPF failure and reset the particles using an EFM estimation. The proposed filter shows robust performance against severe signal noise and abrupt change of target motion. Experiments using vehicle radar signals were performed in harsh environments to compare the proposed tracker with current best practice regularized particle and extended Kalman trackers.

Quadrotor aircraft attitude control algorithm based on improved UKF

The quadrotor aircraft attitude estimation algorithm which is based on the adaptive attenuation memory unscented Kalman filter algorithm is proposed. The UKF algorithm uses the quaternion method to solve the attitude. The drift output of the gyroscope amplified as part of the state variable, and the attitude angle is used as the measurement variable to establish a filtering model, which effectively suppresses the attitude error caused by the gyro drift. The attenuation memory filter and adaptive algorithm are introduced based on the UKF algorithm to solve the problem of the accuracy degradation of the system model when it is abnormally interfered. The filter divergence is suppressed and the robustness and stability of the system are improved. The simulation results show that the algorithm can effectively improve the attitude estimation accuracy of the aircraft compared with the traditional UKF algorithm.

Receding horizon observer for linear time-varying systems

In this paper, we present a receding horizon observer for linear time-varying systems. Our main contribution is that known deterministic input has been fully dealt with. This poses considerable challenges for recursive formulation of the filter. The suggested observer can be used in closed-loop feedback control systems. The existing finite memory filters lack this ability. First- and second-order statistical convergence analysis carried out in this paper provide an insight into the stochastic behaviour of the observer. Some examples demonstrate the utility of the proposed filter.

Echo state networks are universal

This paper shows that echo state networks are universal uniform approximants in the context of discrete-time fading memory filters with uniformly bounded inputs defined on negative infinite times. This result guarantees that any fading memory input/output system in discrete time can be realized as a simple finite-dimensional neural network-type state-space model with a static linear readout map. This approximation is valid for infinite time intervals. The proof of this statement is based on fundamental results, also presented in this work, about the topological nature of the fading memory property and about reservoir computing systems generated by continuous reservoir maps.

The Drosophila Receptor Tyrosine Kinase Alk Constrains Long-Term Memory Formation.

In addition to mechanisms promoting protein-synthesis-dependent long-term memory (PSD-LTM), the process appears to also be specifically constrained. We present evidence that the highly conserved receptor tyrosine kinase dAlk is a novel PSD-LTM attenuator in Drosophila Reduction of dAlk levels in adult α/β mushroom body (MB) neurons during conditioning elevates LTM, whereas its overexpression impairs it. Unlike other memory suppressor proteins and miRNAs, dAlk within the MBs constrains PSD-LTM specifically but constrains learning outside the MBs as previously shown. Dendritic dAlk levels rise rapidly in MB neurons upon conditioning, a process apparently controlled by the 3'UTR of its mRNA, and interruption of the 3'UTR leads to enhanced LTM. Because its activating ligand Jeb is dispensable for LTM attenuation, we propose that postconditioning elevation of dAlk within α/β dendrites results in its autoactivation and constrains formation of the energy costly PSD-LTM, acting as a novel memory filter.SIGNIFICANCE STATEMENT In addition to the widely studied molecular mechanisms promoting protein-synthesis-dependent long-term memory (PSD-LTM), recent discoveries indicate that the process is also specifically constrained. We describe a role in PSD-LTM constraint for the first receptor tyrosine kinase (RTK) involved in olfactory memory in Drosophila Unlike other memory suppressor proteins and miRNAs, dAlk limits specifically PSD-LTM formation as it does not affect 3 h, or anesthesia-resistant memory. Significantly, we show conditioning-dependent dAlk elevation within the mushroom body dendrites and propose that its local abundance may activate its kinase activity, to mediate imposition of PSD-LTM constraints through yet unknown mechanisms.

Notice of Violation of IEEE Publication Principles: Fuzzy-Affine-Model-Based Memory Filter Design of Nonlinear Systems With Time-Varying Delay

This paper studies the piecewise-affine memory $\mathscr {H}_{\infty }$ filtering problem for nonlinear systems with time-varying delay in a delay-dependent framework. The nonlinear plant is characterized by a continuous-time Takagi–Sugeno fuzzy-affine model with parametric uncertainties. The purpose is to develop a new approach for filter synthesis procedure with less conservatism. Specifically, by constructing a novel Lyapunov–Krasovskii functional, together with a Wirtinger-based integral inequality, reciprocally convex inequality and S-procedure, an improved criterion is first attained for analyzing the $\mathscr {H}_{\infty }$ performance of the filtering error system, and then via some linearization techniques, the piecewise-affine memory filter synthesis is carried out. It is shown that the existence of desired filter gains can be explicitly determined by the solution of a convex optimization problem. Finally, simulation studies are presented to reveal the effectiveness and less conservatism of the developed approaches. It is anticipated that the proposed scheme can be further extended to the analysis and synthesis of continuous-time fuzzy-affine dynamic systems with integrated communication delays in the networked circumstance.