Finite Memory Structure Research Articles

Finite-Memory-Structured Online Training Algorithm for System Identification of Unmanned Aerial Vehicles With Neural Networks

In this article, we propose a novel finite-memory-structured online training algorithm (FiMos-TA) for neural networks to identify and predict the unknown functions and states of an unmanned aerial vehicle (UAV). The proposed FiMos-TA is designed based on a system reconstructed by accumulating the states from the UAV dynamics. The system is redefined by replacing the unknown nonlinear functions of the UAV with neural networks, and a random walk modeling is adopted to design a training algorithm. The proposed FiMos-TA with a finite memory structure updates the weights of the neural network by accumulating the refined measurements of a UAV on the receding horizon. The training law of the proposed FiMos-TA is obtained by introducing the Frobenius norm and confirms a robust performance against modeling uncertainties and identification errors. The robustness and accuracy of the proposed FiMos-TA are verified through experiments.

Distributed Finite Memory Estimation From Relative Measurements for Multiple-Robot Localization in Wireless Sensor Networks

Mobile robot localizations have been extensively studied, and various algorithms for multiple-robot localization have been developed. However, existing methods for multiple-robot localization often exhibit poor performance under harsh conditions, such as missing measurements and sudden appearance of obstacles. To overcome this problem, this paper proposes a novel method for multiple-robot localization in wireless sensor networks. The proposed method is theoretically based on the finite memory estimation and utilizes relative distance and angle measurements between robots. Thus, the proposed method is referred to as distributed finite memory estimation from relative measurements (DFMERM). Due to the finite memory structure, the DFMERM has inherent robustness against computational and modeling errors. Moreover, the novel distributed localization method using relative measurements shows the robustness against missing measurements. Robust DFMERM localization performance is experimentally demonstrated using multiple mobile robots under the harsh conditions.

Fuzzy Finite Memory State Estimation for Electro-Hydraulic Active Suspension Systems

This paper presents a novel nonlinear estimator called the fuzzy finite memory (FFM) state estimator for electro-hydraulic active suspension systems, based on fuzzy techniques and finite impulse response. The Takagi-Sugeno fuzzy model is introduced to effectively describe highly nonlinear suspension systems with electro-hydraulic actuator dynamics. Compared with the conventional state estimator, which has an infinite memory structure and requires whole data from the initial to current time, the proposed fuzzy state estimator with a finite memory structure guarantees robustness against external disturbances and modeling uncertainty. The simulation results verify that the developed fuzzy finite memory state estimator is more robust under external disturbances and modeling uncertainties than the existing infinite impulse response nonlinear estimator.

A More Configurable Finite Memory Event Triggering Control Scheme for Networked Control Systems

For a networked control system (NCS), this article proposes a more configurable event triggering control scheme that employs a finite memory structure with tuning parameters for efficiently utilizing network resources in transient and steady-state responses. The proposed finite memory event triggering control scheme (FMETS) uses the current and past states on the recent finite time horizon to asymptotically stabilize a closed-loop system. Tuning parameters can be configured according to the forgetting level, the triggering threshold, and the memory size. Since the FMETS takes a sample-and-hold approach, the undesirable Zeno effect can be avoided automatically. To use an efficient computation solver, the proposed FMETS is formulated into linear matrix inequalities (LMIs). It is shown through a simulation study that the proposed FMETS provides less conservative results and furthermore enables effective configuration for high control performance and efficient network resource utilization.

Selective Finite Memory Structure Filtering Using the Chi-Square Test Statistic for Temporarily Uncertain Systems

In this paper, a finite memory structure (FMS) filtering with two kinds of measurement windows is proposed using the chi-square test statistic to cover nominal systems as well as temporarily uncertain systems. First, the simple matrix form for the FMS filter is developed from the conditional density of the current state given finite past measurements. Then, one of the two FMS filters, the primary FMS filter or the secondary FMS filter, with different measurement windows is operated selectively according to the presence or absence of uncertainty, to obtain a valid estimate. The primary FMS filter is selected for the nominal system and the secondary FMS filter is selected for the temporarily uncertain system, respectively. A declaration rule is defined to indicate the presence or absence of uncertainty, operate the suitable one from two filters, and then obtain the valid filtered estimate. A test variable for the declaration rule is developed using a chi-square test statistic from the estimation error and compared to a precomputed threshold. In order to verify the proposed selective FMS filtering and compare with the existing FMS filter and the infinite memory structure (IMS) filter, computer simulations are performed for a selection of dynamic systems including a F404 gas turbine aircraft engine and an electric motor. Simulation results confirm that the proposed selective FMS filtering works well for nominal systems as well as temporarily uncertain systems. In addition, the proposed selective FMS filtering is shown to be remarkably better than the IMS filtering for the temporarily uncertain system.

Finite Memory Structure Filtering and Smoothing for Target Tracking in Wireless Network Environments

In this paper, a state estimation problem is considered for a target tracking scheme in wireless network environments. Firstly, a unified algorithm of finite memory structure (FMS) filtering and smoothing is proposed for a discrete-time state-space model. As shown in the terminology unified, both FMS filter and smoother are derived by solving one optimization problem directly with incorporation of the unbiasedness constraint. Hence, the unified algorithm provides simultaneously the current state estimate as well as the lagged state estimate using only finite measurements and inputs on the most recent window. The proposed unified algorithm of FMS filtering and smoothing shows that there are some unique properties such as unbiasedness, deadbeat, time-invariance and intrinsic robustness, which cannot be obtained by the recursive infinite memory structure (IMS) filtering such as Kalman filter. The on-line computational complexity of the proposed unified algorithm is discussed. Secondly, as an application of the proposed unified algorithm, a target tracking scheme in wireless network environments is considered via computer simulations for moving target’s accelerations of various shapes. The proposed unified algorithm-based target tracking scheme provides estimates for position as well as acceleration of moving target in real time, while eliminating unwanted noise effects and maintaining desired moving positions. Due to intrinsic robustness and deadbeat properties, the proposed unified algorithm-based scheme can outperform the existing IMS filtering-based scheme when acceleration suddenly changes.

일시적 불확실성을 갖는 DC 모터 시스템을 위한 유한 기억 구조 필터링

In this paper, the finite memory structure (FMS) filter is applied for the estimation filtering of DC motor systems with temporary uncertainties. A couple of temporary uncertainties, model uncertainty and unknown input, are considered for the FMS filtering. Extensive computer simulations are performed for both nominal system and temporarily uncertain system. It is shown that the FMS filter can be better than the infinite memory structure (IMS) filter for temporary uncertainties. Of course, the IMS filter can outperform FMS filters after the effect of temporary uncertainties completely disappears. It is also shown that there can exist the trade-off between the estimation error and the tracking ability in terms of the window length.

일시적 불확실성을 갖는 DC 모터 시스템을 위한 유한 기억 구조 필터링

In this paper, the finite memory structure (FMS) filter is applied for the estimation filtering of DC motor systems with temporary uncertainties. A couple of temporary uncertainties, model uncertainty and unknown input, are considered for the FMS filtering. Extensive computer simulations are performed for both nominal system and temporarily uncertain system. It is shown that the FMS filter can be better than the infinite memory structure (IMS) filter for temporary uncertainties. Of course, the IMS filter can outperform FMS filters after the effect of temporary uncertainties completely disappears. It is also shown that there can exist the trade-off between the estimation error and the tracking ability in terms of the window length.

Finite Memory Output Feedback Control for Unmanned Aerial Vehicle

In this paper, we propose a novel control strategy, a finite memory output feedback control (FMOFC), for an unmanned aerial vehicle (UAV). The proposed control strategy is designed with the estimated states obtained by stacking a finite $N$ number of measurements, and the finite $N$ number is defined as the horizon size, which represents the finite memory structure of the system. Through extending the concept of the horizon size by introducing a vector form to the augmented horizon size, the UAV is considered as a parallel system in terms of four independent states. Furthermore, the controller gain of the FMOFC is derived from the reconstructed parallel system under not only the finite memory structure, but also unbiasedness. Because of those constraints, the FMOFC exhibits a robust performance even in the presence of disturbance or unexpected noises from uncertainties, computational errors, and sudden changes in environments. The fast convergence and robust performance under disturbance or unexpected noises of the proposed control strategy are demonstrated through experimental results.

A Design of Finite Memory Residual Generation Filter for Sensor Fault Detection

Abstract In the current paper, a residual generation filter with finite memory structure is proposed for sensor fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite measurements and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noisefree systems. The proposed residual generation filter is specified to the digital filter structure for the amenability to hardware implementation. Finally, to illustrate the capability of the proposed residual generation filter, extensive simulations are performed for the discretized DC motor system with two types of sensor faults, incipient soft bias-type fault and abrupt bias-type fault. In particular, according to diverse noise levels and windows lengths, meaningful simulation results are given for the abrupt bias-type fault.

A finite memory structure filtering for indoor positioning in wireless sensor networks with measurement delay

In this article, an alternative indoor positioning mechanism is proposed considering finite memory structure filter as well as measurement delay. First, a finite memory structure filter with a measurement delay is designed for the indoor positioning mechanism under a weighted least-squares criterion, which utilizes only finite measurements on the most recent window. The proposed finite memory structure filtering–based mechanism gives the filtered estimates for position, velocity, and acceleration of moving target in real time, while removing undesired noisy effects and preserving desired moving positions. Second, the proposed mechanism is shown to have good inherent properties such as unbiasedness, efficiency, time-invariance, deadbeat, and robustness due to the finite memory structure. Third, through discussions about the choice of window length, it is shown that this can be considered as a useful design parameter to make the performance of the proposed mechanism as good as possible. Finally, computer simulations show that the performance of the proposed finite memory structure filtering–based mechanism can outperform the existing infinite memory structure filtering–based mechanism for the abruptly varying acceleration of moving target.

A Finite Memory Structure Smoother with Recursive Form Using Forgetting Factor

This paper proposes an alternative finite memory structure (FMS) smoother with a recursive form under a least squares criterion using a forgetting factor strategy. The proposed FMS smoother does not require information of the noise covariances as well as the initial state. The proposed FMS smoother is shown to have good inherent properties such as time-invariance, unbiasedness, and deadbeat. The forgetting factor is shown to be considered as useful parameter to make the estimation performance of the proposed FMS smoother as good as possible. Through computer simulations for the F-404 engine system, it is shown that the proposed FMS smoother can be better than the existing FMS smoother for incorrect noise covariances and the IMS smoother for temporary uncertainties.

A computationally efficient fixed-lag smoother using recent finite measurements

This paper proposes a computationally efficient fixed-lag smoother with finite memory structure for discrete-time systems. The proposed fixed-lag smoother is developed under a weighted least square criterion using only the most recent finite measurements on the window. The proposed fixed-lag smoother is shown to have inherent properties such as unbiasedness, deadbeat, time-invariance, and robustness. The proposed fixed-lag smoother is shown to be much less computationally complex than the existing fixed-lag Kalman smoother with infinite memory structure. Since the window length can significantly affect the estimation performance as well as the on-line computational complexity, a guideline for choosing the appropriate window length is introduced. Using computer simulations, the proposed fixed-lag smoother is shown to be comparable to the fixed-lag Kalman smoother for the nominal system and superior to that for the temporarily uncertain system.

Minimal and maximal bi-ideals in ordered ternary semigroups

For estimating a time-varying signal frequency, an alternative estimator with a finite memory structure is proposed. The proposed estimator is developed under a maximum likelihood criterion using only the most recent finite observations on the window. The proposed estimator is first represented in a batch form, and then in a recursive form for computational advantage. The proposed estimator is shown to have good inherent properties, such as unbiasedness and deadbeat. Finally, via computer simulation and comparison, the proposed approach is shown to outperform remarkably the variable forgetting factor (VFF) Kalman filtering approach. Key words: Estimating signal frequency, finite memory structure, unbiasedness, deadbeat.

<formula formulatype="inline"><tex>${\cal H}_{\infty}$</tex> </formula> Finite Memory Controls for Linear Discrete-Time State-Space Models

In this brief, we propose a new type of output feedback control, called a Hinfin finite memory control (HFMC), for discrete-time state-space systems. Some constraints, such as linearity, unbiasedness to the optimal state feedback control, and finite memory structure with respect to an input and an output are required in advance. Among the controls needed with these requirements, we choose the HFMC to optimize the Hinfin performance criterion. The HFMC is obtained by solving the linear matrix inequality problem with a parameterization of a linear equality constraint. We show through simulation that the HFMC is more robust against uncertainties, and is faster in convergence than existing Hinfin output feedback controls

RECEDING HORIZON FINITE MEMORY OUTPUT TRACKING CONTROL AND ITS APPLICATION TO INVERTED PENDULUM CONTROL

In this paper, a new type of output feedback tracking control, called a receding horizon finite memory output tracking control (RHFMOTC), is proposed for discrete-time state-space systems. Constraints such as linearity and finite memory structure with respect to inputs/outputs and future output references, and unbiasedness from the optimal state feedback tracking control are required in advance. The RHFMOTC is chosen to minimize a receding horizon LQG tracking performance criterion with these constraints. The stability and the separation principle of the RHFMOTC are investigated. It is shown through application to inverted pendulum control that the RHFMOTC has the better tracking performance than the linear quadratic Gaussian tracking control (LQGTC).

RECEDING HORIZON FINITE MEMORY CONTROLS FOR OUTPUT FEEDBACK CONTROLS OF DISCRETE-TIME STATE SPACE SYSTEMS

In this paper, a new type of output feedback control, called a receding horizon finite memory control (RHFMC), is proposed for stochastic discrete-time state space systems. Constraints such as linearity and finite memory structure with respect to an input and an output, and unbiasedness from the optimal state feedback control are required in advance. The proposed RHFMC is chosen to minimize an optimal criterion with these constraints. The RHFMC is obtained in an explicit closed form using the output and input information on the recent time interval. It is shown that the RHFMC consists of a receding horizon control and an FIR filter. The stability of the RHFMC is investigated for stochastic systems.

Receding Horizon Finite Memory Controls for Output Feedback Controls of State–Space Systems

In this paper, a new type of control, called a receding horizon finite memory control (RHFMC) or a model predictive finite memory control, is proposed as an optimal output feedback control for stochastic state-space systems. Constraints such as linearity, finite memory structure, and unbiasedness from the optimal state feedback control are required in advance, and in addition, the performance criterion of quadratic cost is required. Constraints for the input and the state are not assumed in this paper. The RHFMC is obtained directly by minimizing the performance criterion for stochastic state-space systems with the previous constraints. It is shown that the RHFMC can be separated into a receding horizon control and a finite-impulse response filter. The stability of the RHFMC is investigated. The validity of the proposed RHFMC is illustrated by a numerical example.

Importance sampling simulation for evaluating lower-bound symbol error rate of the Bayesian DFE with multilevel signaling schemes

For the class of equalizers that employs a symbol-decision finite-memory structure with decision feedback, the optimal solution is known to be the Bayesian decision feedback equalizer (DFE). The complexity of the Bayesian DFE, however, increases exponentially with the length of the channel impulse response (CIR) and the size of the symbol constellation. Conventional Monte Carlo simulation for evaluating the symbol error rate (SER) of the Bayesian DFE becomes impossible for high channel signal-to-noise ratio (SNR) conditions. It has been noted that the optimal Bayesian decision boundary separating any two neighboring signal classes is asymptotically piecewise linear and consists of several hyperplanes when the SNR tends to infinity. This asymptotic property can be exploited for efficient simulation of the Bayesian DFE. An importance sampling (IS) simulation technique is presented based on this asymptotic property for evaluating the lower bound SER of the Bayesian DFE with a multilevel pulse amplitude modulation (M-PAM) scheme under the assumption of correct decisions being fed back. A design procedure is developed, which chooses appropriate bias vectors for the simulation density to ensure asymptotic efficiency (AE) of the IS simulation.

Interpolative multiresolution coding of advance television with compatible subchannels

A multiresolution representation for video signals is introduced. A three-dimensional spatiotemporal pyramid algorithms for high-quality compression of advanced television sequences is presented. The scheme utilizes a finite memory structure and is robust to channel errors, provides compatible subchannels, and can handle different scan formats, making it well suited for the broadcast environment. Additional features such as fast random access and reverse playback make it suitable for digital storage as well. Model-based processing is used both over space and time, where motion-based interpolation is used. Interpolation in an FIR (finite impulse response) scheme solves uncovered area problems, considerably improving the temporal prediction. The complexity is comparable to that of previous recursive schemes. Computer simulations indicate that high compression factors (about an order of magnitude) are easily achieved with no apparent loss of quality. The scheme also has a number of commonalities with the emerging MPEG standard. >