Adaptive Identification Algorithm Research Articles

Initial excitation based discrete-time multi-model adaptive online identification

This paper proposes a novel multi-model adaptive identification (MMAI) algorithm for discrete-time linear time invariant (LTI) uncertain systems with tunable performance. The uncertain plant parameter vector is assumed to belong to a known convex hull of a finite number of vertices; these vertices are considered as initial choice of vertices of an adaptive model parameter set. The estimated parameter, corresponding to the uncertain plant parameter, is computed at every instant as a convex combination of the model set vertices. To update the vertices of the adaptive model parameter set, a switched adaptive update law is proposed, along with a novel discrete-time initial excitation (IE) condition, which is imposed on the regressor signal. The proposed discrete-time IE condition is online verifiable and is milder than persistence of excitation (PE) condition, required for parameter convergence in classical adaptive estimation routines. The switched adaptive law guarantees exponential convergence of the vertices of the model set as well as the estimated parameter, to the true plant parameter, provided the regressor signal satisfies the IE condition. The properties of the designed MMAI strategy are validated through suitable simulation examples.

Classical Preisach Model Based on Polynomial Approximation and Applied to Micro-Piezoelectric Actuators

In engineering applications, where we demand more and more precision, the modeling of systems with hysteretic nonlinearity has received considerable attention. The classical Preisach model (CPM) is currently the most popular for characterizing systems with hysteresis, and this model can represent the hysteresis with an infinite but countable first-order inversion curve (FORC). The table method is a method used to realize CPM in practice. The data in the table corresponds to a limited number of FORC samples. There are two problems with this approach: First, in order to reflect the timing effects of elements with hysteresis, it needs to consume a lot of memory space to obtain accurate data table. Second, it is difficult to come up with an efficient way to modify the data table to reflect the timing effects of elements with hysteresis. To overcome these shortcomings, this paper proposes to use a set of polynomials instead of the table method to implement the CPM. The proposed method only needs to store a small number of polynomial coefficients, and thus it reduces the required memory usage. In addition, to obtain polynomial coefficients, we can use least squares approximation or adaptive identification algorithms, which can track hysteresis model parameters. We developed an adaptive algorithm for the identification of polynomial coefficients of micro-piezoelectric actuators by applying the least mean method, which not only reduces the required memory size compared to the table method implementation, but also achieves a significantly improved model accuracy, and the proposed method was successfully verified for displacement prediction and tracking control of micro-piezoelectric actuators.

An RFID multi-Tag anti-collision algorithm based on Successive Interference Cancellation with power adaptive regulation

Radio Frequency Identification (RFID) is a key technology supporting the Internet of Things (IoT). The simultaneous identification of multiple Tag signals by a Reader will generate the Tag collision problem, which will significantly reduce the identification efficiency of the system and prolong the identification time in the scenario of large-scale Tags. In order to solve this problem, this paper researches RFID Tag collision prevention algorithm, and establishes a multi-Tag collision prevention algorithm model by analyzing the Aloha Tag collision prevention algorithm, which can accurately portray the slot efficiency and time efficiency of the system when recognizing multiple Tags; then establishes a power adaptive regulation RFID multi-Tag identification algorithm using Successive Interference Cancellation (SIC) technology. After simulation and verification results show that the constrained Reader can resolve the signal to noise ratio threshold of 6.9db, the slot efficiency and time efficiency of the SIC-based RFID Tag anti-collision algorithm is improved by 179% and 154% compared to the ISOC algorithm; the designed RFID power adaptive regulation algorithm is improved by 259% and 203% compared to the ISOC algorithm in terms of slot efficiency and time efficiency 203%.

Parameter Estimation for Hindmarsh–Rose Neurons

In the paper, a new adaptive model of a neuron based on the Hindmarsh–Rose third-order model of a single neuron is proposed. The learning algorithm for adaptive identification of the neuron parameters is proposed and analyzed both theoretically and by computer simulation. The proposed algorithm is based on the Lyapunov functions approach and reduced adaptive observer. It allows one to estimate parameters of the population of the neurons if they are synchronized. The rigorous stability conditions for synchronization and identification are presented.

An adaptive Particle Swarm Optimization algorithm for fire source identification of the utility tunnel fire

In order to overcome serious utility tunnel fires and minimize economic loss, it is essential to establish an effective and practical method for finding the fire source. An adaptive Particle Swarm Optimization (PSO) algorithm was proposed for fire source identification of utility tunnel fires, whose inertia weight and learning factors were adjusted dynamically. In comparison with traditional fire detection methods, the proposed algorithm is immune to the specific environment and complicated fire propagation mechanisms, the fire source location can be identified only based on several temperature values. The proposed algorithm was applied in both numerical and experimental scenarios. The results showed that the adaptive algorithm showed greater global searchability in the early exploration, and stronger local searchability in the later exploration compared with the basic one, the improvement ratio of convergence performance could achieve 50%. Additionally, all of the cases demonstrated that the fire source location can be identified by the proposed algorithm with good accuracy. Critically, the spatial temperature distribution of the whole utility tunnel was obtained based on the fire source location and the attenuation coefficient. The reasonable results indicate that the proposed algorithm can provide a reference for fire protection and intervention in the utility tunnel.

Parameter Identification From Normal and Pathological Middle Ears Using a Tailored Parameter Identification Algorithm.

Current clinical practice is often unable to identify the causes of conductive hearing loss in the middle ear with sufficient certainty without exploratory surgery. Besides the large uncertainties due to interindividual variances, only partially understood cause-effect principles are a major reason for the hesitant use of objective methods such as wideband tympanometry in diagnosis, despite their high sensitivity to pathological changes. For a better understanding of objective metrics of the middle ear, this study presents a model that can be used to reproduce characteristic changes in metrics of the middle ear by altering local physical model parameters linked to the anatomical causes of a pathology. A finite-element model is, therefore, fitted with an adaptive parameter identification algorithm to results of a temporal bone study with stepwise and systematically prepared pathologies. The fitted model is able to reproduce well the measured quantities reflectance, impedance, umbo and stapes transfer function for normal ears and ears with otosclerosis, malleus fixation, and disarticulation. In addition to a good representation of the characteristic influences of the pathologies in the measured quantities, a clear assignment of identified model parameters and pathologies consistent with previous studies is achieved. The identification results highlight the importance of the local stiffness and damping values in the middle ear for correct mapping of pathological characteristics and address the challenges of limited measurement data and wide parameter ranges from the literature. The great sensitivity of the model with respect to pathologies indicates a high potential for application in model-based diagnosis.

Crack Size Identification for Bearings Using an Adaptive Digital Twin.

In this research, the aim is to investigate an adaptive digital twin algorithm for fault diagnosis and crack size identification in bearings. The main contribution of this research is to design an adaptive digital twin (ADT). The design of the ADT technique is based on two principles: normal signal modeling and estimation of signals. A combination of mathematical and data-driven techniques will be used to model the normal vibration signal. Therefore, in the first step, the normal vibration signal is modeled to increase the reliability of the modeling algorithm in the ADT. Then, to help challenge the complexity and uncertainty, the data-driven method will solve the problems of the mathematically based algorithm. Thus, first, Gaussian process regression is selected, and then, in two steps, we improve its resistance and accuracy by a Laguerre filter and fuzzy logic algorithm. After modeling the vibration signal, the second step is to design the data estimation for ADT. These signals are estimated by an adaptive observer. Therefore, a proportional-integral observer is then combined with the proposed technique for signal modeling. Then, in two stages, its robustness and reliability are strengthened using the Lyapunov-based algorithm and adaptive technique, respectively. After designing the ADT, the residual signals that are the difference between original and estimated signals are obtained. After that, the residual signals are resampled, and the root means square (RMS) signals are extracted from the residual signals. A support vector machine (SVM) is recommended for fault classification and crack size identification. The strength of the proposed technique is tested using the Case Western Reserve University Bearing Dataset (CWRUBD) under diverse torque loads, various motor speeds, and different crack sizes. In terms of fault diagnosis, the average detection accuracy in the proposed scheme is 95.75%. In terms of crack size identification for the roller, inner, and outer faults, the proposed scheme has average detection accuracies of 97.33%, 98.33%, and 98.33%, respectively.

Adaptive bradycardia assessment in preterm infants

In preterm infants, bradycardias associate to critical health conditions. Standard algorithm for bradycardia identification assumes baseline heart rate (BHR) equal to 150bpm and identifies bradycardias when heart rate (HR) decreases below 100bpm. Since preterm infants show BHR varying from 120bpm to 160bpm, a new adaptive algorithm for real-time bradycardia identification was presented. The adaptive algorithm continuously adjusts BHR by averaging HR over the preceding 10-minute window after eliminating out-of-range HR values, and identifies bradycardias when HR decreases below 67% of BHR. Both standard and adaptive algorithms were evaluated using long-term (20.3–70.3h) electrocardiographic recordings of ten preterm infants ("Preterm Infant Cardio-respiratory Signals" database by Physionet). Bradycardias were characterized in terms of rate (BR, h−1) and depth (BD, bpm). Being also indexes of infants’ health conditions, gestational age at birth (GA, days), birth weight (BW, kg) and HR were used to evaluate performances of the algorithms. Association between BR and BD vs GA, BW and HR was evaluated by computation of the correlation coefficient (ρ). Overall, standard and adaptive algorithms identified 516 and 546 bradycardias, respectively; median BR and BD values were comparable (1.25h−1 and 76bpm vs 1.26h−1 and 70bpm, respectively). However, the adaptive algorithm provided higher BD for HR>150bpm, and vice versa. Significant (p value<0.05) correlations were found between BR and HR (ρ=0.69), BR and BW (ρ=−0.76), and BR and HR (ρ=0.76) only when using the adaptive algorithm. Thus, the adaptive algorithm is superior to the standard algorithm and represents a potentially clinically useful tool for real-time bradycardia assessment in preterm infants.

A robust subband adaptive filter algorithm for sparse and block-sparse systems identification

This paper presents a new subband adaptive filter (SAF) algorithm for system identification scenario under impulsive interference, named generalized continuous mixed p-norm SAF (GCMPN-SAF) algorithm. The proposed algorithm uses a GCMPN cost function to combat the impulsive interference. To further accelerate the convergence rate in the sparse and the block-sparse system identification processes, the proportionate versions of the proposed algorithm, the L0-norm GCMPN-SAF(L0-GCMPN-SAF) and the block-sparse GCMPN-SAF (BS-GCMPN-SAF) algorithms are also developed. Moreover, the convergence analysis of the proposed algorithm is provided. Simulation results show that the proposed algorithms have a better performance than some other state-of-the-art algorithms in the literature with respect to the convergence rate and the tracking capability.

Markovian Adaptive Filtering Algorithm for Block-Sparse System Identification

In this brief, a novel adaptive filtering algorithm for block-sparse system identification called, Block-Sparse Adaptive Bayesian Algorithm (BS-ABA) is proposed. We use a Gaussian Markov (GM) model to generate the unknown block-sparse system. In the proposed algorithm, a maximum a posteriori (MAP) estimation procedure is used to estimate the adaptive filter coefficients which characterized by a Gaussian Mixture Markov (GMM) model. Moreover, the convergence in the mean of the proposed algorithm is provided in this brief. Simulation results show that the computational complexity of the proposed algorithm is less than some recent algorithms, while the performance is comparable to them.

Stable adaptive identification of fully‐coupled second‐order 6 degree‐of‐freedom nonlinear plant models for underwater vehicles: Theory and experimental evaluation

AbstractThis article reports the development, stability analysis, and experimental evaluation of a novel adaptive identification (AID) algorithm for underwater vehicles (UVs) for on‐line estimation of plant parameters (hydrodynamic mass, quadratic drag, righting moment, and buoyancy parameters) that enter linearly into 6 degree‐of‐freedom (6‐DOF) second‐order rigid‐body UV plant dynamic models. The reported UV AID method does not require instrumentation of vehicle acceleration as is required of other standard plant parameter identification methods such as conventional least squares. All but one previously reported adaptive methods for second‐order nonlinear plants have addressed the problem of model‐based adaptive tracking control—approaches in which adaptive plant model identification is performed simultaneously with model‐based trajectory‐tracking control of fully‐actuated second‐order plants; however, these approaches are not applicable when the plant is either uncontrolled, under open‐loop control, underactuated, or using any control law other than an algorithm‐specific adaptive tracking controller. The UV AID algorithm reported herein does not require simultaneous reference trajectory‐tracking control, nor does it require instrumentation of linear acceleration or angular acceleration; thus this novel approach complements previously reported adaptive tracking methods and is applicable to a broader class of UV applications for which fully‐actuated tracking control is impractical or infeasible. We report a experimental performance analysis of the UV AID algorithm in comparison to conventional least‐square identification methods, including comparison in cross‐validation where the performance of the experimentally identified plant models obtained in identification trials are compared to experimental trials differing from the identification trials.

Adaptive Multipopulation Evolutionary Algorithm for Contamination Source Identification in Water Distribution Systems

AbstractReal-time monitoring of drinking water in a water distribution system (WDS) can effectively warn of and reduce safety risks. One of the challenges is to identify the contamination source th...

An improved online system identification method for tiltrotor aircraft

An online system identification method for tiltrotor aircraft flight dynamics modeling is developed in this paper. A general parametric flight dynamics model which can be used for online identification purpose in the three flight modes of tiltrotor aircraft (i.e., helicopter mode, conversion mode, and fixed-wing mode) is developed, and an unideal noise model is also introduced in order to minimize the parameter identification error caused by measurement noise. An adaptive model structure identification algorithm is established by introducing local accuracy criteria, so that a time variant optimal model structure is obtained in real time. A weighted recursive least squares algorithm is established for parameter identification, and a time variant weighting matrix is designed to provide best numerical performance in the identification procedure at any time. A comprehensive nonlinear flight dynamics model of a notional tiltrotor aircraft is used to generate simulation flight test data in all flight modes, and the identification results of the notional tiltrotor aircraft in fixed wing mode, helicopter model as well as conversion mode are obtained and verified. Finally, the flight dynamics model of an unmanned tiltrotor aircraft in helicopter mode is identified by using the real flight test data. The identification results show that the online system identification method for tiltrotor aircraft developed in this paper is effective, efficient and robust.

An Improved Forest Fire Monitoring Algorithm With Three-Dimensional Otsu

Forest fires can destroy millions of acres of land at shockingly fast speeds. The forest fire points identification algorithm is the most critical step in the forest fire monitoring process. Most traditional forest fire monitoring methods use fixed thresholds, ignoring background pixels, and have low recognition rates, which could lead to many problems, such as false reporting and low recognition rate. This paper proposes and tests an adaptive forest fire points identification algorithm using Himawari-8 data. By calculating the three-dimensional histogram of brightness temperature, an adaptive threshold that can automatically identify potential forest fire points is obtained. Based on this three-dimensional Otsu method, the contextual test algorithm has also been adopted to specify forest fire points. The experimental results show that the omission rate of the improved algorithm is about 10% lower than that of the previous algorithm in small-scale fire incidents. The improved algorithm can quickly and effectively extract fire point information, and it is also sensitive to small and low-temperature fires, which provides an efficient means for monitoring fire disasters.

Application of Nonlinear Output Frequency Response Functions and Deep Learning to RV Reducer Fault Diagnosis

In this work, a new method based on the nonlinear output frequency response functions (NOFRFs) and deep convolution neural network (CNN) is proposed for RV reducer fault diagnosis. In order to solve the problem of low accuracy caused by output signal that cannot describe the nonlinear characteristics of system, the nonlinear spectrum based on output frequency response function is adopted. To improve the performance of error cost function model in back propagation (BP) process of CNN, a new comprehensive error cost function model is designed to guide the network parameters’ optimization in direction of the feature classification. Besides, an adaptive network structure optimization algorithm is proposed to avoid the blindness of network structure selection by experience. Specifically, 4-order NOFRF spectrums of system are first obtained by adaptive identification algorithm. Next, NOFRF spectrums are transformed into 2-D images, and CNN extracts the fault features hidden in spectrum images and performs fault classification. The experiment results indicate that the proposed method is effective for RV reducer fault diagnosis with the accuracy of 99.69%.

Adaptive Parameter Identification for Nonlinear Sandwich Systems with Hysteresis Nonlinearity Based Guaranteed Performance

The paper presents an adaptive identification algorithm via data filtering and improved prescribed performance function for Sandwich systems with hysteresis nonlinearity. By developing a filter in which the filter is simple and easy to realize online and several variables, the estimation error vector can be derived. To improve the transient performance of estimator, a modified prescribed performance function is proposed to constrain the estimation error data through the usage of the predefined domain. For the constrained estimation error condition, the error transformation technique is utilized to simplify the design of the estimator thanks to that the restricted condition is transformed into unconstrained condition. To achieve the convergence of the parameter estimation and assure the predetermined property, a fresh adaptive law is developed. Moreover, the theoretical analysis indicates that the error can converge to a small region based on martingale difference theorem. According to the numerical verification and experimental results, the advantage and practicability of the invented estimator are inspected by comparing the estimators with unconstrained condition.

Maximum likelihood‐based adaptive differential evolution identification algorithm for multivariable systems in the state‐space form

SummaryParameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state‐space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood‐based differential evolution (ML‐DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML‐DE algorithm and propose a maximum likelihood‐based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.

Improved Depth Estimation for Occlusion Scenes Using a Light-Field Camera

With the development of light-field imaging technology, depth estimation using light-field cameras has become a hot topic in recent years. Even through many algorithms have achieved good performance for depth estimation using light-field cameras, removing the influence of occlusion, especially multi-occlusion, is still a challenging task. The photo-consistency assumption does not hold in the presence of occlusions, which makes most depth estimation of light-field imaging unreliable. In this article, a novel method to handle complex occlusion in depth estimation of light-field imaging is proposed. The method can effectively identify occluded pixels using a refocusing algorithm, accurately select unoccluded views using the adaptive unoccluded-view identification algorithm, and then improve the depth estimation by computing the cost volumes in the unoccluded views. Experimental results demonstrate the advantages of our proposed algorithm compared with conventional state-of-the art algorithms on both synthetic and real light-field data sets.

Discrete wavelet transform-based RI adaptive algorithm for system identification

In this paper, we propose a new adaptive filtering algorithm for system identification. The algorithm is based on the recursive inverse (RI) adaptive algorithm which suffers from low convergence rates in some applications; i.e., the eigenvalue spread of the autocorrelation matrix is relatively high. The proposed algorithm applies discrete-wavelet transform (DWT) to the input signal which, in turn, helps to overcome the low convergence rate of the RI algorithm with relatively small step-size(s). Different scenarios has been investigated in different noise environments in system identification setting. Experiments demonstrate the advantages of the proposed DWT recursive inverse (DWT-RI) filter in terms of convergence rate and mean-square-error (MSE) compared to the RI, discrete cosine transform LMS (DCTLMS), discrete-wavelet transform LMS (DWT-LMS) and recursive-least-squares (RLS) algorithms under same conditions.

Parameter Identification and Control Algorithm of Electrohydraulic Servo System for Robotic Excavator Based on Improved Hammerstein Model

In view of the nonlinearity and time-varying characteristics of the electrohydraulic servo system of the robotic excavator, a nonlinear adaptive identification and control algorithm based on improved Hammerstein model is proposed. The Hammerstein algorithm model can approximate the nonlinear system with enough precision, but for the time-varying systems is not satisfactory. In order to compensate for the influence of time-varying factors, the fuzzy control module is designed to adaptively update the forgetting factor. The experimental results show that the improved Hammerstein model error is about 40.11% less than the classical Hammerstein model error. This proves that the improved Hammerstein model is feasible and effective to describe the electrohydraulic servo system of the robotic excavator.