Adaptive Identification Research Articles

Strong Lyapunov functions for two classical problems in adaptive control

Strong Lyapunov functions for two classical problems in adaptive control and parameter identification are presented. These Lyapunov functions incorporate in their structure the classical persistency of excitation conditions, allowing to show global uniform asymptotic stability of the associated adaptive systems under sufficient and necessary conditions.

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.

Robust fault detection and adaptive parameter identification for DC‐DC converters via switched systems

SummaryIn this paper, the problem of fault detection and identification for DC‐DC converters is presented. First, switched systems model and fault model are analyzed based the switched characteristics of the DC‐DC converters, taking the DC‐DC buck converter as an example. According to the switched Lyapunov function technique, a fault detection observer and a bank of linear switched fault identification observers are designed for the switched systems. Next, the fault detection observer detects the fault based on the residual produced by the observer output and actual output. After the fault is detected, fault identification observers are activated. The location of fault is identified by comparing the residual evaluation functions. Meanwhile, the adaptive parameter identification is achieved by choosing an appropriate adaptive law. Finally, in order to show the feasibility of the fault detection and identification, the simulation results are given in this article.

Operable adaptive sparse identification of systems: Application to chemical processes

AbstractOver the past few decades, several data‐driven methods have been developed for identifying a model that accurately describes the process dynamics. Lately, sparse identification of nonlinear dynamics (SINDy) has delivered promising results for various nonlinear processes. However, at any instance of plant‐model mismatch or process upset, retraining the model using SINDy is computationally expensive and cannot guarantee to catch up with rapidly changing dynamics. Hence, we propose operable adaptive sparse identification of systems (OASIS) framework that extends the capabilities of SINDy for accurate, automatic, and adaptive approximation of process models. First, we use SINDy to obtain multiple models from historical data for varying input settings. Next, using these models and their training data, we build a deep neural network that is incorporated in a model predictive control framework for closed‐loop operation. We demonstrate the OASIS methodology on the identification and control of a continuous stirred tank reactor.

A Load Identification Application Technology Based on Regularization Method and Finite Element Modified Model

An improved load identification technology of a beam based on different regularization methods and model modifying methods is presented in an attempt to minimize the estimation error at several periodic loads. A hybrid model is developed to simulate such ill-posed problem interactions under different noise levels. The finite element model is modified with the different optimization algorithms to obtain the equivalent constraint condition. Experimental verification is also carried out to obtain correct modes and frequencies by considered vibration response and different boundary conditions. The measured results demonstrate the good agreement with the identification results. The results are shown that the improved method not only has more adaptive range and higher identification accuracy but also has effective identification ability for loads under different noise levels.

Modified complementary sliding mode control with disturbance compensation for permanent magnet linear synchronous motor servo system

In this study, a modified complementary sliding mode control (MCSMC) method based on a disturbance force observer with mass identification (DFOB-MI) applicable to the permanent magnet linear synchronous motor is proposed to achieve high-performance servo control fields. MCSMC is an improvement on the complementary sliding mode control (CSMC) method, which incorporates an approach angle into the saturation function. MCSMC allows for asymptotic convergence of the position tracking errors and guarantees the global robustness of the system. In addition, compared to hybrid control strategies combining neural networks with CSMC, the MCSMC method has a simpler structure and faster response. However, in practical applications, the mass variation of the mover has a significant impact on system performance. To achieve better dynamic and static characteristics, a disturbance force observer capable of identifying the mass variation based on model reference adaptive identification theory is proposed. DFOB-MI can identify the mass of the mover and provide information on the disturbance caused by the change of the load. Thus, the compensation current is calculated to reduce the disturbance and realise compensation. The more accurate tracking performance and stronger robustness of the proposed control scheme compared to conventional approaches have been confirmed through comparative experimental studies.

Analysis of fixed-time outer synchronization for double-layered neuron-based networks with uncertain parameters and delays

In this paper, the problem of fixed-time outer synchronization over double-layered neuron-based networks with uncertain parameters in nonlinear nodes dynamics and delayed coupling among the nodes is studied. To solve the problem, we propose a delay-dependent controller. Via employing Lyapunov stability theory for the double-layered networks, some sufficient criteria for the fixed-time outer synchronization are provided. To guarantee that the uncertain parameters are identified, an adaptive update identification law is designed. The designed controllers and the sufficient criteria can be applied not only to fixed-time synchronization of double-layered directed networks, but also to fixed-time synchronization of double-layered undirected networks. Our results indicate that the fixed settling time is related to the designed controllers, the size for neuron-based networks, the number of uncertain parameters for each neuron and the dimension of each neuron node in the fixed-time synchronization. Simulation examples illustrating the results are included.

A New Load Adaptive Identification Method Based on an Improved Sliding Mode Observer for PMSM Position Servo System

The effective identification of the load torque is one of the key methods to improve the positioning accuracy of the position servo system, and the sliding mode observer (SMO) is a common identification method in the speed control system because of its advantages of insensitive parameters and easy physical realisation. However, the existing SMO cannot achieve high precision and high response identification of load torque in the full speed range when the motor is running at variable-speed or variable-load, which limits its application in the position servo system. Based on the analysis of the stability and the adaptive law of feedback gain coefficient, an improved SMO for adaptive identification based on adaptive control and improvement of cut-off frequency is proposed. To verify the proposed method, the dedicated simulation model and test platform are built. The results show that the proposed observer can realize the adaptive identification of the load torque under the condition of variable-speed or variable-load, and the estimation accuracy is high. The position servo system designed by the proposed observer can improve the response against load change, which is an effective method for high-precision positioning control of the position servo system with a variable-speed or variable-load.

Biogas production management systems with model predictive control of anaerobic digestion processes

We developed a biogas production management system to control biogas production by determining the feedstock inputs to the anaerobic digestion process according to fluctuations of the renewable energy supply. The developed system consists of three functions: a prediction model for the anaerobic digestion processes, a parameter-estimation system, and a feedstock-determination controller. A prediction model for the anaerobic digestion processes in a state-space representation was constructed for the input–output relationship of biogas generation from organic compounds and the state of methane fermentation. A parameter-estimation system that estimated the parameters included in the prediction model from actual operating process data was built based on adaptive identification theory. The feedstock-determination controller was established based on model predictive control as a method to control biogas production. From the results of the identification experiment, the least square estimator of the parameters converged as the training data increased, and a reliable parameter was given in 1 week. From the results of the numerical simulation and the control experiment, it was confirmed that the biogas production management system developed in this study had a high prediction accuracy and control performance.

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.

Torque identification based on LMS adaptive delay inverse system method

In order to relieve the dependence of load estimation upon the prior knowledge of mechanical system, an adaptive delayed inverse model is proposed for identifying the torque time history based on adaptive delay inverse system identification method. The LMS (least mean square) algorithm was used to identify the inverse model of the rotating system, which instead of system characteristic matrix inversion in common determination methods and ill-posed problem is avoided consequently. The adaptive delayed inverse model was used to identify the time-domain torque by analyzing the angular acceleration response data of the working state. The single-point time-domain torque of the bare shaft was identified by the angular acceleration response in the case of noiseless interference. The simulation results illustrate that the recognition results of this method is satisfied. It is proved by the experiment that the method is also feasible to the shaft system with disk coupling. The proposed method can be applied in practical engineering as it is not necessary to grip the mathematical model and system parameters in advance.

Synchronized Ambient Output-Only Based Online Inter-Area Transfer Capability Assessment Considering Small Signal Stability

Inter-area transfer power is a key factor affecting the damping of electromechanical oscillation. This paper presents an output-only based inter-area transfer capability (ITC) assessment method considering small signal stability constraint. Based on the construction of an equivalent two-machine system (ETmS) of an interconnected power grid, the measurements-based approach for estimating the parameters of the ETmS was studied. Then, we developed an optimization model with a maximum ITC target and damping as constraint. Under the premise of using recursive adaptive stochastic subspace identification (RASSI) to extract the oscillation modes (frequency and damping) from the ambient data, the optimization model was solved by employing the particle swarm optimization (PSO) algorithm. The effectiveness of the proposed approach was demonstrated using numerical simulations on the IEEE 16-machine 68-bus test system and measured data from a real system.

Can Psychological Questionnaires Predict the Academic Talent during Higher Education Studies?

The space between secondary-school gifted education and starting one’s academic career is what is termed higher education talent management in this context. The “talent management”, in the aspect of the Hungarian higher education, refers to academically talented students, who prepare to the scientific career. So, the talent management in higher education can be imagined as a bridge between formal school studies and scientific career. In this research, I investigated empirically, with psychological questionnaires and other indicators what features and personality-traits contribute to academic/scientific success (number of scientific activities). 144 talented students were recruited to this research; all of them participated in a talent program or conception. The 5-page test pack included mainly professional psychological tests. The data was analyzed with linear regression statistical method, where the dependent variable was an index calculated from the students’ scientific activities. The regression model has been significant (F=3,453; df=128; p<0,05), but owing to the weak R-square value (0,226), the results are not suitable to the practical adaptation and talent identification.

Estimation and Identifiability of Parameters for Generalized Lotka-Volterra Biological Systems Using Adaptive Controlled Combination Difference Anti-Synchronization

This manuscript systematically describes a procedure to investigate the combination difference anti-synchronization scheme among three identical chaotic generalized Lotka-Volterra biological systems. Initially, an adaptive parameter identification control method has been proposed which is based on the Lyapunov stability analysis. In addition, the biological adaptive control law for achieving global asymptotic stability of state variables of the considered system with unknown parameters has been derived. Numerical simulations have been thereafter presented for ensuring the effectivity and correctness of the considered technique using MATLAB. Remarkably, the obtained analytical results agree excellently with the computational results. The proposed approach has enormous applications in the area of image encryption and secure communication.

Reweighted l p constraint LMS‐based adaptive sparse channel estimation for cooperative communication system

The issue of sparsity adaptive channel reconstruction in time-varying cooperative communication networks through the amplify-and-forward transmission scheme is studied. A new sparsity adaptive system identification method is proposed, namely reweighted l p norm ( 0 < p < 1 ) penalised least mean square (LMS) algorithm. The main idea of the algorithm is to add a l p norm penalty of sparsity into the cost function of the LMS algorithm. By doing so, the weight factor becomes a balance parameter of the associated l p norm adaptive sparse system identification. Subsequently, the steady state of the coefficient misalignment vector is derived theoretically, with a performance upper bounds provided which serve as a sufficient condition for the LMS channel estimation of the precise reweighted l p norm. With the upper bounds, the authors prove that the l p ( 0 < p < 1 ) norm sparsity inducing cost function is superior to the reweighted l 1 norm. An optimal selection of p for the l p norm problem is studied to recover various d sparse channel vectors. Several experiments verify that the simulation results agree well with the theoretical analysis, and thus demonstrate that the proposed algorithm has a better convergence speed and better steady-state behaviour than other LMS algorithms.

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.

Adaptive Detection Algorithm for Hazardous Clouds Based on Infrared Remote Sensing Spectroscopy and the LASSO Method

Longwave infrared (LWIR) spectroscopy is useful for detecting and identifying hazardous clouds by passive remote sensing technology. Gaseous constituents are usually assumed to be thin plumes in a three-layer model, from which the spectral signatures are linearly superimposed on the brightness temperature spectrum. However, the thin-plume model performs poorly in cases of thick clouds. A modification to this method is made using synthetic references as target spectra, which allow linear models to be used for thick clouds. The prior background, which is generally unknown in most applications, is reconstructed through a regression method using predefined references. However, large residuals caused by fitting errors may distort the extracted spectral signatures and identification results if the predefined references are not consistent with the real spectral shapes. A group of references are generated to represent the possible spectral shapes, and the least absolute shrinkage and selection operator (LASSO) method is used to select the most appropriate reference for spectral fitting. Small residuals and adaptive identification are achieved by automatically selecting the reference spectrum. Two experiments are performed to verify the algorithm proposed in this article. Ethylene is adaptively detected during an indoor release process, and the spectral shape varies with the amount released. In addition, ammonia is measured under different humidity conditions, and the background is adaptively removed using the LASSO method. Based on this research, LWIR remote sensing technology can be applied in various target-detection scenarios, and adaptive identification is achieved to promote hazardous cloud detection.

Adaptive parameter identification based nadir-pointing control of spacecraft with misaligned rotational component

The kinematics and dynamics of a nadir-pointing spacecraft with rotational component are derived in the presence of both the parallel and angular misalignments. Based on the derived model, the nadir-pointing control problem is formulated and an adaptive attitude control law with parameter identification is developed. In the proposed control law, periodic angular reference signal is exerted firstly to tune the adaptive parameters, and the misalignments of the rotational component are identified simultaneously. Using the identified parameters, high precision nadir-pointing control is achieved. The stability and effectiveness of the proposed method are verified through Lyapunov method and Monte Carlo analysis.

Adaptive Real-Time Identification of Motor Unit Discharges From Non-Stationary High-Density Surface Electromyographic Signals.

Estimation of the discharge pattern of motor units by electromyography (EMG) decomposition has been applied for neurophysiologic investigations, clinical diagnosis, and human-machine interfacing. However, most of the methods for EMG decomposition are currently applied offline. Here, we propose an approach for high-density surface EMG decomposition in real-time. A real-time decomposition scheme including two sessions, offline training and online decomposition, is proposed based on the convolutional kernel compensation algorithm. The estimation parameters, separation vectors and the thresholds for spike extraction, are first computed during offline training, and then they are directly applied to estimate motor unit spike trains (MUSTs) during the online decomposition. The estimation parameters are updated with the identification of new discharges to adapt to non-stationary conditions. The decomposition accuracy was validated on simulated EMG signals by convolving synthetic MUSTs with motor unit action potentials (MUAPs). Moreover, the accuracy of the online decomposition was assessed from experimental signals recorded from forearm muscles using a signal-based performance metrics (pulse-to-noise ratio, PNR). The proposed algorithm yielded a high decomposition accuracy and robustness to non-stationary conditions. The accuracy of MUSTs identified from simulated EMG signals was 80% for most conditions. From experimental EMG signals, on average, 12 ± 2 MUSTs were identified from each electrode grid with PNR of 25.0 ± 1.8dB, corresponding to an estimated decomposition accuracy 75%. These results indicate the feasibility of real-time identification of motor unit activities non-invasively during variable force contractions, extending the potential applications of high-density EMG as a neural interface.

Adaptive Fuzzy-Wavelet Neural Network identification core for reinforced control of general arbitrarily switched nonlinear Multi Input-Multi Output Dynamic Systems

In control of switched systems with undetectable switching signals, robustness and precision are on two different sides of a spectrum. In one hand, robustness to significant modeling uncertainties arising from undetectability of switching modes can be attained by designing the control scheme for worst-case switching configurations. On the other hand, such a control scheme would potentially be overly conservative and imprecise. A natural solution to this problem is pinpointing active switched dynamics at any given moment. However, this is no trivial task. In this study, we propose that the aforementioned problems can be overcome by designing a control scheme that prioritizes appropriate objectives according to operating conditions. In other words, the control scheme adjusts itself such that either of robustness to unknowable switching or increased tracking precision is selected as the primary control objective. As a result, the control model can be considered as dual-mode featuring a safe control mode and a precise control mode. In precise control mode, a model generation scheme using a modified Fuzzy-Wavelet Neural Network (FWNN) for Multi Input-Multi Output (MIMO) systems is incorporated for precise estimation of active dynamics which potentially features unknowable switching dynamics, external disturbances and parametric modeling uncertainty. However, this approximate model cannot be used immediately since convergence of the FWNN-based model to actual system dynamics takes place after a limited interval. In such periods (which often correspond to discontinuities in switching dynamics and references), using the FWNN scheme is perilous. Therefore, a robust discrete-time sliding mode control (DSMC) is used to ensure stabilization of closed-loop system in all potential modes of switched dynamics at the cost of reduced tracking precision. In combination, the dual-mode scheme ensures robust stabilization in safe control modes corresponding to transient-state stage and accurate tracking in steady-state stage of system response based on the proposed precise mode scheme. Numerical and experimental examples highlight the key features and improvements of the presented control algorithm.