Adaptive Identification Algorithm Research Articles

Robust and adaptive star identification algorithm based on linear assignment for multiple large field of view visual imaging systems

The integration of the visual imaging system and the self-attitude determination system in on-orbit space projects necessitates robust star identification algorithms. However, disturbances in the on-orbit environment pose a challenge to the accuracy and efficiency of star identification algorithms. This paper introduces a novel star identification algorithm, to the best of our knowledge, designed for multiple large field of view (FOV) visual imaging systems, providing stability in the presence of the noise types, including position noise, lost-star noise, and fake-star noise. We employ the dynamic simulated star images generation method to construct simulated star image libraries suitable for various cameras with different FOV angles. Our algorithm comprises two parts: the star edge matching for coarse matching of interstellar angular distances based on linear assignment, and the star point registration for precise matching of star vectors. This innovative combination of local edge generation and global matching approach achieves an impressive 97.83% identification accuracy, maintaining this performance even with a standard deviation of one pixel in image plane errors and up to five missing stars. A comprehensive approach involving both simulated and empirical experiments was employed to validate the algorithm’s effectiveness. This integration of the visual imaging system and the self-attitude determination system offers potential benefits such as reduced space equipment weight, simplified satellite launch processes, and decreased maintenance costs.

Online adaptive identification of clutch torque transmissibility for the drivability consistency of high-performance production vehicles

Drivability in vehicles with a wet-type dual-clutch transmission (DCT) relies heavily on the precise torque transfer control of the clutch to efficiently distribute the engine torque to the wheels. However, the large variation in the clutch torque transmissibility caused by temperature and wear and the lack of torque sensors for the driveline in production vehicles are the two significant challenges that hinder accurate clutch control in wet DCT vehicles. Therefore, knowledge regarding clutch torque transmissibility is crucial for realizing the demanded clutch torque because it can help determine the required clutch pressure. Thus, in this study, an online adaptive identification algorithm for clutch torque characteristics is developed for high-performance vehicles with wet-type DCTs. The proposed algorithm estimates each clutch torque characteristic using the extended Kalman filter (EKF) based on a parameterizable model for the torque transmissibility curve, making it applicable to production vehicles. Further, the algorithm incorporates an adaptation law based on the Lyapunov stability theorem to compensate for uncertainties in the engine torque, thereby ensuring a robust identification performance regardless of the installed engine type. The estimation performance and effectiveness of the adaptive identification algorithm are experimentally validated using test vehicles with wet DCT.

Servo turntable adaptive step size momentum projection identification algorithm based on ARX model

In this paper, two improved projection algorithms are proposed for a servo turntable system based on the autoregressive exogenous (ARX) model. The improvement pertains to the complexity, convergence speed and estimation accuracy of the algorithm. First, the ARX model structure to be identified is obtained from the open-loop velocity model of the servo turntable. To solve the problems of imprecise step size calculation and slow convergence, the adaptive step size momentum projection (AMP) algorithm is proposed, in which segmented adaptive step sizes are adopted and momentum factors are introduced. On this basis, the latest estimation-based hierarchical adaptive step size momentum projection (LE-AMP) algorithm, which has faster convergence, is proposed as a combination of the hierarchical strategy and the latest estimation approach. Second, the complexity and convergence performance of the proposed algorithms are compared and analyzed. Finally, the effectiveness of the algorithms is verified using two examples. Theoretical analysis and simulation experiments show that the AMP algorithm has excellent parameter estimation accuracy, and its convergence speed is further improved through the addition of the momentum factor and the latest estimation, and these improved strategies do not cause any significant increase in complexity.

Adaptive time-varying state safety constraint control for discrete-time nonlinear systems with random actuator failures

This work investigates the adaptive time-varying state safety constraint control problem for a class of discrete-time nonlinear systems subject to large parameter uncertainties and stochastic actuator failures for the first time. The existence of time-varying full-state constraints, large parameter uncertainties and stochastic actuator failures leads to a series of difficulties for realizing all control objectives. Due to the absence of the linearity property of the derivative in discrete-time log type barrier Lyapunov functions, they are not used to deal with the state constraint problem of nonlinear discrete-time systems. The discrete quadratic-fraction time-varying barrier Lyapunov function is employed to guarantee that all states are constrained in the time-varying predefined regions. By virtue of the constructed multiple-model set, an adaptive identification algorithm is developed to deal with large parameter uncertainties and it has a clear advantage that this method does not requires cn (where n is the dimension of the parameter vector and c is an integer) identification models and only relies on n+1 identification models, thereby heavily reducing the computational burden. By employing the discrete-time filter and backstepping techniques, the adaptive failure compensation controller is designed to ensure the boundedness in probability of the considered systems with respect to stochastic actuator failures. Finally, the effectiveness of the proposed scheme is proved by two simulation examples.

An adaptive algorithm for acoustic feedback compensation and secondary path identification of an active noise control system.

An adaptive variable step-size algorithm is proposed in this paper to address the impact of the real-time acoustic feedback and the real-time secondary path identification on the overall noise reduction performance of an active noise control system. An automated adjustment weight factor is introduced in the algorithm to accelerate the convergence of the acoustic feedback path as well as the secondary path identification, and to prevent possible system divergence. It is shown in this study that the proposed algorithm can resolve the trade-off between a fast convergence and a low misalignment of the virtual and the actual control paths typically found in conventional algorithms. An optimized control structure is also proposed in the study by enabling an adaptive gain adjustment based on the output of the auxiliary filter to enhance the practicality of the control system. The effectiveness of the algorithm is tested using two simulated multi-component signals and a broadband noise signal, and the results confirm that the proposed algorithm can achieve a good noise reduction with only a few iterations.

Improved Backward Smoothing Square Root Cubature Kalman Filtering and Fractional Order—Battery Equivalent Modeling for Adaptive State of Charge Estimation of Lithium‐Ion Batteries in Electric Vehicles

The accuracy of lithium‐ion battery state of charge (SOC) estimation affects the driving distance, battery life, and safety performance of electric vehicles. Herein, the polarization reaction inside the battery is modeled using a second‐order fractional‐order equivalent circuit model and uses an adaptive genetic algorithm for model parameter identification. Then, an improved adaptive fractional‐order backward smoothing square root cubature Kalman filtering algorithm (AFOBS‐SRCKF) is proposed by integrating Sage Husa adaptive filtering and backward smoothing processes to optimize the square root cubature Kalman filter for improving the accuracy and adaptability of real‐time estimation of SOC in a complex environment. Finally, the algorithm is compared with the integer‐order SRCKF, fractional‐order SRCKF through simulation, and fractional‐order backward smoothing SRCKF through simulation. Under complex operating conditions, the error sum of SOC estimation of the AFOBS‐SRCKF algorithm is controlled within 1.0% and the convergence speed is improved by at least 30%. The results show that the AFOBS‐SRCKF algorithm effectively improves the accuracy, stability, and convergence of SOC estimation.

Stable nullspace adaptive parameter identification of 6 degree-of-freedom plant and actuator models for underactuated vehicles: Theory and experimental evaluation

Model-based approaches to navigation, control, and fault detection that utilize precise nonlinear models of vehicle plant dynamics will enable more accurate control and navigation, assured autonomy, and more complex missions for such vehicles. This paper reports novel theoretical and experimental results addressing the problem of parameter estimation of plant and actuator models for underactuated underwater vehicles operating in 6 degrees-of-freedom (DOF) whose dynamics are modeled by finite-dimensional Newton-Euler equations. This paper reports the first theoretical approach and experimental validation to identify simultaneously plant-model parameters (parameters such as mass, added mass, hydrodynamic drag, and buoyancy) and control-actuator parameters (control-surface models and thruster models) in 6-DOF. Most previously reported studies on parameter identification assume that the control-actuator parameters are known a priori. Moreover, this paper reports the first proof of convergence of the parameter estimates to the true set of parameters for this class of vehicles under a persistence of excitation condition. The reported adaptive identification (AID) algorithm does not require instrumentation of 6-DOF vehicle acceleration, which is required by conventional approaches to parameter estimation such as least squares. Additionally, the reported AID algorithm is applicable under any arbitrary open-loop or closed-loop control law. We report simulation and experimental results for identifying the plant-model and control-actuator parameters for an L3 OceanServer Iver3 autonomous underwater vehicle. We believe this general approach to AID could be extended to apply to other classes of machines and other classes of marine, land, aerial, and space vehicles.

Automated measurement and hybrid adaptive identification method for kinematic calibration of hybrid machine tools

Kinematic calibration is an effective method for ensuring the accuracy of hybrid machine tools. Traditional calibration methods are limited by cumbersome operations and poor algorithm robustness. In this study, measurement methods and identification algorithms are studied, and a new calibration method is proposed. First, the configuration of the hybrid machine tool is described, and an error model is constructed. Then, an automated measurement method of tool center point position error for multiple test arbors is proposed using the R-test device. Subsequently, a hybrid adaptive identification algorithm is proposed by sequentially solving the unit inhomogeneity of the identification model, the rank deficiency produced by incomplete measurements, and the multicollinearity between error parameters. Finally, the efficacy of the proposed calibration method is verified through simulations and experiments. The proposed method significantly improves the calibration efficiency and accuracy and proposes a new solution for automated calibration.

State of Charge Estimation of Supercapacitor Under Different Temperatures Using Particle Filter Algorithm Based on Fractional-Order Model

With the rise of new energy vehicles, supercapacitors (SCs) have been used as energy storage components for new energy vehicles due to their high-power density and good low-temperature performance. Accurate modeling and state of charge estimation of SC can ensure the safe operation of new energy vehicles. In order to explore the low-temperature performance of supercapacitors, this paper proposes a dual ZARC fractional-order circuit model to simulate the dynamic characteristics of SC. Using adaptive genetic algorithm for SC parameter identification, the model terminal voltage error is less than 6.5 mV. In addition, the SOC of SC at different temperatures and working conditions is estimated by using the fractional-order particle filter (FOPF) method and compared with the fractional-order extended Kalman filter (FOEKF). The experimental results show that the FOPF method has high estimation accuracy and robustness. Under the temperature of minus 40 °C, the maximum mean absolute error and maximum root-mean-square deviation of SOC estimation under different working conditions are less than 2%, showing good low-temperature performance.

Source identification via contact tracing in the presence of asymptomatic patients

Inferring the source of a diffusion in a large network of agents is a difficult but feasible task, if a few agents act as sensors revealing the time at which they got hit by the diffusion. One of the main limitations of current source identification algorithms is that they assume full knowledge of the contact network, which is rarely the case, especially for epidemics, where the source is called patient zero. Inspired by recent implementations of contact tracing algorithms, we propose a new framework, which we call Source Identification via Contact Tracing Framework (SICTF). In the SICTF, the source identification task starts at the time of the first hospitalization, and initially we have no knowledge about the contact network other than the identity of the first hospitalized agent. We may then explore the network by contact queries, and obtain symptom onset times by test queries in an adaptive way, i.e., both contact and test queries can depend on the outcome of previous queries. We also assume that some of the agents may be asymptomatic, and therefore cannot reveal their symptom onset time. Our goal is to find patient zero with as few contact and test queries as possible. We implement two local search algorithms for the SICTF: the LS algorithm, which has recently been proposed by Waniek et al. in a similar framework, is more data-efficient, but can fail to find the true source if many asymptomatic agents are present, whereas the LS+ algorithm is more robust to asymptomatic agents. By simulations we show that both LS and LS+ outperform previously proposed adaptive and non-adaptive source identification algorithms adapted to the SICTF, even though these baseline algorithms have full access to the contact network. Extending the theory of random exponential trees, we analytically approximate the source identification probability of the LS/ LS+ algorithms, and we show that our analytic results match the simulations. Finally, we benchmark our algorithms on the Data-driven COVID-19 Simulator (DCS) developed by Lorch et al., which is the first time source identification algorithms are tested on such a complex dataset.

An Adaptive Deep Feature Neural Classification Algorithm for Efficient Cardiac Disease Early Risk Identification

Cloud environments can store data for the medical field with a large-scale system. Importantly, early detection of diseases using clinical data analysis is essential in medicine. Cardiovascular disease has emerged as the primary cause of sudden high-risk health-related fatalities in recent years. Analyzing time series data has become more complex and non-linear, making predictive risk analysis through feature analysis a crucial aspect of data analysis. Feature measures that are not feasible can hurt prediction accuracy and may result in misclassification. To overcome this problem, an improved clinical data analysis model using Adaptive Deep Feature Neural Classification (DFNC) method for cardiac data prediction can identify early risk stages. Time series data can be standardized from the CVD-DS dataset initially selected using a preprocessing model. Correlation with a subset of margins can be obtained using the Cardiac Deficiency Prediction Rate (CDPR). Then, the Support Frequent Scaling Feature Selection (SFSFS) model can extract the feature components based on the CDPR weights. The required features can be obtained using a deep neural classifier based on logistic neurons. The classifier is based on a Recurrent Neural Network (RNN) that deliberates each class category's feature values and Cardia Influence Rate (CIR). Classification, precision and recall can be implemented in the proposed method to provide high prediction accuracy. Additionally, early cardiovascular disease risk prediction accuracy may support early diagnosis management.

Set-Membership Adaptive Robot Control With Deterministically Bounded Learning Gains

As a powerful set-membership adaptive identification algorithm, the optimal bounded ellipsoid (OBE) enables fast convergence speeds because it exploits <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> information about system dynamics by estimating sets of feasible solutions rather than single-point solutions. However, its learning gain matrix suffers from vanishing or unbounded growth, which seriously limits its practical performance. In this paper, a novel OBE algorithm is proposed to ensure that the learning gain matrix is constrained by upper and lower bounds, which are unaffected by the hardly predictable excitation levels and can be determined before implementing the algorithm. Thus, the system robustness and tracking capability for time-varying dynamics can be improved. In light of the proposed OBE identification algorithm, an adaptive robot control strategy is further proposed, where the robot dynamics are reconstructed through neural networks. The practical partial asymptotic stability of the closed-loop system is demonstrated using the Lyapunov method. Furthermore, noisy acceleration signals and the inversion of the inertial matrix are eliminated with the proposed control strategy. Experimental results on a robot manipulator validate the effectiveness of the proposed approach.

Retracted: Selective Partial Update Adaptive Filtering Algorithms for Block-Sparse System Identification

Retracted: Selective Partial Update Adaptive Filtering Algorithms for Block-Sparse System Identification

Nonsmooth funnel transformation function-based discrete-time sliding-mode control with deterministic performance margin for servo systems

This paper proposes a nonsmooth funnel transformation function-based discrete-time sliding-mode control strategy for the discrete-time servo systems with unknown frictions and disturbances. For obtaining a more accurate discrete-time system model, a filter-based adaptive identification algorithm (FAIA) is introduced, where the unknown measurement noises are considered. Based on the identified system model and a novel discrete-time nonsmooth funnel transformation function (improving the tracking performances), a discrete-time sliding-mode surface is designed to confine the tracking error to a smaller funnel region compared with the predefined one, which has a deterministic performance margin related to the upper bound of the lumped disturbance. Furthermore, selections of steady-state funnel boundary and sliding-mode surface parameter can be guided theoretically according to the upper bound of the lumped disturbance. Experimental results show that the tracking error is guaranteed in the predefined funnel with a deterministic performance margin by using the proposed control strategy.

A Novel Second-OrderSine-Cost-Function-Derived Kernel Adaptive Algorithm for Non-Linear System Identification

A novel kernel recursive second-order sine adaptive (KRSOSA) algorithm was devised for identifying non-linear systems, which was constructed using a symmetry squared sine function to develop a novel kernel loss function and recursive scheme. In the proposed KRSOSA algorithm, the squared sine function provides resistance to impulsive noise due to the sine operation, which was well-derived and investigated in the framework of kernel adaptive filtering (KAF). The behavior of the proposed KRSOSA algorithm was investigated and analyzed using computer simulations, which provided good performance for identifying non-linear systems under impulsive noises.

An Improved Parameter Identification Algorithm for the Friction Model of Electro-Hydraulic Servo Systems.

Friction is an inherent nonlinear disturbance that can lead to creeping, jitter, and decreased tracking precision in an electro-hydraulic servo system. In this paper, the LuGre friction model is used to describe the dynamic and static characteristics of the friction force of a servo system comprehensively. Accurate identification of model parameters is key to implementing friction compensation. However, traditional genetic identification algorithms have the shortcomings of a premature solution, slow convergence, and poor accuracy. To address these shortcomings, this paper proposes an improved adaptive genetic identification algorithm. The proposed algorithm selects evolutionary processes adaptively according to the population concentration in the initial stage of population evolution. Moreover, it adjusts the crossover probability and the mutation probability to identify a local optimum accurately and converge to the global optimum rapidly. During the late stage of population evolution, the accuracy of the global optimal solution can be improved by reducing the search range of identification parameters. The simulation results show that the relative error of the model parameter values identified by the proposed algorithm is reduced to less than 1% and the convergence speed is faster. Compared with the existing traditional genetic algorithm and adaptive genetic algorithm, the overall performance of the proposed method is better. This study provides a feasible and highly accurate identification method for parameter identification of friction models used in electro-hydraulic servo systems.

Adaptive Anti-noise Least-Squares Algorithm for Parameter Identification of Unmanned Marine Vehicles: Theory, Simulation, and Experiment

Adaptive Anti-noise Least-Squares Algorithm for Parameter Identification of Unmanned Marine Vehicles: Theory, Simulation, and Experiment

Recursive adaptive filtering algorithms for sparse channel identification and acoustic noise reduction

Recursive adaptive filtering algorithms for sparse channel identification and acoustic noise reduction

Adaptive Identification of System with Bouc-Wen Hysteresis Modifications

The adaptive identification method developed to evaluate the parameters of the Bouc-Wen hysteresis (BWH). The adaptive approach based on the use of adaptive observer. We synthesize adaptive identification algorithms using the second Lyapunov method. Requirements for the input of the system which guarantee the identification of parameters considered. We propose BWH modifications (BWHM). Adaptive algorithms for estimating BWHM parameters developed. The boundedness of adaptive system processes shown in coordinate and parametric spaces. We prove the exponential dissipativity of processes in an adaptive system by using the Lyapunov vector function method. Estimating method proposed for signaling uncertainty in the system.

Ekblom promoting adaptive algorithm for system identification

We propose an Ekblom promoting adaptive algorithm (EPAA) which uses Ekblom norm to construct a data reusing scheme to achieve better performance under impulsive noise (IN) environments. By exerting Ekblom-norm constraint on the posterior error vector, a sliding window cost function is created to realize the EPAA that is derived to reduce IN effects. The derivation and theoretical convergence analysis of the EPAA are presented in detail. The simulation examples are setup to illustrate the superior performance of the EPAA compared to popular data reusing algorithms for system identification and echo cancellation under IN environments.