Parameter Identification Algorithm Research Articles

Dynamic Characterization of SynRM With Dual-Axis Hybrid Excitation Self-Commissioning

The synchronous reluctance machine (SynRM) has gained increasing attraction for future electric drive systems. The characterization of its magnetic and current dynamics is essential for high-performance model-driven control, which still remains a promising problem. In this paper, a dynamic characterization scheme for SynRM with dual axis hybrid excitation self-comissioning is proposed to characterize the magnetic and current dynamics. Inspired by separating dual-axis current variables, a compact magnetic circuit model is investigated with reciprocity analytics. Furthermore, an improved current dynamic model is derived with resulting analytical apparent and incremental inductances. On this basis, the characterized parameters involved in the magnetic circuit and current dynamic models can be accurately identified by combining the designed dual axis hybrid excitation method with effective parameter identification algorithm. The effectiveness and practicability of the proposed scheme are verified based on the development platform for commercial SynRM drive. This simple dynamic characterization scheme fits for online implementation on standard drives. Compared with the existed work, 38.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> improvement of normalized modeling accuracy for magnetic circuit can be achieved. In the meanwhile, the normalized modeling errors of apparent inductances for current dynamics are strictly less than 6.91 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

Revolutionizing motor dysfunction treatment: a novel closed-loop electrical stimulator guided by multiple motor tasks with predictive control

Functional electrical stimulation (FES) has been demonstrated as a viable method for addressing motor dysfunction in individuals affected by stroke, spinal cord injury, and other etiologies. By eliciting muscle contractions to facilitate joint movements, FES plays a crucial role in fostering the restoration of motor function compromised nervous system. In response to the challenge of muscle fatigue associated with conventional FES protocols, a novel biofeedback electrical stimulator incorporating multi-motor tasks and predictive control algorithms has been developed to enable adaptive modulation of stimulation parameters. The study initially establishes a Hammerstein model for the stimulated muscle group, representing a time-varying relationship between the stimulation pulse width and the root mean square (RMS) of the surface electromyography (sEMG). An online parameter identification algorithm utilizing recursive least squares is employed to estimate the time-varying parameters of the Hammerstein model. Predictive control is then implemented through feedback corrections based on the comparison between predicted and actual outputs, guided by an optimization objective function. The integration of predictive control and roll optimization enables closed-loop control of muscle stimulation. The motor training tasks of elbow flexion and extension, wrist flexion and extension, and five-finger grasping were selected for experimental validation. The results indicate that the model parameters were accurately identified, with a RMS error of 3.83% between actual and predicted values. Furthermore, the predictive control algorithm, based on the motor tasks, effectively adjusted the stimulus parameters to ensure that the stimulated muscle groups can achieve the desired sEMG characteristic trajectory. The biofeedback electrical stimulator that was developed has the potential to assist patients experiencing motor dysfunction in achieving the appropriate joint movements. This research provides a foundation for a novel intelligent electrical stimulation model.

Linear Parameter Varying Observer-Based Adaptive Dynamic Surface Sliding Mode Control for PMSM

This paper presents an adaptive dynamic surface sliding mode control technique to address the issue of system parameter changes in permanent magnet synchronous motor (PMSM) position servo systems. The proposed method involves adopting a linear parameter varying (LPV) observer-based parameter identification algorithm and adaptive control technique. Initially, a mathematical model of the PMSM is established, and the system parameters are divided into nominal and perturbation values. This allows for the reconstruction of the system model into a state space equation that incorporates the unknown perturbation parameters. To accurately estimate these unknown parameters, an LPV observer is designed based on the reconstructed model. Additionally, an adaptive dynamic surface sliding mode control technique is explored to achieve the desired tracking performance. Meanwhile, an exponential reaching law is introduced to expedite the dynamic behavior of the system and mitigate chattering. Finally, a suitable Lyapunov function is selected to ensure the overall stability of the system. The simulation results demonstrate the effectiveness of the parameter identification and control algorithm in achieving good identification and tracking control ability for PMSM systems.

Practical fixed‐time composite‐learning control for full‐state constraint strict‐feedback non‐linear systems: A dynamic regressor extension and mixing based approach

AbstractA practical fixed‐time composite learning control scheme, by combining dynamic regressor extension and mixing (DREM) parameter identification algorithm and adaptive dynamic surface control (DSC) technique, is proposed for a class of strict‐feedback non‐linear systems subjected to linear‐in‐parameters uncertainties and full‐state constraint. To address the problem of state constraint, a non‐linear transformation function is introduced to convert the originally constrained non‐linear system into an unconstrained one. Meanwhile, the hyperbolic tangent function is employed to avoid singularity issues that often appeared in the traditional fixed‐time (FXT) control designs. In order to relax the requirement of persistency of excitation condition, a modified FXT‐DREM parameter identification approach with an interval excitation condition is constructed by introducing a three‐layer transformation technique derived from the classical DREM algorithm. Then, the modified FXT‐DREM parameter identification algorithm is seamlessly integrated into the adaptive DSC framework, resulting in a composite‐learning control scheme. By employing Lyapunov stability analysis, the fixed‐time convergence of both the parameter estimation error and the trajectory tracking error is proved. Finally, the effectiveness of the proposed design is demonstrated through simulation test.

An identification algorithm of switched Box-Jenkins systems in the presence of bounded disturbances: An approach for approximating complex biological wastewater treatment models

This paper focuses on the development of linear Switched Box–Jenkins (SBJ) models for approximating complex dynamical models of biological wastewater treatment processes. We discuss the adaptation of these processes to the SBJ framework, showing the model's generality and flexibility as a class of switched systems that can offer accurate predictions for complex and nonlinear dynamics. This approach of modeling enables real-time data reconciliation from experiments and allows the design of model-based control strategies. Through the extension of the Outer Bounding Ellipsoids (OBEs) algorithm, the paper introduces an online two-stage parameter identification algorithm that effectively handles bounded disturbances for SBJ models. Using the OBE method relaxes the stochastic assumptions on disturbances, which may not be satisfied in practice, particularly for biological and environmental fluctuations. The proposed decomposed OBE algorithm separately identifies the switching patterns and parameters of linear submodels, conducting parameter identification in two distinct phases for input/output and disturbance/output submodels. The efficacy of this approach is shown via simulation results validated against both ADM1 and PBM models, demonstrating the proposed algorithm's capability to accurately predict outputs from different biological wastewater treatment models.

Patient's Healthy-Limb Motion Characteristic-Based Assist-As-Needed Control Strategy for Upper-Limb Rehabilitation Robots.

The implementation of a progressive rehabilitation training model to promote patients' motivation efforts can greatly restore damaged central nervous system function in patients. Patients' active engagement can be effectively stimulated by assist-as-needed (AAN) robot rehabilitation training. However, its application in robotic therapy has been hindered by a simple determination method of robot-assisted torque which focuses on the evaluation of only the affected limb's movement ability. Moreover, the expected effect of assistance depends on the designer and deviates from the patient's expectations, and its applicability to different patients is deficient. In this study, we propose a control method with personalized treatment features based on the idea of estimating and mapping the stiffness of the patient's healthy limb. This control method comprises an interactive control module in the task-oriented space based on the quantitative evaluation of motion needs and an inner-loop position control module for the pneumatic swing cylinder in the joint space. An upper-limb endpoint stiffness estimation model was constructed, and a parameter identification algorithm was designed. The upper limb endpoint stiffness which characterizes the patient's ability to complete training movements was obtained by collecting surface electromyographic (sEMG) signals and human-robot interaction forces during patient movement. Then, the motor needs of the affected limb when completing the same movement were quantified based on the performance of the healthy limb. A stiffness-mapping algorithm was designed to dynamically adjust the rehabilitation training trajectory and auxiliary force of the robot based on the actual movement ability of the affected limb, achieving AAN control. Experimental studies were conducted on a self-developed pneumatic upper limb rehabilitation robot, and the results showed that the proposed AAN control method could effectively estimate the patient's movement needs and achieve progressive rehabilitation training. This rehabilitation training robot that simulates the movement characteristics of the patient's healthy limb drives the affected limb, making the intensity of the rehabilitation training task more in line with the patient's pre-morbid limb-use habits and also beneficial for the consistency of bilateral limb movements.

Two-stage estimator for the complete inertia tensor of uncooperative debris on CubeSat based Active Debris Removal missions

The threat of debris collisions in Low Earth Orbit (LEO) has driven many researchers to develop Active Debris Removal (ADR) missions to manage large debris in LEO. A modern method of capturing debris with a non-rigid tether has been a recent research focus because of difficult post capture dynamics associated with controlling an uncooperative debris through a tether. To facilitate these control schemes, this paper proposes a new two-stage inertia estimator that estimates all principal and products of inertia for an uncooperative space debris using LiDAR and tether force measurements made on-board a CubeSat sized chaser satellite. The method proposed in this work estimates the debris center of mass with a novel pseudo measurement Kalman Filter so that the full inertia tensor can be subsequently estimated using an additional iterative parameter identification algorithm. In addition, the paper achieves online inertia estimation without assuming the tether connection point on the debris by approximating its location using tether tension measurements. The proposed control and estimation scheme is shown in this paper to estimate the full debris inertia tensor even with frequent tether slackness. Two simulation scenarios are presented in this paper, one where the connection point of the tether on the debris is approximated using noiseless measurements of tether tension, and a second where the location of the tether connection point is approximated using noisy measurements of tether tension. For the simulation conditions used in this research, it is shown that the proposed two-stage estimator (TSE) is successful for both cases that the debris is inertially asymmetrical and tri-inertial with non-zero products of inertia.

Battery multi-time scale fractional-order modeling method for state of charge estimation adaptive to full parameters updating

The fractional-order theory has been successfully applied to battery modeling and state of charge (SOC) estimation thanks to the rapid development of smart energy storage and electric vehicles. The fractional-order model (FOM) has high nonlinearity, which makes it difficult to identify the parameters of the FOM, especially the online identification of the order. Aiming at the problem of parameter identification and SOC estimation of the FOM of battery, a multi-time scale fractional-order modeling method is proposed in this paper. Then, a multi-time scale parameter identification strategy based on feature separation is proposed, and two sub-filters are used to complete the online identification of parameters. Finally, a fractional-order multi-innovation unscented Kalman filtering (FO-MI-UKF) algorithm is proposed for SOC estimation to utilize the value of historical information better. Under dynamic stress test (DST) and Beijing bus dynamic stress test conditions (BBDST), compared with the single-time scale parameter identification algorithm and the single-innovation SOC estimation algorithm, the root mean square error of the estimation results is reduced by 13.3 % and 8.7 %, respectively. The experimental results verify the effectiveness of the modeling method and provide a new idea for fractional-order modeling.

Highly-efficient filtered hierarchical identification algorithms for multiple-input multiple-output systems with colored noises

Multiple-input multiple-output (MIMO) systems have extensive applications in industrial processes and systems engineering. This letter applies the filtering identification idea to establish a filtered identification model and investigate a filtered auxiliary model-based recursive least squares (F-AM-RLS) algorithm for parameter identification of MIMO systems with colored noises. To improve the computational efficiency, this work further proposes a four-stage filtered auxiliary model-based recursive least squares (4S-F-AM-RLS) algorithm by means of the hierarchical identification principle. Then, by incorporating the forgetting factor, a four-stage filtered auxiliary model-based forgetting factor recursive least squares (4S-F-AM-FF-RLS) algorithm is given to improve the convergence speed and the estimation accuracy. Additionally, the computational complexity analysis of the proposed algorithms indicates that the 4S-F-AM-RLS algorithm effectively reduces the computational burden and improves computational efficiency. Finally, the effectiveness of the F-AM-RLS, 4S-F-AM-RLS and 4S-F-AM-FF-RLS algorithms is validated through a numerical example.

Modeling, identification, and high-speed compensation study of dynamic hysteresis nonlinearity for piezoelectric actuator

Hysteresis nonlinearity widely exists in the piezoelectric actuator (PEA), and the hysteresis nonlinearity has strong dynamic characteristics that lead to deterioration of tracking performance. To decrease the positioning error caused by hysteresis nonlinearity, a generalized Bouc-Wen (GBW) hysteresis model and its compensation method are proposed in this paper. First, based on the Bouc-Wen hysteresis model, two asymmetric terms and a second-order IIR filter are applied to describe the asymmetric hysteresis and high-frequency phase lag characteristics of PEA. Then, the model parameters with strong relevance to frequency variation are modified as frequency-dependent parameters. Meanwhile, based on the particle swarm optimization (PSO) algorithm, a novel parameter identification algorithm is designed for identifying the parameters of GBW hysteresis model. Then, an inverse feedforward controller is constructed based on the GBW hysteresis model, and a composite compensation control algorithm combining PID controller and repetitive controller is developed to reduce the unmodeled dynamics errors and unknown external disturbances. Finally, the comparison experiment results show that the accuracy and performance of the GBW model proposed in this paper are much better than the classical Bouc-Wen (CBW) model and the enhanced Bouc-Wen (EBW) model, and the developed compensation controller has excellent control performance and robustness.

Joint estimation of state-of-charge and state-of-power for hybrid supercapacitors using fractional-order adaptive unscented Kalman filter

As a new type of energy storage device, hybrid supercapacitors have the advantages of both lithium-ion batteries and supercapacitors. State of charge and state of power estimation are crucial for system operation and energy management. This work proposes a joint estimation method for the state-of-charge (SoC) and state-of-power (SoP) of hybrid supercapacitors based on a fractional-order model and unscented Kalman filter algorithm. Firstly, a parameter identification method for second-order fractional-order models is proposed using a competitive learning-based particle swarm optimization algorithm. On this basis, a SoC estimation method is designed based on the fractional-order adaptive unscented Kalman filter. Then, a SoP estimation method considering multiple constraint conditions is proposed. Finally, the proposed parameter identification and state estimation algorithms are validated under different operating dynamic conditions and environmental temperatures. The experimental results show that the error of model voltage is lower than 100 mV and the SoC estimation error is lower than 2% in the vast majority of cases, which proves the proposed algorithms have good accuracy and robustness in different environments.

Parameter identification algorithm for ship manoeuvrability and wave peak model based multi-innovation stochastic gradient algorithm use data filtering technique

This paper addresses the issue of identifying ship motion parameters and wave peak frequency. Utilising the Euler discretisation principle, we establish a discrete-time auto-regressive moving average model with exogenous input (ARMAX) for the ship-wave system. Furthermore, we develop a filtering-based stochastic gradient algorithm for the system by applying filtering techniques and auxiliary model identification idea. A filtering-based multi-innovation stochastic gradient algorithm, utilizing the multi-innovation identification theory, was developed to enhance the convergence rate and accuracy of parameter identification. This approach was found to be more effective than the filtering-based stochastic gradient algorithm. Simulation results validate the efficacy of the proposed algorithm in parameter identification.

Algorithm for identifying parameters of production function models using a neural network

The purpose of the work is to ensure the monitoring and reliability of statistical data used for the analysis and forecasting of the production development on the basis of generalized indicators that take into account the volume of resources used and the ways of their use. An algorithm for parametric identification of production functions models from time series of statistical data using generalized regression, which provides the best estimation of the parametric optimization error by the method of least squares, is proposed. In contrast to existing assessment methods, neural networks are used to build models, which significantly expand the technical capabilities of modeling and contribute to improving the accuracy of calculations through the use of neural network technologies. It is shown that in order to solve the problems of this class, it is advisable to use generalized regression neural networks with simple training modes and high modeling accuracy. As a result, it is possible to propose an algorithm for quantitative assessment of the production functions parameters, which consists in the construction of a neural model with its subsequent use to fit the trajectory of the production model of a given structure to the obtained data by means of recurrent estimates of the vector of the desired coefficients at a given initial approximation. The proposed algorithm is demonstrated by estimating the parameters of the Cobb-Douglas production function and the discrete-dynamic model of the consumption function according to the corresponding statistical series. The calculations are performed using the functions of the Neural Networks package of the MATLAB software environment. The algorithm is applicable for quantitative estimation of the production models parameters with complex logical-probabilistic relationships, as well as for obtaining numerical values of target indicators and indicators for assessing the inland water transport development by statistical series and monitoring.

Identification Algorithm of Extrinsic Parameters Between 3-D Solid-State LiDAR and Mono-Camera

Identification Algorithm of Extrinsic Parameters Between 3-D Solid-State LiDAR and Mono-Camera

Double-layer prescribed performance control of free-floating space manipulators after capturing unknown targets

This paper studies the parameter identification and prescribed performance control of the free-floating space manipulator after capturing unknown targets. A novel finite-time concurrent learning (FT-CL) parameter identification algorithm utilizing suitable historical data is proposed. This algorithm guarantees the finite-time convergence of the parameter estimation error without using the persistent excitation condition. Then, we apply the dual modeling approach and view the end-effector states as generalized coordinates of novel virtual dual dynamics. This technique bypasses solving the inverse kinematics and calculating the Jacobi matrix, laying the foundation for Jacobi-free pose control of the end-effector. Besides, stabilizers of prescribed performance reference acceleration are carefully designed in a double-layer form for regulating end-effector pose and base attitude. Finally, the Jacobi-free controllers satisfying the double-layer performance constraints are developed. The efficacy of the proposed parameter identification scheme and control strategy is validated through numerical simulations.

Identification of Vivo Material Parameters of Arterial Wall Based on Improved Niching Genetic Algorithm and Neural Networks

Cardiovascular diseases are seriously threatening human health and the incidence rate is high. Many scholars are devoted to studying arterial mechanical properties and material parameters. In this study, the bovine artery was selected as the experimental object and the uniaxial tensile test was carried out by cutting the specimens along its axial, circumferential and [Formula: see text] directions. The finite element software ABAQUS and hyperelastic Holzapfel Gasser Ogden (HGO) constitutive model were used to simulate the experimental process. Niche technology is introduced on the basis of genetic algorithm, and the program of Improved Niche Genetic Algorithm for material parameter identification is compiled based on Python language. In addition, BP Neural Network was constructed based on Tensorflow mathematical system. The material parameters of the constitutive model of bovine artery in different directions were identified by finite element method and experimental data. The results show that Improved Niche Genetic Algorithm and Neural Network, respectively, combined with finite element are both effective and accurate methods for predicting the parameters of arterial vascular hyperelastic materials, which can provide reference and help for the study of arterial vascular mechanical properties.

Research on transmission line dance monitoring and early warning system by fusing multi inertial sensors

Abstract This paper first analyzes the mechanism of transmission line dancing and constructs the mathematical model of transmission line dancing and the parameters of transmission line dancing. Then, a transmission line dancing monitoring and warning system is designed by integrating multiple inertial sensors, and the tower monitoring main splitter and wireless inertial monitoring and warning unit are designed, respectively. Then, the transmission line dancing trajectory was denoised using the wavelet threshold method, and the two-way inequality was determined by the attitude decomposition algorithm so as to design the transmission line dancing trajectory parameter identification algorithm. Finally, the designed system is tested experimentally, and the monitoring performance of the dance monitoring trajectory system is analyzed using collected data. The results show that the angular error of the sensor’s pitch and roll attitudes is within 0.5°, the angular error of the heading angle is within 1°, and the acceleration of the smoothed signal is in the range of -0.2/g~0.2/g. The relative error of amplitude recognition is up to 2.6 cm, and the frequency recognition basically agrees with the actual movement frequency, which is 0.21 Hz, and the error of the recognized frequency is within 0.03 Hz. Hz.

A novel recursive multivariate nonlinear time-series modeling method by using the coupling identification concept

This paper focuses on the modeling problem of complex time-series based on a type of multivariate nonlinear model with colored noise, i.e., the M-RBF-ARMA model. For the purpose of achieving highly accuracy modeling performance under colored noise interference, a novel recursive parameter identification algorithm for the M-RBF-ARMA model is investigated. In the framework of constructing several univariate system and noise sub-identification models using the mixed parameter characteristic of the model, three recursive sub-algorithms are presented by applying the coupling identification concept. Based on the singular value decomposition, a recursive update approach with numerical stability for covariance matrices is given. Then a three-stage coupled average extended recursive algorithm is proposed for the M-RBF-ARMA model by using the interactive strategy, which can implement the separable and interactive identification of different type of parameters. The effectiveness of the proposed algorithm is verified by an illustrative and a real multivariate nonlinear time-series.

Development of Digital Twins to Support the Functioning of Cyber-physical Systems

The peculiarities of developing a digital twin for cyberphysical systems using an analytical model of the investigated process are considered. The proposed approach takes into account the conceptual uncertainty of the analytical model parameters with the subsequent passive identification by adapting the analytical model to the dynamic characteristics of the real physical process. In the article, the digital twin development is carried out on the analytical model air heating process example with the help of an electric calorifier. The analytical model uncertain parameters of the electric heater are analyzed and an integral quality index is proposed to evaluate the dynamic model adequacy in the process of air heating. For electric heater model adaptation, the passive identification algorithm of uncertain parameters is developed, in which deviations of the mathematical model uncertain coefficients are minimized. A numerical study of the considered approach has been carried out. It is shown that uncertain parameters’ passive identification of the model belongs to the problem of single-extremal optimization. The considered modeling examples confirmed the effectiveness of the proposed approach to digital twins’ development for cyber-physical systems.

Experimental Investigations of Damage Identification for Aluminum Foam Sandwich Beams Using Two-Step Method

In the experiment, strain gauges and dynamic signal acquisition instruments are used to collect and record data, and the stochastic subspace algorithm is used to extract the first three strain modal parameters of each case. The damage amount identified by the second natural frequency based on the modified Timoshenko beam theory is more in line with the actual situation. The damage depth of case 2 and case 4 is 2 mm, and the identified damage amount is 10% and 9%, respectively. The damage depth of case 3 and case 5 is 4 mm, and the identified damage amount is 16% and 23%, respectively. The damage location information of case 6 is well identified by using the normalized strain modal shape difference index and the enhanced strain modal shape difference index. Taking the strain response signal of case 6 as an example, it is proved that the stochastic subspace strain modal parameter identification algorithm has strong anti-interference ability under the action of 1.5 times, 4 times, and 9 times noise. In addition, the method is verified by theoretical calculation and numerical simulation, and the damage law has a high degree of coincidence with the test. The experimental results show that this method expands the theoretical basis of foam metal damage degree information identification and improves the accuracy of damage location information identification and the anti-interference of parameter identification.