Parameter Identification Scheme Research Articles

Control Strategy of Flywheel Energy Storage System for Improved Model Reference Adaptive System Based on Tent-Sparrow Search Algorithm

This study addresses speed sensor aging and electrical parameter variations caused by prolonged operation and environmental factors in flywheel energy storage systems (FESSs). A model reference adaptive system (MRAS) flywheel speed observer with parameter identification capabilities is proposed to replace traditional speed sensors. The proposed method uses reference and adjustable models to identify the stator resistance and permanent magnet flux (PM Flux) to mitigate the adverse effects of electrical parameter changes on control performance. The Tent chaotic mapping-improved Sparrow Search Algorithm (SSA) optimizes the Proportional-Integral (PI) controller parameters for the dual closed-loop and MRAS speed adaptation laws of the flywheel motor. Moreover, a self-switching parameter identification (SSPI) scheme, which constructs a cost function based on the current, parameter identification, and speed errors, is proposed to prevent inaccuracies in parameter identification. The MRAS observer selects the appropriate PI adaptive mechanism based on the error values, thereby enhancing identification accuracy. Simulink simulations show significant improvements in the rapidity and accuracy of the Tent-SSA optimized MRAS flywheel speed observer, enhancing the stability and robustness of the flywheel rotor. Experimental validation on a constructed FESS platform confirms the feasibility of this method.

Optimal Control for an Epidemic Model of COVID-19 with Time-Varying Parameters

The coronavirus disease 2019 (COVID-19) pandemic disrupted public health and economies worldwide. In this paper, we investigate an optimal control problem to simultaneously minimize the epidemic size and control costs associated with intervention strategies based on official data. Considering people with undetected infections, we establish a control system of COVID-19 with time-varying parameters. To estimate these parameters, a parameter identification scheme is adopted and a mixed algorithm is constructed. Moreover, we present an optimal control problem with two objectives that involve the newly increased number of infected individuals and the control costs. A numerical scheme is conducted, simulating the epidemic data pertaining to Shanghai during the period of 2022, caused by the Omicron variant. Coefficient combinations of the objectives are obtained, and the optimal control measures for different infection peaks are indicated. The numerical results suggest that the identification variables obtained by using the constructed mixed algorithm to solve the parameter identification problem are feasible. Optimal control measures for different epidemic peaks can serve as references for decision-makers.

Robust equivalent circuit model parameters identification scheme for State of Charge (SOC) estimation based on maximum correntropy criterion

Equivalent circuit model (ECM) parameters identification, which aims to identify the model parameters of ECM accurately is vital for assessing battery status and refining battery management systems (BMS), extensive research has been conducted in this field. However, most of the existing studies rely on global similarity criteria such as Root Mean Square Error (RMSE) and Sum of Squares due to Error (SSE), which are highly sensitive to non-Gaussian noise, so they may not perform effectively when faced with the non-Gaussian noise which is often encountered in battery working environments. This article presents a robust ECM parameter identification scheme termed the Maximum Correntropy Criterion-based Gradient Ascending scheme (MCCGA). The proposed MCCGA scheme adopts correntropy for similarity assessment and leverages a gradient ascent algorithm to optimize the model parameters iteratively. Through these methods, the MCCGA scheme not only identifies model parameters with precision but also remains robustness to non-Gaussian noise. Extensive experimental results based on public dataset are provided, which validate the effectiveness, robustness and convergence of the proposed MCCGA scheme.

Identifying composition-mechanics relations in human brain tissue based on neural-network-enhanced inverse parameter identification

The mechanical properties of human brain tissue remain far from being fully understood. One aspect that has gained more attention recently is their regional dependency, as the brain’s microstructure varies significantly from one region to another. Understanding the correlation between tissue components and the mechanical behavior is an important step toward better understanding how human brain tissue properties change in space and time and to develop highly spatially resolved constitutive models for large-scale brain simulations. Here, we analyze the correlation between human brain tissue components quantified through enzyme-linked immunosorbent assays (ELISA) and material parameters obtained through an inverse parameter identification scheme based on a hyperelastic Ogden model and multimodal mechanical testing data for eight regions of the brain. We use neural networks as a metamodel to save computational costs. The networks are trained on finite element simulation outputs and are able to replace the simulations in the initial optimization step. We identified strong dependencies between mechanical properties and Iba1 associated with microglia cells, collagen VI, GFAP associated with astrocytes, and collagen IV. These results advance our understanding of microstructure-mechanics relations in human brain tissue and will contribute to the development of highly spatially resolved microstructure-informed constitutive models.

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 and high-precision trajectory tracking control for space robotic manipulator

In this paper, an effective control and parameters identification scheme was proposed for high-precision trajectory tracking of space robotic manipulators. Our proposed control method employed a linear-extended-state-observer (LESO) based model predictive control (MPC) scheme. The feedback linearization approach was utilized to develop a model predictive controller. The optimization objective function was simplified to a standard quadratic programming (QP) problem for solution. To minimize the impact of the inaccuracy in the dynamics model, a LESO was designed to estimate the disturbance, and its convergence was demonstrated. In order to improve the accuracy of dynamic model, the physical consistency of inertial parameters was constrained using linear matrix inequality (LMI) techniques. Moreover, the dynamics parameters were estimated through semidefinite programming (SDP) techniques. To validate the effectiveness of the proposed control method, it was compared with existing controllers through simulations and experiments. The results demonstrated the superior performance of the proposed scheme, highlighting its potential for achieving high-precision trajectory tracking control in space robotic manipulators.

Inverse identification of region-specific hyperelastic material parameters for human brain tissue

The identification of material parameters accurately describing the region-dependent mechanical behavior of human brain tissue is crucial for computational models used to assist, e.g., the development of safety equipment like helmets or the planning and execution of brain surgery. While the division of the human brain into different anatomical regions is well established, knowledge about regions with distinct mechanical properties remains limited. Here, we establish an inverse parameter identification scheme using a hyperelastic Ogden model and experimental data from multi-modal testing of tissue from 19 anatomical human brain regions to identify mechanically distinct regions and provide the corresponding material parameters. We assign the 19 anatomical regions to nine governing regions based on similar parameters and microstructures. Statistical analyses confirm differences between the regions and indicate that at least the corpus callosum and the corona radiata should be assigned different material parameters in computational models of the human brain. We provide a total of four parameter sets based on the two initial Poisson’s ratios of 0.45 and 0.49 as well as the pre- and unconditioned experimental responses, respectively. Our results highlight the close interrelation between the Poisson’s ratio and the remaining model parameters. The identified parameters will contribute to more precise computational models enabling spatially resolved predictions of the stress and strain states in human brains under complex mechanical loading conditions.

Modelling and RBF Control of Low-Limb Swinging Dynamics of a Human–Exoskeleton System

With the increase in the elderly population in China and the growing number of individuals who are unable to walk normally, research on lower limb exoskeletons is becoming increasingly important. This study proposes a complete dynamic model parameter identification scheme for the human–machine coupling model of lower limb exoskeletons. Firstly, based on the coupling model, the excitation trajectory is optimized, data collection experiments are conducted, and the dynamic parameter vector of the system is identified using the least squares method. Secondly, this lays the foundation for designing adaptive control based on RBF neural network approximation. Thirdly, the Lyapunov function is used to prove that the RBF neural network adaptive controller can achieve stable tracking of the lower limb exoskeleton. Finally, simulation analysis reveals that increasing the gains of the RBF controllers effectively reduces tracking errors. Furthermore, the tracking errors and control torques show that adaptive control based on the RBF neural network approximation works well.

Maneuverability indices estimation of nonlinear response model based on full-scale trial data and square root unscented Kalman filter

Research on ship maneuvering performance provides a theoretical reference for autonomous ship navigation control. The gain and time coefficients accurately describe the dynamic performance of the ship. Given the challenge that the unscented Kalman filter cannot ensure the nonnegativity of the state covariance matrix during the filtering process, this study proposed a parameter identification scheme for a second-order nonlinear response model based on the square root unscented Kalman filter. First, the maneuverability indices of the free-running model were identified based on simulation data. Thereafter, the feasibility and convergence performance of the proposed scheme were verified. Based on full-scale trial data with uncertainty interference, the parameters of the second-order nonlinear response model were estimated for the YUKUN ship. The feasibility, effectiveness, and generalizability of the proposed scheme were further verified. The proposed scheme was analyzed based on three perspectives: parameter convergence, estimation results of the maneuverability indices, and yaw angle prediction results. The results obtained using the proposed algorithm were compared with those obtained using the extended Kalman filter. Finally, the proposed scheme can provide a reference for online parameter identification and prior knowledge for course keeping controllers and autonomous navigation control.

Parameter identification for the non-associated flow rules representing corner effects through the equivalent tangential shear modulus reduction after abrupt strain-path change

Some specific plastic flow rules have been proposed to describe the experimental observations of “corner effects”—the apparent violation of the normality of plastic flow after abrupt strain-path change, and rotation of the plastic strain rate direction toward the strain rate direction. In this paper, a simple parameter identification scheme for these non-associated flow rules representing corner effects is proposed from the perspective of the equivalent tangential shear modulus reduction. The nonlinear tension–torsion loading experiments with abrupt strain-path changes were performed on tube specimens made of the aluminum alloy A6063-T5, and working conditions of different prestrains and different loading path changes were included. The finite element analysis was used to verify the rationality of the determined parameters and compare the performances of different plastic flow rules in predicting the non-normality of the plastic flow. The experimental and simulation results suggest that the non-associated flow rules can well predict the plastic flow when the determined parameters are used, with the rules proposed by Yoshida and Tsuchimoto (2018) and Zhang et al. (2022) yielding the most accurate predictions. At the same time, the equivalent tangential shear modulus is apparently reduced during the transient process of strain-path change without elastic unloading; however, it does not seem to decrease to near zero as reported before.

A multi-step parameter identification of a physico-chemical lithium-ion battery model with electrochemical impedance data

Physico-chemical battery models are widely used in the design and simulation of lithium-ion batteries due to their physically descriptive modelling approach. The model accuracy highly depends on the accurate identification of model parameters, yet accurate and feasible model parameterisation with state-of-the-art experimental techniques is laborious, expensive and tied to inherent measurement and estimation errors. Multi-step voltage-based data-driven parameter identification techniques are widely adopted in the literature to tackle this challenge. However, impedance-based parameter identification schemes lack a similar level of detailed analysis. Therefore, we propose a novel multi-step electrochemical impedance spectroscopy (EIS) based data-driven parameter identification framework to identify kinetic parameters of a physico-chemical battery model utilising particle swarm optimisation featuring metaheuristic optimisation capabilities. The parameter optimisation framework is designed methodically to identify parameter clusters with distinct sensitivities to specific EIS impedance regions, significantly improving identification accuracy. The generic particle swarm optimisation is fused with nature-inspired Darwinian events cross-generating new particles using selected parents to improve the algorithm’s predictions. The proposed data-driven parameter identification framework is benchmarked under multiple cell operating conditions and achieves a voltage prediction improvement of 28% for constant current discharge and 65% for transient duty cycles compared to an experimentally derived parameter set from the literature.

Piecewise Linear Strain Cosserat Model for Soft Slender Manipulator

Recently soft robotics has rapidly become a novel and promising area of research with many designs and applications due to their flexible and compliant structure. However, it is more difficult to derive the nonlinear dynamic model of such soft robots. The differential kinematics and dynamics of the soft manipulator can be formulated as a set of highly nonlinear partial differential equations (PDEs) via the classic Cosserat rod theory. In this work, we propose a discrete modeling technique named piecewise linear strain (PLS) to solve the PDEs of Cosserat-based models, based on which the associated analytic models are deduced. To validate the accuracy of the proposed Cosserat model, the static model of the conical cantilever rod under gravity as a simple example is simulated by using different discretization methods. Results indicate that PLS Cosserat model is comparable to the mechanical deformation behavior of a real-world soft manipulator. Finally, a parameters identification scheme for this model is established, and the simulation as well as experimental validation demonstrate that using this method can identify the model physical parameters with high accuracy.

A parameter identification scheme of the visco-hyperelastic constitutive model of rubber-like materials based on general regression neural network

A parameter identification scheme of the visco-hyperelastic constitutive model of rubber-like materials based on general regression neural network

Poro-viscoelastic material parameter identification of brain tissue-mimicking hydrogels.

Understanding and characterizing the mechanical and structural properties of brain tissue is essential for developing and calibrating reliable material models. Based on the Theory of Porous Media, a novel nonlinear poro-viscoelastic computational model was recently proposed to describe the mechanical response of the tissue under different loading conditions. The model contains parameters related to the time-dependent behavior arising from both the viscoelastic relaxation of the solid matrix and its interaction with the fluid phase. This study focuses on the characterization of these parameters through indentation experiments on a tailor-made polyvinyl alcohol-based hydrogel mimicking brain tissue. The material behavior is adjusted to ex vivo porcine brain tissue. An inverse parameter identification scheme using a trust region reflective algorithm is introduced and applied to match experimental data obtained from the indentation with the proposed computational model. By minimizing the error between experimental values and finite element simulation results, the optimal constitutive model parameters of the brain tissue-mimicking hydrogel are extracted. Finally, the model is validated using the derived material parameters in a finite element simulation.

Robust adaptive model predictive control with persistent excitation conditions

For constrained linear systems with bounded disturbances and parametric uncertainty, we propose a robust adaptive model predictive control strategy with online parameter estimation. Constraints enforcing persistently exciting closed loop control actions are introduced for a set-membership parameter identification scheme. The algorithm requires the online solution of a convex program, satisfies constraints robustly, and ensures recursive feasibility and input-to-state stability. Almost sure convergence to the actual system parameters is demonstrated under assumptions on stabilizability, reachability, and tight disturbance bounds.

Cooperative Filtering and Parameter Identification for Advection–Diffusion Processes Using a Mobile Sensor Network

This article presents an online parameter identification scheme for advection–diffusion processes using data collected by a mobile sensor network. The advection–diffusion equation is incorporated into the information dynamics associated with the trajectories of the mobile sensors. A constrained cooperative Kalman filter is developed to provide estimates of the field values and gradients along the trajectories of the mobile sensors so that the temporal variations in the field values can be estimated. This leads to a co-design scheme for state estimation and parameter identification for advection–diffusion processes that is different from comparable schemes using sensors installed at fixed spatial locations. Using state estimates from the constrained cooperative Kalman filter, a recursive least-square (RLS) algorithm is designed to estimate unknown model parameters of the advection–diffusion processes. Theoretical justifications are provided for the convergence of the proposed cooperative Kalman filter by deriving a set of sufficient conditions regarding the formation shape and the motion of the mobile sensor network. Simulation and experimental results show satisfactory performance and demonstrate the robustness of the algorithm under realistic uncertainties and disturbances.

Speed-Controller-Independent Mechanical Parameter Identification in SPMSM Drive Achieved via Signal Injection

The mechanical parameters, including the Coulomb and viscous friction coefficients together with the moment of inertia, are of great interest for the performance optimization and condition evaluation of surface-mounted permanent magnet synchronous motor (SPMSM) drives. This article contributes a speed-controller-independent mechanical parameter identification scheme using open-loop speed response due to signal injection, which has not been reported in existing works. It is designed based on the premise that the viscous friction torque is disproportional to the rotor speed, which is a new progress toward estimating the mechanical parameters with high precision. Moreover, as a practical full-participation-type solution, this scheme involves a new electrical parameter estimation technique based on differential algebra to assist in identifying the mechanical parameters. This auxiliary technique attains the current-controller-free estimation of the electrical parameters with the inverter nonlinearity eliminated, thereby addressing the current controller self-commissioning and serving to acquire the accurate electromagnetic torque. The experimental evaluations on two prototype SPMSM drive systems validate the feasibility of this suggested scheme.

Time-dependent hyper-viscoelastic parameter identification of human articular cartilage and substitute materials

Numerical simulations are a valuable tool to understand which processes during mechanical stimulations of hydrogels for cartilage replacement influence the behavior of chondrocytes and contribute to the success or failure of these materials as implants. Such simulations critically rely on the correct prediction of the material response through appropriate material models and corresponding parameters. In this study, we identify hyper-viscoelastic material parameters for numerical simulations in COMSOL Multiphysics® v. 5.6 for human articular cartilage and two replacement materials, the commercially available ChondroFillerliquid and oxidized alginate gelatin (ADA-GEL) hydrogels. We incorporate the realistic experimental boundary conditions into an inverse parameter identification scheme based on data from multiple loading modes simultaneously, including cyclic compression-tension and stress relaxation experiments. We provide individual parameter sets for the unconditioned and conditioned responses and discuss how viscoelastic effects are related to the materials’ microstructure. ADA-GEL and ChondroFillerliquid exhibit faster stress relaxation than cartilage with lower relaxation time constants, while cartilage has the largest viscoelastic stress contribution. The elastic response predominates in ADA-GEL and ChondroFillerliquid, while the viscoelastic response predominates in cartilage. These results will help to simulate mechanical stimulations, support the development of suitable materials with distinct mechanical properties in the future and provide parameters and insight into the time-dependent material behavior of human articular cartilage.

Identification of temperature-dependent elastic and damping parameters of carbon–epoxy composite plates based on experimental modal data

High-strength composite materials are receiving increased attention within the aerospace and transportation industries. These materials, although light in weight, still impart high stiffness when compared to conventional structural materials, e.g., aluminum and steel. Fibers and matrix are the basic constituents of composite materials. During high-speed maneuvering of aircraft and high-speed trains, the composite materials are subjected to dynamic loads in changing temperatures. The dynamic behavior of composites strongly depends on the ambient thermal environment. Furthermore, during the in-situ operation, the quantification of real-time dynamic loads is a challenging task. Therefore, the experimental investigation of the dynamic behavior of several carbon-fiber epoxy laminated composite plates at different temperatures, namely 0°C, 25°C, 50°C, 75°C, 100°C, and 125°C, is carried out using operational modal analysis, to identify the modal characteristics of the structure, and the temperature-dependent modal data of the tested composite plates is given in the supplementary data. The temperature-dependent elastic and damping parameters of the carbon–epoxy laminate are estimated using a genetic algorithm-based parameter identification scheme for different sets of modal contribution. A combined experimental and numerical simulation procedure is implemented to estimate deterministic material parameters at different temperatures. To obtain the in-situ material parameters for a given operating frequency range, the modal contribution is selected such that the operating frequency of interest lies within the considered resonance modes. As an example of matrix-dominated elastic parameter, the shear modulus, has been found to degrade significantly with increasing temperature, and shown a strong correlation with temperature.

Machine learning-assisted parameter identification for constitutive models based on concatenated loading path sequences

A hybrid strategy for the identification of material parameters of constitutive models is presented. One main challenge in the context of classic optimisation-based parameter identification schemes is the generation of adequate starting values for the multi-objective optimisation procedure. We address this issue by employing an artificial neural network that is trained with data obtained from the material model, more precisely speaking, from different solutions of the direct problem for homogeneous states of deformation. As a result, a solution of the inverse problem of parameter identification can be approximated with the help of a neural network processing experimental data. This approximate solution is used as a starting value for a subsequent, classic optimisation-based parameter identification approach. One key advantage of this strategy is that a network only has to be trained once per material model and can subsequently be applied for different materials. A rather sophisticated material model, incorporating gradient-enhanced damage coupled to plasticity in a geometrically non-linear setting, is used to demonstrate the capabilities of this approach. Two different strategies for the generation of training data are investigated and compared, and the neural network’s hyperparameters are optimised for improved prediction capabilities. In view of the calibration of material parameters related to the non-locality of the model, the obtained set of parameters is again considered as a starting value for a final optimisation step, where we consider inhomogeneous states of deformation and digital image correlation data.