System Identification Method Research Articles

System Identification for Battery State Prediction and Lifespan Estimation

In this paper, a nonlinear system Identification method, wavelet-network-based Nonlinear Auto-Regressive Exogenous (NLARX) approach, is employed for battery state estimation and lifespan estimation. More specifically, three key battery parameters and health metrics, including temperature, voltage and State of Charge (SOC), are estimated and these parameters are essential for condition or state monitoring. Further, State of Health (SOH), crucial for forecasting the battery remaining useful life, is also predicted. Two open datasets are used to train and validated the performance of the proposed method. For temperature and voltage forecasting, the NLARX model outperforms the existing Thermal Single Particle Model with electrolyte (TSPMe) for prediction horizons under 600 seconds. In SOC estimations, the NLARX method produces consistent 15-second ahead prediction results even only using a small percentage of training data, while the SOH estimation, the proposed metho provides precise SOH variation prediction for 400 post cycles with less than 10% of the batterys life for training. Extensive results demonstrates that the NLARX model’s promise for the precise prediction of key battery parameters and health metrics and it can be used as a useful tool for battery fault detection and remaining useful life prediction.

Feasibility of Human Wrist-joint Neuromuscular System Identification Method Using Functional Electrical Stimulation in Clinical Examinations

Feasibility of Human Wrist-joint Neuromuscular System Identification Method Using Functional Electrical Stimulation in Clinical Examinations

Deep Neural Network-Based System Identification for Nonlinear MPC: Enhancements for Massive Multi-Output Systems and Experimental Validation with 1D Camera Image Outputs*

In this research, we investigate a system identification method based on deep neural networks for nonlinear Model Predictive Control (MPC), focusing on efficiently managing massive multi-output systems. This method involves the direct synthesis of state estimators and output predictors represented by neural networks from experimental data. The integration of these components with the Levenberg-Marquardt optimization method, coupled with the use of automatic differentiation, enables efficient realization of nonlinear MPC.In this research, we propose a specific architecture for the state estimator and output predictor, designed to suit multi-output systems. This approach is applied to a miniature four-wheeled vehicle equipped with a 1D camera, which generates 160-pixel image outputs. The experimental application to this test vehicle demonstrates the method's capability in effectively managing complex, multi-output systems.

Adaptive exchange protocol for multi-agent communication in augmented reality system

Augmented Reality (AR) is one of the modern ways of providing users with different types of information. In applications based on this technology, the main influence on the subjective assessment of product quality is the speed of interaction between the system and the user and between users of the system. The main purpose of the work series are creation and modification for algorithms to assessing and improving the quality of AR services. This paper describes and justifies an adaptive communication protocol for multi-agent interaction. We consider a general nonlinear dynamic system with introducing feedback control which is based on measurements under almost arbitrary noise. In practice, this control is realized as a superposition of neural networks with the estimation of the result of mutual additional learning based on the system identification method (M.K.Campi and E.Weyer’s LSCR is used). The prototype is built for a system with a distributed server architecture and multiagent behavior of both clients and servers in the system.

Gradient-based Iterative Identification Method for Non-uniformly Sampled Input-nonlinear Systems

Gradient-based Iterative Identification Method for Non-uniformly Sampled Input-nonlinear Systems

Spatial–temporal distribution and key factors of urban land use ecological efficiency in the Loess Plateau of China

Urban land use ecological efficiency is crucial to the urbanization process and urban ecosystem sustainability. Cities in ecologically sensitive zones with frequent natural disasters need more complex land use patterns and plans. Achieving the goal of harmonizing economy and ecosystem is key for sustainable development policy makers in these cities. Aiming to explore the urban land use ecological efficiency (LUEE) of ecologically sensitive areas, urban land use ecological efficiency index system of the Loess Plateau was constructed, the SBM-Tobit model was adopted to measure the LUEE and influencing factors from 2009 to 2018, and the characteristics of spatial–temporal evolution was discussed. The results indicated that there were significant spatial differences of LUEE in ecologically sensitive zone. The high-level cities of LUEE were located in the southwest areas, while low-level cities of LUEE were mostly situated in the northeast zones, and the temporal variation trend showed the characteristic of “W” curve. Additionally, the results of key factors identification demonstrated that science and technology expenditure and public transport development had positive effects on urban LUEE, while the land expansion, GDP growth, the second industry and real estate development will limit the improvement of urban LUEE. This study used the scientific evaluation index system and key factors identification method to explore the land use ecological efficiency in ecologically sensitive zones, aiming to provide a case study reference for urban land management and optimization in ecologically fragile areas.

Dynamic modeling and motion prediction of two projectiles launched successively underwater

The technology of successive underwater launching of multiple projectiles is a growing trend in military development. However, the process is complex, and the wake field formed after the launch of the first projectile can significantly impact the trajectories and attitudes of the subsequent projectiles. This can potentially lead to mutual collisions or destabilization of motion among the projectiles, resulting in mission failures. In this paper, a mathematical model incorporating unknown parameters is proposed to effectively describe the underwater motion of projectiles. To refine this model, we employed a system identification method that utilizes experimentally validated numerical simulation data to solve for the unknown parameters. The model is then used to predict the trajectory and attitude changes of the projectiles across various launching conditions. The results indicate that to ensure a successful launch mission, certain adjustments must be made. These adjustments include increasing the launching velocity within acceptable limits, extending the launch time interval, increasing the spatial distances between projectiles, and reducing the platform velocity.

An improved dynamic model identification method for small unmanned helicopter

PurposeThe purpose of this paper is to introduce an improved system identification method for small unmanned helicopters combining adaptive ant colony optimization algorithm and Levy’s method and to solve the problem of low model prediction accuracy caused by low-frequency domain curve fitting in the small unmanned helicopter frequency domain parameter identification method.Design/methodology/approachThis method uses the Levy method to obtain the initial parameters of the fitting model, uses the global optimization characteristics of the adaptive ant colony algorithm and the advantages of avoiding the “premature” phenomenon to optimize the initial parameters and finally obtains a small unmanned helicopter through computational optimization Kinetic models under lateral channel and longitudinal channel.FindingsThe algorithm is verified by flight test data. The verification results show that the established dynamic model has high identification accuracy and can accurately reflect the dynamic characteristics of small unmanned helicopter flight.Originality/valueThis paper presents a novel and improved frequency domain identification method for small unmanned helicopters. Compared with the conventional method, this method improves the identification accuracy and reduces the identification error.

A New Method for Dynamical System Identification by Optimizing the Control Parameters of Legendre Multiwavelet Neural Network

Wavelet neural networks have been widely applied to dynamical system identification fields. The most difficult issue lies in selecting the optimal control parameters (the wavelet base type and corresponding resolution level) of the network structure. This paper utilizes the advantages of Legendre multiwavelet (LW) bases to construct a Legendre multiwavelet neural network (LWNN), whose simple structure consists of an input layer, hidden layer, and output layer. It is noted that the activation functions in the hidden layer are adopted as LW bases. This selection if based on the its rich properties of LW bases, such as piecewise polynomials, orthogonality, various regularities, and more. These properties contribute to making LWNNs more effective in approximating the complex characteristics exhibited by uncertainties, step, nonlinear, and ramp in the dynamical systems compared to traditional wavelet neural networks. Then, the number of selection LW bases and the corresponding resolution level are effectively optimized by the simple Genetic Algorithm, and the improved gradient descent algorithm is implemented to learn the weight coefficients of LWNN. Finally, four nonlinear dynamical system identification problems are applied to validate the efficiency and feasibility of the proposed LWNN-GA method. The experiment results indicate that the LWNN-GA method achieves better identification accuracy with a simpler network structure than other existing methods.

Experimental admittance-based system identification for equivalent circuit modeling of piezoelectric energy harvesters on a plate

Equivalent circuit modeling is a useful tool for piezoelectric energy harvesters to analyze the electromechanical response of the system especially when complex host structure geometries and nonlinear circuits are used in the harvesting systems. Previous studies have used analytical and finite element models to estimate the equivalent circuit model (ECM) of piezoelectric energy harvesters (PEH) on beam- and plate-like structures. However, those methods require accurate analytical and/or numerical representation of the PEH. Here, we present an experimental admittance-based system identification method that allows us to identify the multi-modal ECM without prior knowledge of the host plate's geometry and/or physical properties of the piezoelectric patches. Using the proposed experimental method, we obtain the electromechanical frequency–response admittance of the PEH system at each vibration mode, and thereby, we calculate the equivalent system parameters. Additionally, a novel experimental technique is presented for the identification of the equivalent voltage sources associated with each LCR branch of the ECM. The derived ECM is experimentally validated for single and multiple piezoelectric patch harvesters on a plate. The electrical frequency response of the system has been validated for standard AC and rectifier circuits using SPICE software. Overall, the proposed admittance-based system identification is an accurate and robust method to identify the equivalent system parameters, making it a practical and reliable tool for modeling piezoelectric energy harvesting systems.

Modal analysis of earthquake records for dams using stochastic subspace based on error analysis

AbstractSystem identification based on seismic data is crucial for updating numerical analyses and implementing health monitoring strategies for critical structures such as dams. However, most system identification methods have been developed with the assumption of white noise and stationarity of excitations, which is inconsistent with the nature of seismic data. As a result, the systems estimated from such methods are often associated with high uncertainties, making obtaining accurate and reliable results challenging. In this study, a new figure of merit (FOM) is proposed for tracking the ill‐conditioning of systems by analyzing the errors in stochastic subspace identification (SSI) through the inversion process. Unlike the condition number, which measures the ratio between the largest and smallest singular values, this FOM uses all singular values to assess the system's ill‐conditioning comprehensively. A semi‐automatic identification method based on SSI has been developed using the proposed FOM. The optimal dimensions of the Hankel matrix are obtained so that none of the system error components dominate. Frequency/damping clustering is used to overcome the identified system's over‐determination and remove outliers. Finally, the complexity of the modal shapes resulting from clustering is examined to validate the structural modal characteristics. The procedure was validated using seismic data from the Finite Element Model of Koyna Dam. It identified the first four modal frequencies of the structure with less than 2% error rate. The average error in estimating damping ratios was below 20%. The algorithm was then used to analyze the seismic monitoring data of Pacoima Dam for the Chino Hill 2008 and Granada Hills 2020 earthquakes. Despite the closely spaced Pacoima Dam modes, the study demonstrated excellent accuracy and robust methodology performance. By distinguishing the structural modes related to the dam body from other modes of the “dam‐foundation‐reservoir” system, the proposed method helps to determine the existing model's nonlinearity.

Identification of structured nonlinear state–space models for hysteretic systems using neural network hysteresis operators

Hysteretic system behavior is ubiquitous in science and engineering fields including measurement systems and applications. In this paper, we put forth a nonlinear state–space system identification method that combines the state–space equations to capture the system dynamics with a compact and exact artificial neural network (ANN) representation of the classical Prandtl–Ishlinskii (PI) hysteresis. These ANN representations called PI hysteresis operator neurons employ recurrent ANNs with classical activation functions, and thus can be trained with classical neural network learning algorithms. The structured nonlinear state–space model class proposed in this paper, for the first time, offers a flexible interconnection of PI hysteresis operators with a linear state–space model through a linear fractional representation. This results in a comprehensive and flexible model structure. The performance is validated both on numerical simulation and on measurement data.

Frequency Domain Identification of Continuous-Time Hammerstein Systems With Adaptive Continuous-Time Rational Orthonormal Basis Functions

In this paper, we propose a new identification method for continuous-time Hammerstein systems. This method is based on the adaptive generalized rational orthonormal basis functions. In particular, we use the strategy in which poles of basis functions are selected one by one in terms of a criterion. By doing this, we not only take advantage of the variability of the generalized orthogonal basis functions, but also reduce the computational burden. The proposed algorithm is adaptive. Meanwhile, convergence analysis is performed with considering the sampling frequency, noise level, sampling length, and terms of basis functions. The obtained results guarantee the convergence of the whole Hammerstein system. Finally, a numerical example is presented to illustrate the usefulness of the proposed identification algorithm.

Research on system of ultra-flat carrying robot based on improved PSO algorithm

Ultra-flat carrying robots (UCR) are used to carry soft targets for functional safety road tests of intelligent driving vehicles and should have superior control performance. For the sake of analyzing and upgrading the motion control performance of the ultra-flat carrying robot, this paper develops the mathematical model of its motion control system on the basis of the test data and the system identification method. Aiming at ameliorating the defects of the standard particle swarm optimization (PSO) algorithm, namely, low accuracy, being susceptible to being caught in a local optimum, and slow convergence when dealing with the parameter identification problems of complex systems, this paper proposes a refined PSO algorithm with inertia weight cosine adjustment and introduction of natural selection principle (IWCNS-PSO), and verifies the superiority of the algorithm by test functions. Based on the IWCNS-PSO algorithm, the identification of transfer functions in the motion control system of the ultra-flat carrying robot was completed. In comparison with the identification results of the standard PSO and linear decreasing inertia weight (LDIW)-PSO algorithms, it indicated that the IWCNS-PSO has the optimal performance, with the number of iterations it takes to reach convergence being only 95 and the fitness value being only 0.117. The interactive simulation model was constructed in MATLAB/Simulink, and the critical proportioning method and the IWCNS-PSO algorithm were employed respectively to complete the tuning and optimization of the Proportional-Integral (PI) controller parameters. The results of simulation indicated that the PI parameters optimized by the IWCNS-PSO algorithm reduce the adjustment time to 7.99 s and the overshoot to 13.41% of the system, and the system is significantly improved with regard to the control performance, which basically meets the performance requirements of speed, stability, and accuracy for the control system. In conclusion, the IWCNS-PSO algorithm presented in this paper represents an efficient system identification method, as well as a system optimization method.

Quantifying Individual Variability in Neural Control Circuit Regulation Using Single-Subject fMRI

As a field, control systems engineering has developed quantitative methods to characterize the regulation of systems or processes, whose functioning is ubiquitous within synthetic systems. In this context, a control circuit is objectively “well regulated” when discrepancy between desired and achieved output trajectories is minimized and “robust” to the degree that it can regulate well in response to a wide range of stimuli. Most psychiatric disorders are assumed to reflect dysregulation of brain circuits. Yet, probing circuit regulation requires fundamentally different analytic strategies than the correlations relied upon for analyses of connectivity and their resultant networks. Here, we demonstrate how well-established methods for system identification in control systems engineering may be applied to functional magnetic resonance imaging (fMRI) data to extract generative computational models of human brain circuits. As required for clinical neurodiagnostics, we show these models to be extractable even at the level of the single subject. Control parameters provide two quantitative measures of direct relevance for psychiatric disorders: a circuit’s sensitivity to external perturbation and its dysregulation.

System identification in presence of undetectable packet losses in the actuation path

Identification of networked systems over communication links with packet losses is considered. It is assumed that there is no connection from the actuators to the system identifier such that no information is available on loss of data packets carrying the actuation signals. While the packet loss occurrences in the sensor links are naturally detectable by the system identifier as the receiving node, the packet loss occurrences in the actuation paths are not directly detectable. The existing system identification methods fail to be applicable without the information on packet loss occurrences. Presenting a solution for this issue is the objective of the present work. Two recursive algorithms are proposed to solve the problem in real time. For this purpose, ARMAX input–output representations of the system with Markovian jumping modes are developed. Then, filters are designed for estimation of the packet loss occurrences. The estimated mode is used in the recursive algorithms for identification of the whole system based on the maximum likelihood approach. A main algorithm and its simplified version are proposed that are capable of finding the various kinds of system parameters including the physical plant parameters and the communication mode transition probabilities. Effectiveness of the algorithms is verified through simulation examples where the system performance is evaluated and compared.

Adaptive neural identification and non-singular control of pure-feedback nonlinear systems

This paper proposes a new constructive identification and adaptive control method for nonlinear pure-feedback systems, which remedies the ’explosion of complexity’ and potential control singularity encountered in the traditional adaptive backstepping controllers. First, to avoid using the backstepping recursive design, alternative state variables and the corresponding coordinate transformation are introduced to reformulate the pure-feedback system into an equivalent canonical model. Then, a high-order sliding mode (HOSM) observer is used to reconstruct the unknown states for this canonical model. To remedy the potential singularity in the control, the unknown system dynamics are lumped to derive an alternative identification structure and one-step control synthesis, where two radial basis function neural networks (RBFNN) are adopted to online estimate these lumped dynamics. In this framework, the online estimation of control gain is not in the denominator of controller, and thus the division by zero in the controllers is avoided. Finally, a new online learning algorithm is constructed to obtain the RBFNNs’ weights, ensuring the convergence to the neighborhood of true values and allowing accurate identification of unknown dynamics. Theoretical analysis elaborates that the convergence of both the tracking error and the estimation error is obtained simultaneously. Simulations and practical experiments on a hydraulic servo test-rig verify the effectiveness and utility of the suggested methods.

Combined invariant subspace & frequency-domain subspace method for identification of discrete-time MIMO linear systems

Combined invariant subspace & frequency-domain subspace method for identification of discrete-time MIMO linear systems

A Levenberg-Marquardt Algorithm for Sparse Identification of Dynamical Systems.

Low complexity of a system model is essential for its use in real-time applications. However, sparse identification methods commonly have stringent requirements that exclude them from being applied in an industrial setting. In this article, we introduce a flexible method for the sparse identification of dynamical systems described by ordinary differential equations. Our method relieves many of the requirements imposed by other methods that relate to the structure of the model and the dataset, such as fixed sampling rates, full state measurements, and linearity of the model. The Levenberg-Marquardt algorithm is used to solve the identification problem. We show that the Levenberg-Marquardt algorithm can be written in a form that enables parallel computing, which greatly diminishes the time required to solve the identification problem. An efficient backward elimination strategy is presented to construct a lean system model.

Identification of polynomial nonlinear systems based on center manifold

A center-manifold (CM) based system identification (SID) method is developed to identify multi-input-multi-output continuous-time polynomial nonlinear systems in the state–space form. On one hand, the CM method is a natural extension of the frequency-domain subspace method to nonlinear SID problems. It decomposes the nonlinear SID problem into a series of linear least-square problems (in a precise sense), which can be solved analytically. This is in contrast to many existing methods that rely on iterative search of the global minimum. On the other hand, it is also a generalization of the invariant-subspace (ISP) based SID method. It inherits the nice property of the ISP method that the advantages of time-domain and frequency-domain SID methods are combined. In the paper, a persistence of excitation (PE) condition for the CM method is derived, which is described by the rank of certain Khatri–Rao product. Under the PE condition, the identification error is shown to be bounded within an arbitrarily small neighborhood of the origin when measurement noise is presented.