Online Parameter Identification Method Research Articles

A model reference adaptive system approach for nonlinear online parameter identification

Dynamical systems, for instance in model predictive control, often contain unknown parameters, which must be determined during system operation. Online, or on-the-fly, parameter identification methods are therefore necessary. The challenge of online methods is that one must continuously estimate parameters as experimental data becomes available. The existing techniques in the context of time-dependent partial differential equations exclude the case where the system depends nonlinearly on the parameters. Based on a model reference adaptive system approach, we present an online parameter identification method for nonlinear infinite-dimensional evolutionary systems.

Energy modeling and online parameter identification for permanent magnet synchronous motor driven belt conveyors

This paper studies the problems of energy modeling and online parameter identification method for permanent magnet synchronous motor (PMSM) driven belt conveyors. Firstly, a new dynamic energy model is established by combining the existing energy model and the PMSM dynamic model. The model is free of the drive system efficiency which is usually not known exactly. Then, a parameter identification method is designed based on load observer, bulk material mass calculator and recursive least squares (RLS), by which a two-stage parameter identification method with waiting period is further proposed to overcome the influence of load observation errors and large differences between the model parameters. Finally, the effectiveness and feasibility of the proposed method are verified by a hardware-in-the-loop simulation system.

Gradient Descent Optimization Based Parameter Identification for FCS-MPC Control of LCL-Type Grid Connected Converter

Aging and temperature changes in the passive components of an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> -filter grid connected converter system (GCCs) may lead to parameter uncertainties, which can in turn influence its modeling accuracy for finite-control-set model predictive control (FCS-MPC). The presence of model errors will change the resonance point and deteriorate the power quality of the grid current, in turn degrading the active damping performance. In this situation, there is a serious possibility that the GCCs may malfunction and automatically disconnect from the grid, causing great challenges to the system stability. To solve this problem, first, prediction error analysis in FCS-MPC due to the model parameter errors is presented. Second, to achieve high accuracy and fast filter parameter estimation in utility, an adaptive online parameter identification method based on gradient descent optimization (GDO) has been proposed. Finally, to further reduce the searching time needed by the optimal iteration step, a variable iteration step searching method based on the root-mean-square-prop (RMSprop) GDO method is proposed. Experimental studies of an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LCL</i> -GCCs prototype in the laboratory have been conducted to validate the effectiveness of the proposed method.

A Vehicle-Model-Aided Navigation Reconstruction Method for a Multicopter during a GPS Outage

The integrated navigation of inertial navigation systems (INS) and the Global Positioning System (GPS) is essential for small unmanned aerial vehicles (UAVs) such as multicopters, providing steady and accurate position, velocity, and attitude information. Nevertheless, decreasing navigation accuracy is a serious threat to flight safety due to the long-term drift error of INS in the absence of GPS measurements. To bridge the GPS outage for multicopters, this paper proposes a novel navigation reconstruction method for small multicopters, which combines the vehicle dynamic model and micro-electro-mechanical system (MEMS) sensors. Firstly, an induced drag model is introduced into the dynamic model of the vehicle, and an efficient online parameter identification method is designed to estimate the model parameters quickly. Secondly, the body velocity can be calculated from the vehicle model and accelerometer measurement. In addition, the nongravitational acceleration estimated from body velocity and radar height are utilized to yield a more accurate attitude estimate. Fusing the information of the attitude, body velocity, magnetic heading, and radar height, a navigation system based on an error-state Kalman filter is reconstructed. Then, an adaptive measurement covariance algorithm based on a fuzzy logic system is designed to reduce the weight due to the disturbed acceleration. Finally, the hardware-in-loop experiment is carried out to demonstrate the effectiveness of the proposed method. Simulation results show that the proposed navigation reconstruction algorithm aided by the vehicle model can significantly improve navigation accuracy during a GPS outage.

Fast and Comprehensive Online Parameter Identification of Switched Reluctance Machines

The switched reluctance machine has been an attractive candidate for many applications owing to its simple design and low construction costs, without the use of permanent magnets. However, the double saliency of its stator and rotor poles results in noise-causing torque ripples. And although advanced torque ripple minimization control techniques exist, they rely on modeling the machine, which in turn requires specialized offline experimental setups or online (during operation) parameter identification techniques. To date, existing online techniques are iterative without proof of convergence, do not provide all model parameters, and/or rely on a priori information that can change after the machine is commissioned. In this work, an online parameter identification method is developed with a new empirical model of its flux linkage and electromagnetic torque, to provide a complete nonlinear model of the machine. With two seconds of data collected online, all electrical and mechanical parameters are identified using a non-iterative algorithm, and so it does not pose a risk of divergence. Therefore, parameter identification can be reliably and frequently carried out at different operating conditions as the machine ages for diagnostics. Also, the resulting model is designed to be used by advanced torque ripple minimization control techniques. The implementation procedure is detailed along with simulation results to demonstrate its efficacy.

Lithium-Sulfur Cell State of Charge Estimation Using a Classification Technique

Lithium-Sulfur (Li-S) batteries are a promising next-generation technology providing high gravimetric energy density compared to existing lithium-ion (Li-ion) technologies in the market. The literature shows that in Li-S, estimation of state of charge (SoC) is a demanding task, in particular due to a large flat section in the voltage-SoC curve. This study proposes a new SoC estimator using an online parameter identification method in conjunction with a classification technique. This study investigates a new prototype Li-S cell. Experimental characterization tests are conducted under various conditions; the duty cycle – intended to represent a real-world application – is based on an electric city bus. The characterization results are then used to parameterize an equivalent-circuit-network (ECN) model, which is then used to relate real-time parameter estimates derived using a Recursive Least Squares (RLS) algorithm to state of charge using a Support Vector Machine (SVM) classifier to estimate an approximate SoC range. The estimate is used together with a conventional coulomb-counting technique to achieve continuous SoC estimation in real-time. It is shown that this method can provide an acceptable level of accuracy with less than 3% error under realistic driving conditions.

Parameter Identification of Spatial–Temporal Varying Processes by a Multi-Robot System in Realistic Diffusion Fields

SUMMARYIn this article, we investigate the problem of parameter identification of spatial–temporal varying processes described by a general nonlinear partial differential equation and validate the feasibility and robustness of the proposed algorithm using a group of coordinated mobile robots equipped with sensors in a realistic diffusion field. Based on the online parameter identification method developed in our previous work using multiple mobile robots, in this article, we first develop a parameterized model that represents the nonlinear spatially distributed field, then develop a parameter identification scheme consisting of a cooperative Kalman filter and recursive least square method. In the experiments, we focus on the diffusion field and consider the realistic scenarios that the diffusion field contains obstacles and hazard zones that the robots should avoid. The identified parameters together with the located source could potentially assist in the reconstruction and monitoring of the field. To validate the proposed methods, we generate a controllable carbon dioxide (CO2) field in our laboratory and build a static CO2 sensor network to measure and calibrate the field. With the reconstructed realistic diffusion field measured by the sensor network, a multi-robot system is developed to perform the parameter identification in the field. The results of simulations and experiments show satisfactory performance and robustness of the proposed algorithms.

Development of a new method for online parameter identification in seismically excited smart building structures using virtual synchronization and adaptive control design

In this paper a new method for online parameter identification and damage detection in smart building structures that are subjected to arbitrary seismic excitation is proposed. It uses real-time measurements of a structure's motion to identify its unknown constant or piecewise constant parameters such as stiffness, damping and mass over the time. The method is based on elements of system synchronization and adaptive control theories. First, a computational system, called the virtual system, is defined. Next, by using properly designed controller and estimations for the unknown parameters, the state of the virtual system is forced to follow the measured motion of the real structure. The mentioned estimations are computed from a proposed update law which depends on the measured motion of the real structure and the virtual system’s state. A major theoretical novelty of this paper is a proposed convergence condition which is applicable in case of arbitrary external forces or ground acceleration. It is shown that upon the satisfaction of that condition, as the synchronization completes, the computed estimation function converges to the true value of the vector of unknown parameters. In addition, an important practical contribution presented in this study is the introduction of a technique called scale factors. It helps to use available initial guesses of the unknown parameters to improve the speed of online identification. Numerical examples show that the proposed method is promising and has a good performance in both online identification and online damage detection problems.

Research on online parameter identification of interior permanent magnet synchronous motor based on augmented robust forgetting factor recursive least square

AbstractThe high‐performance motor control relies on the accuracy of the interior permanent magnet synchronous motor (IPMSM) parameters. In order to automatically identify the electrical parameters of IPMSM, an online parameter identification method has been developed based on robust adjustable and double tracking error to improve the identification accuracy of IPMSM parameters, which has been verified by both simulation and experiment. Also the torque closed loop was used to check the accuracy and reliability of the parameter identification. On the one hand, the simulation results show that the identification error was less than 10%, decreasing the response time and enhancing the robustness of parameter identification. On the other hand, the experimental results derived from the TC1782‐based platform verify the effectiveness and robustness of the proposed method, above all results indicating that the present identification algorithm has excellent performances of IPMSM drives system and engineering practicability.

An online and real-time adaptive operational modal parameter identification method based on fog computing in Internet of Things

A large number of smart devices make the Internet of Things world smarter. However, currently cloud computing cannot satisfy real-time requirements and fog computing is a promising technique for real-time processing. Operational modal analysis obtains modal parameters that reflect the dynamic properties of the structure from the vibration response signals. In Internet of Things, the operational modal analysis method can be embedded in the smart devices to achieve structural health monitoring and fault detection. In this article, a four-layer framework for combining fog computing and operational modal analysis in Internet of Things is designed. This four-layer framework introduces fog computing to solve tasks that cloud computing cannot handle in real time. Moreover, to reduce the time and space complexity of the operational modal analysis algorithm and support the real-time performance of fog computing, a limited memory eigenvector recursive principal component analysis–based operational modal analysis approach is proposed. In addition, by examining the cumulative percent variance of principal component analysis, this article explains the reasons behind the identified modal order exchange. Finally, the time-varying operational modal identification results from non-stationary random response signals of a cantilever beam whose density changes slowly indicate that the limited memory eigenvector recursive principal component analysis–based operational modal analysis method requires less memory and runtime and has higher stability and identification effect.

Optimal regenerative braking torque of permanent-magnet synchronous motor in electric vehicles

This study investigated a method to determine the optimal regenerative braking torque with maximum energy recovery at given speed, which is essential for the regenerative braking control of electric vehicles. The method is based on the loss modelling of a permanent-magnet synchronous motor (PMSM) system, which consists of electric motor and inverter. The system-loss model is established based on a PMSM mathematical model including iron loss and is used to derive the optimal regenerative braking torque symbolic formula. In addition, the parameter sensitivity of the optimal regenerative braking torque is analysed and a method for online parameter identification is used to obtain the actual parameter values in real time. Thus, the accuracy and adaptability of the optimal regenerative braking torque during the motor-operation process is improved. The analysis results are validated through simulation and bench tests.

Optimal regenerative braking torque of permanent-magnet synchronous motor in electric vehicles

This study investigated a method to determine the optimal regenerative braking torque with maximum energy recovery at given speed, which is essential for the regenerative braking control of electric vehicles. The method is based on the loss modelling of a permanent-magnet synchronous motor (PMSM) system, which consists of electric motor and inverter. The system-loss model is established based on a PMSM mathematical model including iron loss and is used to derive the optimal regenerative braking torque symbolic formula. In addition, the parameter sensitivity of the optimal regenerative braking torque is analysed and a method for online parameter identification is used to obtain the actual parameter values in real time. Thus, the accuracy and adaptability of the optimal regenerative braking torque during the motor-operation process is improved. The analysis results are validated through simulation and bench tests.

Online Thevenin Equivalent Parameter Identification Method of Large Power Grids Using LU Factorization

Real-time tracking and identification of Thevenin equivalent parameters is crucial for voltage stability online monitoring of large power grids. An online parameter identification method based on wide-area measurements and nodal analysis is proposed in this paper. The node-voltage equation is utilized directly, and the open-circuit voltage is calculated accurately as the Thevenin equivalent voltage of load buses. Moreover, the equivalent parameters can be identified precisely under the generator reactive power over-limit condition. To avoid repetitive Gaussian elimination of the high-dimensional linear system, lower upper factorization and optimized substitution are used to accelerate the open-circuit voltage solution process. The simulation results of multiple test systems demonstrate the accuracy and rapidity of the proposed method.

A Simplified Thermal Model and Online Temperature Estimation Method of Permanent Magnet Synchronous Motors

Monitoring critical temperatures in permanent magnet synchronous motors is crucial for improving working reliability. Aiming at resolving the difficulty in online temperature estimation, an accurate and simple five-node lumped parameter thermal network (LPTN) is proposed and the mathematical model of the LPTN is built. Both radial and axial heat transfer paths inside the motor are considered to model the complete thermal circuit. In addition, an innovative parameter identification method based on multiple linear regression is applied to identify the parameters of the LPTN model. The parameters in the state equation are identified instead of the data of the motor, which are strongly dependent on the material and geometrical parameters. Finally, an open-loop estimation scheme based on the state equation and Kalman filter algorithm is adopted to predict the motor temperature online. The model performances are validated by extensive experiments under varying speed and torque conditions in terms of the accuracy and robustness. The results indicate that the temperature estimation error is within the range of ±5 °C in most cases and the proposed model can quickly follow the load variation. Besides, the online temperature estimation scheme and parameter identification method are easy and convenient to implement in an embedded system, which is feasible in automobile applications.

A Simplified Fractional Order Equivalent Circuit Model and Adaptive Online Parameter Identification Method for Lithium-Ion Batteries

In order to improve the battery management system performance and enhance the adaptability of the system, a fractional order equivalent circuit model of lithium-ion battery based on electrochemical test was established. The parameters of the fractional order equivalent circuit model are identified by the least squares parameter identification method. The least squares parameter identification method needs to rely on the harsh test conditions of the laboratory, and the parameter identification result is static; it cannot adapt to the characteristics of the lithium battery under dynamic conditions. Taking into account the dynamic changes of lithium batteries, a parameter adaptive online estimation algorithm for fractional equivalent circuit model is proposed. Based on the theory of fractional order calculus and indirect Lyapunov method, the stability and convergence of the estimator are analyzed. Finally, simulation experiments show that this method can continuously estimate the parameters of the fractional order equivalent circuit under UDDS conditions.

A Position-Based Friction Error Model and Its Application to Parameter Identification

Most existing methods rely on the torque information of a servo drive to predict the tracking error caused by the friction force (friction error), but the torque information is not available in certain systems. This paper proposes a position-based modeling method for the friction error. Two examples are given to verify the practicality of the proposed friction error model on the systems when torque information is not available. The experimental results show that the proposed method can predict the friction error accurately and the proposed online parameter identification method exhibits better performance. The proposed position-based friction error model can also be easily applied to commercial CNC systems because only the position information is required.

Evaluation of SOC Estimation Method Based on EKF/AEKF under Noise Interference

In this paper, the error of data acquisition in battery management system is considered. Combined with the online parameter identification method based on recursive least squares (RLS), the state of charge (SOC) estimation method based on extended Kalman filter (EKF) and adaptive extended Kalman filter (AEKF) is established and tested under the federal urban driving schedule. Then in the presence of noise interference on the input signal, the anti-interference of three methods is compared. Finally, it is concluded that the AEKF method with innovation-based adaptive estimator has the strongest suppression of noise with less than 2% SOC estimation error. It provides the research foundation for the application of EKF/AEKF in the actual battery management system.

A Novel Method for Lithium-Ion Battery Online Parameter Identification Based on Variable Forgetting Factor Recursive Least Squares

For model-based state of charge (SOC) estimation methods, the battery model parameters change with temperature, SOC, and so forth, causing the estimation error to increase. Constantly updating model parameters during battery operation, also known as online parameter identification, can effectively solve this problem. In this paper, a lithium-ion battery is modeled using the Thevenin model. A variable forgetting factor (VFF) strategy is introduced to improve forgetting factor recursive least squares (FFRLS) to variable forgetting factor recursive least squares (VFF-RLS). A novel method based on VFF-RLS for the online identification of the Thevenin model is proposed. Experiments verified that VFF-RLS gives more stable online parameter identification results than FFRLS. Combined with an unscented Kalman filter (UKF) algorithm, a joint algorithm named VFF-RLS-UKF is proposed for SOC estimation. In a variable-temperature environment, a battery SOC estimation experiment was performed using the joint algorithm. The average error of the SOC estimation was as low as 0.595% in some experiments. Experiments showed that VFF-RLS can effectively track the changes in model parameters. The joint algorithm improved the SOC estimation accuracy compared to the method with the fixed forgetting factor.

Real time adaptive efficient cold start strategy for proton exchange membrane fuel cells

Cold start of proton exchange membrane fuel cells (PEMFCs) at sub-zero temperatures is perceived as one of the obstacles in their commercialization way in automotive application. This paper proposes a novel internal-based adaptive strategy for the cold start of PEMFC to control its operating current in real time in a way to maximize the generated heat flux and electrical power in a short time span. In this respect, firstly, an online parameter identification method is integrated into a semi-empirical model to cope with the PEMFC performances drifts during cold start. Subsequently, an optimization algorithm is launched to find the best operating points from the updated model. Finally, the determined operating point, which is the current corresponding to the maximum power, is applied to PEMFC to achieve a rapid cold start. It should be noted that the utilization of adaptive filters has escaped the attention of previous PEMFC cold start studies. The ultimate results of the proposed strategy are experimentally validated and compared to the most commonly used cold start strategies based on Potentiostatic and Galvanostatic modes. The experimental outcomes of the comparative study indicate the striking superior performance of the proposed strategy in terms of heating time and energy requirement.

Two‐step method for the online parameter identification of a new simplified composite load model

The electrical load is one of the most important parts of power systems. Parameter identification of load model is critical for power system stability analysis. The composite load model, which comprises a static load and a dynamic load, is one of the most widely used models in power system simulation and control. As the load characteristics could change considerably over time, online parameter identification is needed. However, because of the non-linearity and complexity, online parameter identification for load models remains difficult to achieve, and there is rarely an effective solution. This study proposes a load model simplification and parameter identification method to solve this problem. First, the composite load model is preliminarily simplified by choosing the dominant parameters. Next, the load model is further simplified based on a second-ordered state equation of the induction motor. Subsequently, a two-step method for online parameter identification is presented. The first step is the electrical parameter identification based on the multi-layer searching method proposed in this study. The second step is the mechanical parameter identification via the Newton method. Finally, an example for a typical case system is presented to demonstrate the effectiveness, efficiency and accuracy of the two-step method.