On-line Identification Method Research Articles

Online Identification with Reliability Criterion and State of Charge Estimation Based on a Fuzzy Adaptive Extended Kalman Filter for Lithium-Ion Batteries

In the field of state of charge (SOC) estimation, the Kalman filter has been widely used for many years, although its performance strongly depends on the accuracy of the battery model as well as the noise covariance. The Kalman gain determines the confidence coefficient of the battery model by adjusting the weight of open circuit voltage (OCV) correction, and has a strong correlation with the measurement noise covariance (R). In this paper, the online identification method is applied to acquire the real model parameters under different operation conditions. A criterion based on the OCV error is proposed to evaluate the reliability of online parameters. Besides, the equivalent circuit model produces an intrinsic model error which is dependent on the load current, and the property that a high battery current or a large current change induces a large model error can be observed. Based on the above prior knowledge, a fuzzy model is established to compensate the model error through updating R. Combining the positive strategy (i.e., online identification) and negative strategy (i.e., fuzzy model), a more reliable and robust SOC estimation algorithm is proposed. The experiment results verify the proposed reliability criterion and SOC estimation method under various conditions for LiFePO4 batteries.

A changing forgetting factor RLS for online identification of nonlinear systems based on ELM–Hammerstein model

In this paper, an online identification method is proposed for nonlinear system identification based on extreme learning machine (ELM)–Hammerstein model. The ELM–Hammerstein model comprises an ELM neural network followed by a linear dynamic subsystem. This model is linear in parameters and nonlinear in the input. To speed up the convergence and meanwhile improve identification accuracy, a changing forgetting factor recursive least squares (CFF-RLS) method is proposed as online learning algorithm. The algorithm can identify the parameters of linear dynamic subsystem and the weights of ELM neural network simultaneously. Simulation results verify the effectiveness of the proposed method.

Online Identification of Tripped Line for Transient Stability Assessment

Identification of post-fault topology is critical to not only cascaded outage prevention but also prediction of post-fault dynamic behavior. Existing methods for identifying the tripped lines rely on steady state SCADA/PMU measurements, and therefore fail to accomplish their task within fractions of a second. This limits their application to online transient stability assessment. This paper presents a method for online identification of post-fault topology on the basis of information of the pre-fault operating conditions and synchronized measurements at all generator buses. The proposed technique identifies the tripped line with negligible delay, before the system reaches steady state. An innovative index, which represents the normalized kinetic energy gained by generators immediately after the fault, is used to minimize the search set of possible tripped lines. For every candidate line, the generator output powers are calculated considering the line as tripped, and the minimum error between the measured and the calculated generator powers is used to finally identify the tripped line. The IEEE 39-bus system and a 246-bus North Indian Grid representation are used to show the effectiveness of the method.

A New Electrode Regulator System Identification of Arc Furnace Based on Time-Variant Nonlinear-Linear-Nonlinear Model

<p>In this paper, we express arc furnace electrode regulator system as a time-variant nonlinear-linear-nonlinear model. On this basis, we propose an online identification method based on nonlinear-linear-nonlinear model system. This new scheme solves the problem of model variation and prediction precision decline causing by time-varying of arc characteristic. In order to dispose the difficulty of parameters separation in the online identification process, this new method adopts the mind of update the parameters of linear parts and nonlinear parts respectively. It realizes the parameters separation of system effectively. Simulation results show that this method can track the changes of arc characteristics effectively. That it achieves the aim of real-time monitoring and controlling system parameters.</p>

油圧アームのオンライン物理パラメータ同定

This paper discusses an on-line parameter identification for hydraulic arms. First, the state-space equation of hydraulic arms is converted into a linear equation with respect to unknown physical parameters such as the bulk modulus and the flow gain. Second, a new on-line parameter identification method for hydraulic arms is proposed based on the linear equation. Third, the validity of the proposed method is confirmed by simulation using a nonlinear model. Finally, the validity of the proposed method is confirmed by experiment using an actual arm. The identified values by the proposed method converge to those by the previous off-line method and the setting times in our laboratory scale are less than 10 seconds.

Online Identification of a Two-Mass System in Frequency Domain using a Kalman Filter

Some of the most widely recognized online parameter estimation techniques used in different servomechanism are the extended Kalman filter (EKF) and recursive least squares (RLS) methods. Without loss of generality, these methods are based on a prior knowledge of the model structure of the system to be identified, and thus, they can be regarded as parametric identification methods. This paper proposes an on-line non-parametric frequency response identification routine that is based on a fixed-coefficient Kalman filter, which is configured to perform like a Fourier transform. The approach exploits the knowledge of the excitation signal by updating the Kalman filter gains with the known time-varying frequency of chirp signal. The experimental results demonstrate the effectiveness of the proposed online identification method to estimate a non-parametric model of the closed loop controlled servomechanism in a selected band of frequencies.

A Security Assessment on the Designated PC service

In this paper, we draw a security assessment by analyzing possible vulnerabilities of the designated PC service which is supposed for strengthening security of current online identification methods that provide various areas such as the online banking and a game and so on. There is a difference between the designated PC service and online identification methods. Online identification methods authenticate an user by the user's private information or the user's knowledge-based information, though the designated PC service authenticates a hardware-based unique information of the user's PC. For this reason, high task significance services employ with online identification methods and the designated PC service for improving security multiply. Nevertheless, the security assessment of the designated PC service has been absent and possible vulnerabilities of the designated PC service are counterfeiter and falsification when the hardware-based unique-information is extracted on the user's PC and sent an authentication server. Therefore, in this paper, we analyze possible vulnerabilities of the designated PC service and draw the security assessment.

A Current-Sensorless Online ESR and <italic>C</italic> Identification Method for Output Capacitor of Buck Converter

As electrolytic capacitor is apt to fail in power circuits, it is very important to identify its electrical parameters, mainly the equivalent series resistance (ESR) and capacitance ( C ). A noninvasive online identification method of capacitor's ESR and C for continuous-conduction-mode (CCM) buck converter is proposed in this paper. Based on the ac component of capacitor voltage, the calculation model is founded. By sampling the pulse width modulation signal and output voltage, the method needs no current sensor and is effective for the CCM buck converter operating at different conditions. Implementation of the identification system is presented. The tests are carried out for different aged capacitors and ambient temperatures. The experimental results validate the effectiveness of the method.

ANN Based Inverse Dynamic Model of the 6-PGK Parallel Robot Manipulator

<p>This paper presents an inverse dynamic model estimation based on an artificial neural network of a complete new parallel robot manipulator prototype 6- PGK with six degrees of freedom, built at Petru Maior University of Tirgu-Mures. The model estimation of the parallel robot manipulator is performed with a feedforward artificial neural network. In the control engineering domain there are control structures that need the direct or inverse model of the process for ensuring the process control at the imposed performances. Usually, the determination of the direct/inverse mathematical model is a difficult or impossible task to be achieved. In these cases different non-parametric or parametric, off-line or on-line identification methods are used. A solution that may support the on-line parametric methods is represented by the feedforward artificial neural networks. By implementing feedforward artificial neural networks as a nonlinear autoregressive model with exogenous inputs, the authors investigate the possibility of choosing the optimum parameters that characterize the neural network so that it approximates as better as possible the model of the 6-PGK prototype robot. Finally an innovative algorithm is developed for obtaining the optimal configuration parameters set of the feedforward artificial neural network. The proposed algorithm helps in setting the optimal parameters of the neural network that offer high opportunities to provide satisfactory identification of the robot model. Experimental results obtained by a structure derived from the proposed solution demonstrate a good approximation related to the studied system, which is characterized by nonlinearities and high complexity.</p>

An optimal differential pressure reset strategy based on the most unfavorable thermodynamic loop on-line identification for a variable water flow air conditioning system

The variable differential pressure (DP) set-point control strategy has become the main energy-saving measure for central air conditioning water system variable volume adjustment. In optimal operation control, it is critical to use the right reference object to implement global, accurate, and online control. In mainland China, the most unfavorable loop is typically used as the reference object for variable DP set-point control in the majority of cases of variable volume air conditioning system adjustment. However, its concept and feasibility require further clarification. First, this paper analyzes the difference in the characteristics of a most unfavorable loop under air conditioning design work conditions versus under operational conditions. Under design operational conditions, the referenced most unfavorable hydraulic loop only reflects the distribution of the pipe network's hydraulic characteristics, whereas under system operational conditions, the variable volume adjustment strategy should be chosen according to each user's load requirement, i.e., a most unfavorable thermodynamic loop. Then, this paper provides definitions of the most unfavorable thermodynamic loop and online identification method and proposes a variable DP set-point optimal reset strategy based on the maximum valve position loop's optimal reset valve position domain and best valve position domain. Finally, the variable DP set-point control of a primary pump variable volume air conditioning water system in an industrial factory is used as an example to conduct a test studying 6 variable DP or constant DP control strategies, including the method proposed in this paper. The test results show that the most unfavorable loop-based variable DP control strategy saves 47–58% water pump power consumption (up to 985kWh electric power consumption per day) when compared with the conventional variable DP control strategy, whereas the most unfavorable thermodynamic loop-based variable DP control strategy is more advantageous in ensuring the overall cooling energy supply for each user.

Indirect sliding mode control based on gray-box identification method for pneumatic artificial muscle

We present an indirect robust nonlinear controller for position-tracking control of a pneumatic artificial muscle (PAMs) testing system. The system modeling is reviewed, for which the existence of uncertain, unknown, and nonlinear terms in the internal dynamics is presented. From the obtained results, an online identification method is proposed for estimation of the internal functions with learning rules designed via a Lyapunov derivative function. A robust nonlinear controller is then designed based on the approximated functions to satisfy the control objective under the sliding mode technique. Appropriate selection of the smooth robust gain and the sliding surface ensures convergence of the tracking error to a desired level of performance. Stability of the closed-loop system is proven through another Lyapunov function. The proposed approach is verified and compared with a conventional proportional–integral–differential (PID) controller, adaptive recurrent neural network (ARNN) controller, and robust nonlinear controller in a real-time system with three different kinds of trajectories and loading. From the comparative experimental results, the effectiveness of the proposed method is confirmed with respect to transient response, steady-state behavior, and loading effect.

PID Control of a Lab Scale Single-Rotor Helicopter System using a Multicore Microcontroller

In this paper, a lab-scale single-rotor helicopter system is modeled and controlled. The introduced system is first modeled mathematically and then modeled with an online identification method using MATLAB. The model is then used to design a PID controller for the system. The designed controller is then implemented using a multicore microcontroller. The control task is one of the tasks in the introduced control software where the role of the microcontroller is to execute it along with many different tasks like interfacing sensors and actuators, tuning control parameters, data filtering and logging. The implemented control system uses a multicore microcontroller to execute all tasks simultaneously and hence improves performance and functionality. It is also demonstrated that using multicore microcontrollers can reduce design-time, implementation-time and cost while keeping higher performance rates. This contribution shows that it is possible to design and implement complex real-time embedded control systems that employ advanced control algorithms using multicore microcontrollers.

A novel method for fault diagnosis of hydro generator based on NOFRFs

Fault diagnosis and recognition of hydro generator are important issues which encounter in repair and security posture assessment. It influences the operational planning and security directly. Volterra series as an effective modeling method has been widely used in modeling and fault diagnosis of hydro generator, but the larger number of kernels limits its application in faults diagnosis. Non-linear output frequency response functions (NOFRFs) as a transformation style of Volterra series have a more intuitive visual and simple structure. In this paper, NOFRFs have been proposed to be employed in fault diagnosis of hydro generator, and a novel online identification method is proposed at the same time. Firstly, NOFRFs models of hydro generators are built. Secondly, a new method for online identification is proposed according to the operational characteristics of hydro generators. Finally, simulation verification has been done to demonstrate the advantage of the proposed method, and experimental studies are put forward on a hydro generator to analyze the failure mechanism in different states. The results indicate that the proposed method is useful and concise for fault diagnosis and nonlinear analysis of hydro generators.

On-line Inertia Identification Algorithm for PI Parameters Optimization in Speed Loop

This paper presents a novel on-line inertia identification method with a load torque observer to optimize the speed loop PID parameters of a servo system. The proposed inertia identification algorithm in this paper adopts the fixed-order recursive empirical frequency-domain optimal parameter estimation to improve the speed loop performance. A load torque observer is employed in order to obtain a more precise value of inertia. Then, the method of speed loop PI parameters optimization with the identified inertia and load torque is deduced in frequency domain. Compared with the recursive least square algorithm, the effectiveness of the proposed method is demonstrated by the simulation and experimental results.

Experimental Study on a Novel Optimal Differential Pressure Reset Method for Online Application in Chilled Water System

This paper firstly define most unfavorable thermodynamic loop to reflect the dynamic situation of terminal cooling load distribution, then present its online identification method that can easily applied for optimal reset of differential pressure. We use the optimal domain of maximum valve position among whole braches in chilled water system to calculate the variation of differential pressure setpoint, and control the pumps according to the optimal setpoint. We applied presented method to a VWF retrofit project in a Suzhou factory building and evaluate its energy saving effect through its equipped building energy management system. Experimental results showed that presented method can achieve 22%-53% pump power consumption savings compared to traditional control method. During the test period, the average level of valve position of presented method was highest among all tested control strategies. The optimal reset method of differential pressure presented in this paper is an energy efficient, high stability and easily online applied control method.

An Isolated Industrial Power System Driven by Wind-Coal Power for Aluminum Productions: A Case Study of Frequency Control

Due to the uneven geographical distribution between load and wind power, currently it is difficult to integrate the large-scale wind power into the State Grid in China. There are significant wind curtailments for wind farms due to system restrictions. This paper proposes a new way of utilizing wind power, i.e., in-situ consumption, as an alternative to the costly development of long-distance transmission of large-scale wind power. Electrolytic aluminum load, which is one of the most typical high energy consuming loads, is employed to absorb the excess wind power in western China. An actual isolated industrial power system for aluminum production driven by wind power and coal-fired power is described. The penetration level of wind power in the isolated power system is up to 48.8%. The power imbalance between generation and load demand caused by wind power fluctuation or the tripping of coal-fired generators can dramatically impact the frequency stability of the isolated power system, which is of small inertia. An online identification method of power imbalance based on Wide Area Measurement System (WAMS) is presented. According to the characteristics of the electrolytic aluminum load, a system frequency control method by regulating the bus voltages of aluminum loads to eliminate the power imbalance is introduced. The simulation is done in Real Time Digital Simulator (RTDS) and the results verify the validity of the proposed frequency control method.

On the algebraic parameter identification of vibrating mechanical systems

This paper proposes an on-line algebraic parameter identification method in time domain for multiple degrees-of-freedom linear mass–spring–damper mechanical systems using position measurements. The constant parameters of mass, stiffness and damping are algebraically estimated from transient real-time measurements. Analytical and numerical results prove the accuracy and efficiency of the proposed identification approach for reference trajectory tracking control tasks on multiple-input–multiple-output vibrating mechanical systems with unknown constant parameters.

Hybrid kernel identification method based on support vector regression and regularisation network algorithms

This study proposes a new kernel method for online identification of a non-linear system modelled on reproducing kernel Hilbert space (RKHS). The proposed method is a concatenation of two techniques proposed in the literature, the support vector regression and the Regularisation Networks (RNs). The proposed algorithm, called the online SVR-RN kernel method, uses first the SVR in an offline phase to construct an RKHS model with a reduced parameter number and second the RN method in an online phase to update the model parameters. The proposed algorithm has been tested to identify the chemical Tennessee Eastman Process and the electronic non-linear system with a Wiener Hammerstein structure.

Online adaptive parameter identification of PMSM based on the dead-time compensation

This paper presents a method of online identification of the parameters of permanent magnet synchronous motors (PMSM) by model reference adaptive identification based on Popov Super Stability Theory. Firstly, the relations of parameters in the Field Orientation Control (FOC) system are analysed. Secondly, the proposed identification method of PMSM concerns two parts. In the case of high-speed operation of the motor, the method can accurately identify the inductance in dq-axis and the permanent magnet (PM) flux linkage. On the other hand, in the case of low speed, it can accurately identify the winding resistance of the stator. The method does not require additional excitation signals, but only makes use of motor voltage, current and their deviations. Thirdly, a simple and effective dead-time compensation method has been applied to inhibit the dead-time effects on the parameter identification. At last, the simulation and experiment results clearly demonstrate the validity and feasibility of the method.

A new approach for online T-S fuzzy identification and model predictive control of nonlinear systems

This paper proposes a new unsupervised fuzzy clustering algorithm (NUFCA) to construct a novel online evolving Takagi–Sugeno (T-S) fuzzy model identification method and an adaptive predictive process control methodology. The proposed system identification approach consists of two main steps: antecedent T-S fuzzy model parameters identification and consequent parameters identification. The NUFCA combines the K-nearest neighbour and fuzzy C-means methods into a fuzzy modelling method for partitioning of the input–output data and identifying the antecedent parameters of the fuzzy system; then the recursive least squares method is exploited to obtain initialization type consequent parameters and to construct a method for on-line fuzzy model identification. The integration of the proposed adaptive identification method with the generalized predictive control results in an effective adaptive predictive fuzzy control methodology. For better demonstration of the robustness and efficiency of the proposed methodology, it is applied to the identification of a model for the estimation of the flour concentration in the effluent of a real-world wastewater treatment plant (WWTP); and to control a simulated continuous stirred tank reactor (CSTR), and a real experimental setup composed of two coupled DC motors. The results show that the developed evolving T-S fuzzy model methodology can identify nonlinear systems satisfactorily and can be successfully used for a prediction model of the process for the generalized predictive controller. It is also shown that the algorithm is robust to changes in the initial parameters, and to unexpected disturbances.