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This research involves a 4-DOF ship maneuvering modeling with full-scale trial data. In order to avert the inversion of the multi-innovation matrix in the traditional multi-innovation least squares algorithm, a new novel is proposed based on the recognition concept new multi-innovation least squares algorithm to identify the innovation of the stochastic gradient hyperbolic tangent nonlinearity. A lot of work and efforts have been made to ensure the consistency and final convergence. Combined with relevant data and statistical indicators, the author derives a more effective hyperbolic tangent nonlinear innovation identification scheme to identify ship maneuvering motion. Compared with the previous results, this design scheme has significant computational advantages, with higher accuracy, faster identification speed, higher computational efficiency, and requiring fewer parameters. At the same time, the example is given to illustrate the effectiveness of the algorithm, especially for identification applications with full-scale trial data.

This article proposes a precise identification scheme of an interior permanent-magnet synchronous motor (IPMSM) flux linkage through experimental tests considering motor nonlinearities, such as the magnetic saturation and spatial harmonics. In the proposed scheme, the flux linkage is identified based on the voltage equation of IPMSM. The fundamental and harmonic electromotive force in the rotor reference frame is obtained in real time by applying a discrete Fourier transform with the phase compensation of the time delay due to digital control and pulsewidth modulation. The effect of the inverter nonlinearity is compensated using an experimentally obtained lookup table. Based on the identified flux-linkage map, a flux-based IPMSM model that accurately simulates motor behavior can be constructed. The effectiveness of the proposed flux-linkage identification scheme is verified through the comparison of simulation and experimental results.

For high-voltage direct current (HVDC) transmission systems that use overhead transmission lines, the lightning disturbance is the main reason for the line protection mal-operation. To avoid the impact of lightning disturbance on the safety and stability of DC line protection, the model of HVDC transmission system including lightning strokes is constructed. On this basis, the complementary ensemble empirical mode decomposition (CEEMD) and Hilbert transform are combined as a time-frequency analysis method to calculate the energy distribution of the signals. Then the low-to-high-frequency energy ratio of the 1-mode voltage signal in a 3 ms data window after the protection startup is used as the protection criterion for identifying lightning disturbance, and that of the current signal in 1 ms is used as the criterion to further distinguish lightning and non-lightning faults. Verified through simulation tests, the scheme appears good adaptability and accuracy in different situations.

Inverter-Based Resources (IBRs), including Wind turbine generators (WTGs), exhibit substantially different negative-sequence fault current characteristics compared to synchronous generators (SGs). These differences may cause misoperation of customary negative-sequence-based protective elements set under the assumption of a conventional SG dominated power system. The amplitude of the negative-sequence fault current of a WTG is smaller than that of an SG. This may lead to misoperation of the negative-sequence overcurrent elements 50Q/51Q. Moreover, the angular relation of the negative-sequence current and voltage is different under WTGs, which may result in the misoperation of directional negative-sequence overcurrent element 67Q. This paper first studies the key differences between the WTGs and SG by comparing their equivalent negative-sequence impedances with SG's. Then, simulation case studies are presented showing the misoperation of 50Q and 67Q due to wind generation and the corresponding impact on communication-assisted protection and fault identification scheme (FID). The impact on directional element is also experimentally validated in a hardware-in-the-loop real-time simulation set up using a physical relay. Finally, the paper studies the impact of various factors such as WTG type (Type-III/Type-IV) and Type-IV WTG control scheme (coupled/decoupled sequence) to determine the key features that need to be considered in practical protection studies. The objective is to show potential protection misoperation issues, identify the cause, and propose potential solutions.

The grid-connected converter (GCC) is widely used as the interface between various distributed generations and the utility grid. To achieve precise power control for GCC, this paper presents a model predictive direct power control (MPDPC) with consideration of the unbalanced filter inductance and grid conditions. First, the characteristics of GCC with unbalanced filter inductance are analyzed and a modified voltage control function is derived. On this basis, to compensate for the power oscillation caused by unbalanced filter inductance, a novel power compensation method is proposed for MPDPC to eliminate the DC-side current ripple while maintaining sinusoidal grid current. Besides, to improve the control robustness against mismatched filter inductance, a filter inductance identification scheme is proposed. Through this scheme, the estimated value of filter inductance is updated in each control period and applied in the proposed MPDPC. Finally, simulation results in PSCAD/EMTDC confirm the validity of the proposed MPDPC and the filter inductance identification scheme.

This paper presents an approach to identify the off-diagonal elements of the added mass matrix of a fully actuated marine vessel based on a set of experiments. The off-diagonal elements of the mass matrix characterize the coupled acceleration response of the vessel and are therefore required for the intended design of a control system. The introduced identification scheme utilizes the equivalence between the mass matrix and the coupled acceleration response. The process is extended by a compensation that attempts to match the coupling. A few acceleration experiments are performed with the vessel, applying different compensations for testing. Based on the tested compensations and the measured acceleration responses, the exact compensation and the mass matrix can be reconstructed finally. The exact compensation is sometimes desired itself, rather than the off-diagonal elements of the mass matrix. It makes the coupled process appear as separate SISO processes, which is convenient for controller design.

In this paper, a new identification and control scheme for the flexible joint robotic manipulator is proposed. Firstly, by defining some new state variables, the commonly used dynamic equations of the flexible joint robotic manipulators are transformed into the standard form of a singularly perturbed model. Subsequently, an optimal bounded ellipsoid algorithm based identification scheme using multi-time-scale neural network is proposed to identify the unknown system dynamic equations. Lastly, by using the singular perturbation theory, an indirect adaptive controller based on the identified model is proposed to control the system such that the joint angles can track the given reference signals. The closed-loop stability of the whole system is proved, and the effectiveness of the proposed schemes is verified by simulations.

This brief presents a symmetrical relay-based identification scheme for a class of linear time-invariant systems with time delay. During off-line identification, the adverse effect of static load disturbance may lead to erroneous system model parameters. Therefore, on-line identification scheme is proposed where the system characteristics is extracted with respect to proportional-integral (PI) controller dynamics in terms of sustained oscillatory responses broadly known as limit cycle at the system output. Utilizing the relay settings (amplitude and hysteresis width), controller parameters and limit cycle information, an explicit set of mathematical expressions for on-line parameter estimation of system dynamics in terms of a stable first order plus time delay (FOPTD) model is deduced from frequency domain and state space analyses. As compared to recent relay-based identification methods, the proposed set of explicit expressions does not require any prior guess of initial values of the system model parameters for the simultaneous solution of a nonlinear set of mathematical equations. Simulation of a benchmark example from literature and experimental results from the coupled tanks system are included for validation of the proposed identification algorithms using the deduced set of mathematical expressions.

The parameter identification of a nonlinear Hammerstein-type process is likely to be complex and challenging due to the existence of significant nonlinearity at the input side. In this paper, a new parameter identification strategy for a block-oriented Hammerstein process is proposed using the Haar wavelet operational matrix &#x0028 HWOM &#x0029 . To determine all the parameters in the Hammerstein model, a special input excitation is utilized to separate the identification problem of the linear subsystem from the complete nonlinear process. During the first test period, a simple step response data is utilized to estimate the linear subsystem dynamics. Then, the overall system response to sinusoidal input is used to estimate nonlinearity in the process. A single-pole fractional order transfer function with time delay is used to model the linear subsystem. In order to reduce the mathematical complexity resulting from the fractional derivatives of signals, a HWOM based algebraic approach is developed. The proposed method is proven to be simple and robust in the presence of measurement noises. The numerical study illustrates the efficiency of the proposed modeling technique through four different nonlinear processes and results are compared with existing methods.

This research work is motivated by the success of relay feedback in linear system domain thereby an attempt has been made at extending it to nonlinear Hammerstein models. The identification is carried out using a relay with hysteresis in feedback, the parameter estimation is done solely by solving state-space equations at various points on one cycle of the input and output signals. The work considers not all but wider probable cases of the linear subsystem-real roots, complex conjugate roots, first order plus dead time systems, integrating second order plus dead time and repeated roots. The proposed method is simple compared to other works in literature as well as applicable to unstable systems. Simulations of various examples are carried out to demonstrate the efficacy of the proposed identification scheme.

Most identification algorithms do not consider the prescribed error bound on the parameter estimation error information, which may result in the poor transient performance of parameter estimation during the identification process. In this study, a novel identification scheme is presented for the extended Wiener–Hammerstein systems with backlash non-linearity, which is implemented by using prescribed performance function and error transformation technique. To improve the transient performance of parameter estimation, the prescribed performance function is designed to prescribe estimation error bound. Then, the error transformation technique is applied to transform the prescribed estimation error problem into an equivalent generalised error problem, which can simplify the design of identification algorithm. By developing the parameter updating law, the convergence of generalised error problem can be guaranteed, and the parameter estimation of considered system can be achieved. The numerical example and experiment results validate that the proposed scheme can provide more accurate parameter estimation and better transient performance than the available identification approaches.

In this article, we propose a fault directional identification scheme that depends on the positive-sequence components of current based on an investigation of pre-fault and fault phase angles. The proposed protection scheme is developed for both distribution and transmission systems. However, as the current-only approach depends on the pre-fault current direction as the polarizing quantity, if the pre-fault current direction changes during normal conditions, the direction definitions would be swapped. This will cause a problem for the current-only approach. Therefore, a signal processing-based solution is presented, leveraging the fact that a change in direction in the pre-fault condition is associated with a phase angle change of 180°. The proposed protection scheme is examined with two different power system configurations (distribution and transmission systems). Simulation experiments conducted using ATP/EMTP software verified the feasibility of the proposed protection scheme. Moreover, the validity of the proposed protection scheme is compared with that of other directional schemes. The results confirmed the accurate operation of the proposed scheme.

This paper introduces an integrated distance protection technique for series-compensated transmission lines (SCTLs) based on a new scheme to identify the faulty side with respect to the compensator. Conventional distance algorithms suffer from mal-operation under faults behind the Series Compensator (SC). Therefore, the modified mathematical algorithm of the distance relay treats with these faults via compensating the voltage drop through the SC to integrate the relay performance along the line. Consequently, the faulty side either upstream or downstream the SC must be identified. This identification is accomplished via a new function inserted in the mathematical algorithm. The presented function is a voting system based on both of the angles of the obtained conventional and modified apparent impedances. The presented voting scheme is based on basics theories of the fault analysis and the phasors diagrams of the equivalent apparent impedances seen by the conventional and modified algorithms whether the fault is upstream or downstream the SC. The presented scheme is suitable for all fault types even with high fault resistances. MATLAB package is used to simulate 400 kV compensated transmission lines and also to implement the presented algorithm. Diverse fault cases are investigated to test the response of the presented voting system under different fault conditions including the fault type, location, and impedance. The results confirm the efficacy of the presented scheme.

In terms of outdoor flight experimental data, the paper investigates the indirect identification of a generalized attitude model (GAM) for a controlled quadrotor. The GAM is comprised of an attitude model of the quadrotor and the dynamics of the actuator involved in the attitude control loop. Being collected from outdoor, the flight data are undoubtedly corrupted by large-magnitude random wind and other unknown disturbances. Another challenge is from an existing unknown attitude controller. In such case, an indirect identification scheme is proposed to estimate the quadrotor GAM. The correlation analysis is first applied to reduce the disturbance impacts in the flight data as greatly as possible. A pair of non-coprime factors of the GAM can then be estimated from the available correlation processing experimental data. An extended Hankel model reduction method is studied to cancel the same unstable zeros and poles in the non-coprime factors for obtaining their corresponding coprime factor pair, from which the GAM is readily estimated. Finally, model validations are given to show the accuracy of the estimated GAM.

The estimation of SOC of lithium battery is an important index for the safety and reliability of battery energy storage system. So it’s very important to choose the appropriate circuit model. At present, the most extensive model is the second-order Thevenin equivalent model. But the parameters in this model are nonlinear, which brings difficulties to parameter identification. Aiming at this problem, an improved fireworks algorithm (LAFWA) is proposed in this paper. In the improved fireworks algorithm, an adaptive inertia weight based on the rate of cluster focus distance changing is introduced to improve the search and optimization ability of explosive sparks. To solve the problem of Gaussian mutation which can not jump out of the local optimization problem, this paper uses the Levy flight variation method, which has a larger variation range. The proposed algorithm is used to identify the parameters of lithium battery model. Based on the data collected by the pulse charging and discharging experiment, the identification result is obtained by exploiting the LAFWA. The error between the fitting results and the experimental data is within a certain range, which indicates that the identification scheme is accurate and effective.

The accurate electrical parameters of PMLSM is important for precision control. This paper studied the electrical parameter identification, and discussed the method that injects “DC+AC” voltage to identify the d-q axis incremental inductance. In order to improve the identification accuracy, the injected signal characteristics and inverter nonlinearity were considered. Finally, the resistance and d-q axis incremental inductance were identified, and the q-axis identification experiments were implemented in different current values. Through Piecewise Cubic Hermite interpolation, a smooth q-axis current-incremental inductance curve was achieved. The effectiveness of the proposed identification scheme was confirmed on an air-bearing PMLSM.

One of the critical components in the satellite attitude control subsystem is the control moment gyroscope. If it fails, the satellite cannot finish its mission. Fault isolation followed by in-time corrective action can help prevent failures. However, it is necessary to know the fault severity for better maintenance planning and prioritize the corrective actions. This way, the more severe faults can be corrected first. Therefore, a data-driven fault identification scheme is proposed in this paper, adopting an optimized support vector regressor to determine the severity of multiple in-phase faults of the satellite control moment gyroscopes. The features are extracted using correlation analysis. A grid search is used to optimize the model's hyperparameters, and the R2-score is adopted to evaluate the model performance. It is shown that the proposed scheme can predict the fault severities with 94.9% accuracy, on average.

The document search function in the quality assurance unit's information system uses the Knuth-Morris-Pratt algorithm. This KMP algorithm is a search algorithm that will match the pattern or arrangement of words to be searched from left to right at the beginning of the text and then shift the order of words until the word order is at the end of the text. The KMP algorithm has the advantage of matching matches on large files. The system development method used is the Rapid Application Development method. This RAD method is a life cycle strategy aimed at providing development that is much faster and gets results with better quality compared to results achieved through traditional cycles. Application of School Administration Information System is expected to be able to overcome problems arising from conventional document control processes to make it easier and more precise. Abstrak: Fungsi pencarian dokumen pada sistem informasi unit jaminan mutu menggunakan algoritma Knuth-Morris-Pratt. Algoritma KMP ini merupakan algoritma pencarian yang akan mencocokkan pattern atau susunan kata yang akan dicari dari kiri ke kanan pada awal teks dan kemudian menggeser susunan kata sampai susunan kata tersebut berada di ujung teks. Algoritma KMP memiliki keunggulan pencarian kecocokan pada file yang berukuran besar. Metode pengembangan sistem yang digunakan yaitu metode Rapid Application Development. Metode RAD ini adalah strategi siklus hidup yang ditujukan untuk menyediakan pengembangan yang jauh lebih cepat dan mendapatkan hasil dengan kualitas yang lebih baik dibandingkan dengan hasil yang dicapai melalui siklus tradisional. Aplikasi Sistem Informasi Administrasi Sekolah ini diharapkan dapat mengatasi permasalahan yang timbul dari proses pengendalian dokumen secara konvensional agar lebih mudah dan tepat.

This study presents the frequency-domain and state-space approaches for modelling and identification of processes exhibiting non-minimum phase and time delay characteristics. An asymmetrical relay is fed back to unknown process to bring sustained oscillatory responses or limit cycle at the process output. Utilising the relay settings and limit cycle information, a set of mathematical expressions for the identification of process dynamics in terms of non-minimum phase second-order plus time delay, non-minimum phase first-order plus time delay, non-minimum phase integrating first-order plus time delay models is deduced. As compared to the methods reported in recent literature, the proposed set of explicit expressions is completely free from the simultaneous solution of a set of non-linear equations and does not need any prior guess for initial value of the unknown process model parameters. Simulations of benchmark examples from literature and experimental results from coupled tanks system are included to demonstrate the efficacy of the proposed relay-based identification algorithms.

In this paper, a parameter identification method by randomly excited trajectories for decentralized joints of robot manipulators is presented. Each joint of a robot manipulator is decoupled and identified as a second order linear equation by a recursive least square method. Although robot manipulators are a nonlinear and coupled system, decentralized models are required for either the independent joint control such as model-based linear control methods, a time-delayed control (TDC) method or the input torque estimation. The random walk-based parameter identification scheme of using a recursive least square (RLS) method is applied to a mobile manipulator, KOBOKER as a test-bed. Then the identified models are used for designing a state observer to estimate the states of KOBOKER more accurately when the robot follows the sinusoidal trajectory. The accuracies of the identified model and the estimated state are verified experimentally by comparing with the torque of a linearized motion equation.

The non-parametric identification problem aims to estimate a suitable model based on the response produced by a given stimulus on an uncertain model. Complementary, input estimation considers a different problem where the model and the output are known. However, if neither model nor input is known, the identification problem seems to be more complicated. This is a major challenge in electrophysiological systems where the output signal can be measured, but the biological system is uncertain and the stimuli are unknown. The aim of this study was to introduce a novel methodology that may estimate the uncertain input in this more complex situation where the biological model of the system under analysis is uncertain and the input stimuli must be estimated accurately. A two step non-parametric identification scheme based on differential neural networks (DifNN) and the second order super-twisting sliding mode algorithm (STA) identified the input stimulus. The STA makes a robust exact estimation of the output signal. The DifNN produces an internal non-parametric mathematical modeling of the input-output relationship executed without the input information. Learning laws for both levels of DifNN modeling were presented as part of the uncertain input identification process. The estimation of uncertain visual stimulus applied over the retina in an ex-vivo avian model (EVAM) served to test the method proposed in this study. The output information corresponded to the electrophysiological voltage variation in the optical nerve.

Due to the high rate changes in the handling of cars, the use of an auxiliary identification process to design efficient controllers is of importance. Many identification algorithms have been proposed in the literature, which generally performs well under normal situations, but does not show acceptable performance in uncertain conditions. In this article, due to the nature of the neuro-fuzzy networks in identifying and predicting uncertain conditions, an adaptive neuro-fuzzy identification algorithm is proposed to steer vehicles at the uncertain slippery condition of roads. A set of data for three well-known manoeuvres of vehicle dynamics at conventional conditions was collected to train the algorithm using adaptive neuro-fuzzy inference system of MATLAB. Using back propagation of error as the learning algorithm, the parameters of the algorithm were modified regarding uncertain conditions. Making an analogy, the performance of the proposed identification scheme was compared to the untrained fuzzy one. In regular situations, the results were almost identical, but in uncertain ones such as slippery roads, the performance of the proposed neuro-fuzzy algorithm was much better.

Monitoring the condition of suspension systems is significant to ensure the safe operation of modern railway vehicles. For this purpose, an online modal identification scheme, denoted as Correlatio...

This paper proposes a diagonal recurrent neural network (DRNN) based identification scheme to handle the complexity and nonlinearity of high-power continuous microwave heating system (HPCMHS). The new DRNN design involves a two-stage training process that couples an efficient forward model selection technique with gradient-based optimization. In the first stage, an impact recurrent network structure is obtained by a fast recursive algorithm in a stepwise forward procedure. To ensure stability, update rules are further developed using Lyapunov stability criterion to tune parameters of reduced size model at the second stage. The proposed approach is tested with an experimental regression problem and a practical HPCMHS identification, and the results are compared with four typical network models. The results show that the new design demonstrates improved accuracy and model compactness with reduced computational complexity over the existing methods.

This work presents a closed-loop parameter identification algorithm for a class of second-order non-linear systems affected by constant disturbances, quantisation, and state estimation errors. The proposed scheme permits obtaining a linear parameterisation of the non-linear system by developing a simplified procedure that allows using the distributional framework approach straightforwardly. The parametrisation stage requires signals with known delays. These delays are introduced to the system through the reference signal. Then, the linear parametrisation is used by a least-squares (LS) algorithm and a state estimator to generate the estimated values of the system parameters and the constant disturbance. The proposed algorithm is compared to a standard off-line LS algorithm in numerical simulations. Besides, the effectiveness and robustness of the proposed methodology are verified using a Monte Carlo simulation by considering that the system's output is corrupted by white noise. The results indicate that the proposed parameter identification scheme outperforms the LS algorithm, but without requiring any pre-processing stage.

Controlled islanding often acts as the last resort against a severe blackout. Generator coherency is the primary constraint to determine an effective controlled islanding strategy. This paper proposes a scheme to identify the dynamic coherency of generators for controlled islanding. The generator coherency is identified based on the Phase-plane Trajectory Vectors (PTVs) on the Phase Plane for Generators (PPG). Then a Phase Plane for Buses (PPB) is proposed to assign the non-generator buses to coherent generator groups following the minimum distance principle. The separated islands are formed by disconnecting certain transmission lines according to the identified coherent generators and areas. The case studies in IEEE 39-bus 10-machine power system show that the proposed scheme can not only adapt to different disturbances and changes of system conditions and network topology but also succeed in identifying the generator coherency at different times and developing proper islanding strategy according to current system states.

The paper presents a relay feedback experiment for the generation of sustained oscillations or limit cycle in a class of time delay single-input single-output (SISO) processes. Based on limit cycle information, a generalized set of explicit expressions is deduced for modelling and identification of unknown processes in terms of first order plus time delay (FOPTD), second order plus time delay (SOPTD) models with or without zero. As compared to recent relay based identification methods, the proposed scheme does not require any prior guess for the initial conditions for a set of simultaneous nonlinear equations. Thereafter, the robustness of the proposed explicit expressions is dependent on the limit cycle parameters shown through the addition of random signals at the process output. Validation of the proposed identification scheme is performed through simulations for well-known examples in literature and experimental tests on a level control system. Finally, the efficacy of the proposed scheme is compared with potential results available in literature through appropriate performance metric and step response plots.

Aiming at the rank-deficient issue of the full-parameter identification model of synchronous reluctance motor (SynRM), an online full-parameter identification scheme combined with the reclusive least square (RLS) algorithm and the least mean square (LMS) algorithm is proposed in this paper. Due to the characteristics that the inductance parameters vary rapidly with the currents, while the stator resistance varies slowly with the winding temperature, the RLS algorithm with a faster convergence rate is proposed to identify the inductance parameters quickly, and based on the RLS identification results, the LMS algorithm with less calculation is used to update the stator resistance. This method avoids the effect of using fixed parameters to identify the other parameters, and it can follow the changes in motor parameters well. Finally, the simulation results in the MATLAB/Simulink verify the effectiveness of the proposed scheme.

This paper presents modeling and parameter identification of the Duhem model to describe the hysteresis in the Piezoelectric actuated nano-stage. First, the parameter identification problem of the Duhem model is modeled into an optimization problem. A modified particle swarm optimization (MPSO) technique, which escapes the problem of local optima in a traditional PSO algorithm, is proposed to identify the parameters of the Duhem model. In particular, a randomness operator is introduced in the optimization process which acts separately on each dimension of the search space, thus improving convergence and model identification properties of PSO. The effectiveness of the proposed MPSO method was demonstrated using different benchmark functions. The proposed MPSO-based identification scheme was used to identify the Duhem model parameters, then, the results were validated using experimental data. The results show that the proposed MPSO method is more effective in optimizing the complex benchmark functions as well as the real-world model identification problems compared to conventional PSO and genetic algorithm (GA).

The present work proposes an islanding identification scheme based on the discriminating factor of islanding (DFI) extracted by the superimposed angle of impedance. The suggested methodology effectively identifies islanding within half of the cycle, discriminates islanding events (IEs) from non-islanding events (NIEs) at 0% power mismatch (PM), and unnecessary tripping due to different forms of NIEs. A significant Figure of IEs and NIEs have been tested across a wide assortment of PM on 7 bus microgrid test systems. Within this work, three separate cases (I, II, and III) are addressed. In the last, the results that were obtained from the proposed methodology is compared.

Aiming at the fault line identification issues of ineffectively grounded systems, a fault feeder identification programme lay on Variational Mode Decomposition (VMD) energy weight is proposed. The VMD algorithm has better adaptability than the Empirical Mode Decomposition (EMD), it can effectively suppress modal aliasing. Firstly, in order to determine the fault feeder, the zero-module current is used after every line fault is extracted for VMD decomposition. Then the frequency band energy corresponding to the intrinsic mode function (IMF) component are achieved. Finally, the energy weight of each line is compared as the final criterion. Case studies on a benchmark system verify the correctness and stability of the proposed fault line identification scheme.

To further improve the capability of fault identification for protection and adaptive reclosing, a fault identification scheme based on a control and protection coordination strategy for a hybrid multi-terminal HVDC system is proposed. The protection principle for fault isolation is constructed based on the difference of the polarity characteristic of fault current between the fault line and non-fault line in the uncontrolled fault stage and fault current limiting stage. In the adaptive reclosing stage, an active fault identification scheme based on the amplitude distribution law of the injection signal along the line is proposed. Intensive simulation studies prove the fault identification scheme can identify the fault area in various fault scenarios and identify permanent faults to carry out adaptive reclosing.

An identification scheme of the nonlinear Hammerstein-Wiener system with colored noise with the help of combined signals is proposed. The combined signals are used to active the Hammerstein-Wiener system, resulting in the identification process are significantly simplified. Firstly, the correlation analysis algorithm is employed to identify the output nonlinear block parameters and linear block parameters, thus the process noise can be compensated by correlation function. Furthermore, the immeasurable internal variable and the unknown noise term in identification system are replaced with the output of designed auxiliary model and estimated residual, therefore, auxiliary model based recursive extended least square identification scheme can be obtained to learn the input nonlinear block parameters and noise model parameter. Simulations results are given to demonstrate the advantage of the identification scheme.

In control system research, the pendulum system is one of the important nonlinear systems, and it has been widely used in many control applications. Different from the conventional pendulum system, this paper presents a reaction wheel based pendulum control system. From the application point of view, the design task is to stabilize the pendulum system through the reaction force generated from the flywheel. To this aim, dynamics modelling of the reaction wheel based pendulum must be considered first. Second, the system parameter identification scheme is required for future controller designs. Therefore, in this paper, dynamics modelling of the reaction wheel based pendulum is derived in term of Lagrange equation. For the associated nonlinear model, a discrete model approximation is proposed for the identification of system parameters. Finally, simulations are carried out to demonstrate the feasibility and effectiveness of the proposed method.

The model predictive direct torque control of AC induction motor is a promising high-performance motor control algorithm, but it depends heavily on the accuracy of motor parameters. In order to solve this problem, an online parameter identification scheme based on model reference adaptive system and sliding mode observer is proposed to identify both stator and rotor resistance. The rotor resistance is identified by using a reactive-power-based model reference adaptive system, and the observed value of the rotor flux is obtained at the same time. The stator resistance can be identified by the sliding mode observer from the obtained rotor flux and rotor resistance. According to the Popov’s superstability theory, the stability of system is proved. The validity of the proposed method is verified by simulation.

This paper presents a fault classification and faulty section identification scheme in Thyristor Controlled Series Compensator (TCSC) based transmission lines. The features derived from instantaneous current signals have been given to Support Vector Machine (SVM) classifiers for classifying faults and identifying faulty section. To validate the proposed method, a power system model with 400 kV, 300 km long, TCSC transmission line connected to sources at both the ends has been modelled using PSCAD/EMTDC. The SVM classifiers have been trained with 3,840 fault cases. Upon testing 14,400 fault cases with diversified fault and system scenario, the overall accuracy achieved for fault classification and faulty section identification with the proposed method is 99.83%

This paper focuses on parameter identification of a target tracker robot possessing flexible joints using particle swarm optimization (PSO) algorithm. Since, belt and pulley mechanisms are known as flexible joints in robotic systems, their elastic behavior affecting a tracker robot is investigated in this work. First, dynamic equations governing the robot behavior are extracted taking into account the effects of considered flexible joints. Thus, a flexible joint is modeled by a non-linear spring and damper system connecting the motor to the link. It is found that the governing dynamic equations include some unknown parameters, which must be identified in order to design the robot system. Consequently, a PSO-based identification scheme is proposed to achieve the unknown variables based on the experimental data of the open-loop system. Lastly, for validating the proposed identification scheme, the obtained results are compared to the experimental measurements as well as the results of another similar work in which the robot is modeled with rigid joints. The consequences reveal that the mathematical model of the robot with flexible joint can describe the elastic behavior of the tracker robot. Thus, a better agreement between the simulation and experimental data are found in comparison with outcomes of the robot model with rigid joints.

In this paper, a new active power loss allocation (LA) scheme is developed by eliminating the influence of cross-term mathematically from loss equation for allocating losses to the network participants with/without distributed generators (DGs). The unique bus identification scheme introduced performs load flow easily and efficiently by considering DGs as negative constant power loads. This method assigns losses to the consumers/DG units with due consideration to their power consumptions/injections and physical locations in the radial distribution network (RDN). The effectiveness of the proposed LA scheme has been investigated against the quadratic scheme of LA using a 33-bus RDN with/without DGs.

In this chapter adaptive state observers that takes parameters variation into consideration is used to achieve high performance without spoiling the dynamic response. These adaptive state observers are designed using the induction machine model. A modified gain rotor flux observer with a parameter adaptive scheme has been proposed for sensorless vector control induction motor drives. The value of the observer optimal gain has been derived by minimizing the error between the measured and estimated states. Also, the stability of the proposed rotor flux observer with the parameter identification scheme is proved using the Lyapunov’s theorem.

The problem of dynamics and control using a space robot to capture a noncooperative satellite is an important issue for on-orbit services. Inertia parameters of the satellite should be identified before capturing such that the robot can design an active controller to finish the capturing task. In this paper, a new identification scheme is proposed for parameter identification of a noncooperative satellite. In this scheme, the space robot is controlled to contact softly and then maintain contact with the noncooperative target firstly, then the variation of momentum of the target during the contact-maintaining phase is calculated using the control force and torque acting on the base of the space robot and the kinematic information of the space robot, and finally, the momentum-conservation-based identification method is used to estimate inertia parameters of the target. To realize soft contact and then maintain contact, a damping contact controller is designed in this paper, in which a mass-damping system is designed to control the contact between the robot and the target. Soft contact and then contact maintenance can be realized by utilizing the buffering characteristics of the mass-damping system. The effectiveness of the proposed identification scheme is verified through numerical simulations at the end of this paper. Simulation results indicate that the proposed scheme can achieve high-precision identification results.

A new identification approach for a nonlinear system with hysteresis, namely a cascading Bouc–Wen hysteresis model with linear dynamics, is proposed in this study. The properties of the Bouc–Wen model are analyzed under specific inputs. These properties play important roles in the parameter identification procedure. Unlike the commonly used iterative search or two-step identification scheme, the proposed approach completely decouples the identification tasks of linear and nonlinear parts and transforms each task into a linear task without iteration. First, a set of equations based on the aforementioned properties is developed. These equations enable the least squares estimation of all the parameters involved in linear dynamics with the use of the designed input signals and extended state estimation. Second, after the linear part is obtained, the hysteresis output is observed and used to establish the least squares estimation of all the parameters in the nonlinear part based on its input–output data. Simulation studies are performed to demonstrate the effectiveness of the proposed approach.

Effective fault identification scheme plays an important role in the fault isolation stage and system recovery stage of multi-terminal DC grid. Therefore, an active detection based single-end DC fault identification scheme is proposed to improve the reliability and sensitivity of fault identification. The control strategy is suggested of the generation of sinusoidal signals at different frequencies by the hybrid modular multilevel converters. Based on the blocking effect of the fault branch on the injection signal, by introducing the improved correlation discrimination algorithm, a single-end fault identification scheme is proposed by using the amplitude difference between the measured and calculated values of injection signals. The proposed scheme can be used for fault isolation and adaptive system recovery. Intensive simulation studies prove the improved sensitivity, and reliability of the proposed scheme for the full length of transmission line without relying on DC boundary. Moreover, the scheme can identify the permanent fault during the system recovery stage.

Maritime transport plays a vital role in economic development. To establish a vessel scheduling model, accurate ship maneuvering models should be used to optimize the strategy and maximize the economic benefits. The use of nonparametric modeling techniques to identify ship maneuvering systems has attracted considerable attention. The Gaussian process has high precision and strong generalization ability in fitting nonlinear functions and requires less training data, which is suitable for ship dynamic model identification. Compared with other machine learning methods, the most obvious advantage of the Gaussian process is that it can provide the uncertainty of prediction. However, most studies on ship modeling and prediction do not consider the uncertainty propagation in Gaussian processes. In this paper, a moment-matching-based approach is applied to address the problem. The proposed identification scheme for ship maneuvering systems is verified by container ship simulation data and experimental data from the Workshop on Verification and Validation of Ship Maneuvering Simulation Methods (SIMMAN) database. The results indicate that the identified model is accurate and shows good generalization performance. The uncertainty of ship motion prediction is well considered based on the uncertainty propagation technology.

Duhem model has been widely used to describe the hysteresis property for many memory-type nonlinear systems. In this paper, a novel parameter identification method based on artificial neural network has been developed for the Duhem hysteresis model. With the Duhem differential equation, a neural network is constructed reasonably whose weights are specially designed mapping to the model parameters. Based on the universal function approximation capability of neural network, the model parameters can be identified with the proposed approach by network training. The parameter identification scheme is validated by simulation firstly and then applied to modeling the piezoelectric actuator. The results reveal that the proposed identification approach can identify the static hysteresis nonlinearity with high accuracy. Furthermore, combined with the dynamic component, the presented method can also be used to describe the dynamic hysteresis of the piezoelectric actuator.

SUMMARYThis paper contributes by presenting a parameter identification procedure for n-degrees-of-freedom flexible joint robot manipulators. An advantage of the given procedure is the obtaining of robot parameters in a single experiment. Guidelines are provided for the computing of the joint position filtering and velocity estimation. The method relies in the filtered robot model, for which no acceleration measurements are required. The filtered model is expressed in regressor form, which allows applying a parameter identification procedure based on the least squares algorithm. In order to assess the performance of the proposed parameter identification scheme, an implementation of a least squares with forgetting factor (LSFF) parameter identification method is carried out. In order to assess the reliability of the tested identification schemes, a model-based trajectory tracking controller has been implemented twice in different conditions: one control experiment using the estimated parameters provided by the proposed scheme, and another experiment using the parameters given by the LSFF method. These real-time control experiments are compared with respect to numerical simulations using the estimated parameters for each identification method. For the proposed scheme, the comparison between experiments and numerical simulations indicates better accuracy in the torque and position prediction.

In this work, firstly, an asymptotic variance expression is derived for the transfer function estimates in the over-sampling based identification scheme. Then the result is used to analyze the over-sampling scheme. The asymptotic variance expression says that the joint covariance matrix of the transfer function estimates is proportional to the “generalized” noise-to-signal ratio. It is an application of the result in Ljung (1985); and it covers both open-loop and closed-loop tests. Using the result, one can show that informativity of the closed-loop tests without external excitation can be attained by using the over-sampling scheme, but only when the output disturbance has high frequency content beyond the bandwidth of the plant. Moreover, when the output disturbance has high frequency content and when test signal is used, the result points out that the over-sampling scheme can increase model accuracy, and, it will outperform conventional method with anti-aliasing filtering. Numerical examples are used to illustrate the asymptotic variance expression and the analysis about over-sampling scheme of identification.

In this paper, a new identification and control scheme using multitime scale recurrent high-order neural networks is proposed to control the singularly perturbed nonlinear systems with uncertainties. First, a novel identification scheme using modified optimal bounded ellipsoid based weight's updating laws is developed to identify the unknown nonlinear systems. By adding two additional terms to the original optimal bounded ellipsoid based weight's updating laws, the new modified identification scheme can achieve high convergence speed due to the adaptively adjusted learning gain at the beginning of the identification process and remain effective during the whole identification process. Based on the identified model, a new indirect adaptive control scheme for trajectory tracking problem using singular perturbation theory is developed, which is different from the control scheme proposed previously that can only be applied to a regulation problem. The closed-loop stability is analyzed and the convergence of system states is guaranteed. Experimental results are presented to demonstrate the effectiveness of the identification and control scheme.

In this paper, the conventional relay feedback test has been modified for modelling and identification of a class of real-time dynamical systems in terms of linear transfer function models with time-delay. An ideal relay and unknown systems are connected through a negative feedback loop to bring the sustained oscillatory output around the non-zero setpoint. Thereafter, the obtained limit cycle information is substituted in the derived mathematical equations for accurate identification of unknown plants in terms of overdamped, underdamped, critically damped second-order plus dead time and stable first-order plus dead time transfer function models. Typical examples from the literature are included for the validation of the proposed identification scheme through computer simulations. Subsequently, the comparisons between estimated model and true system are drawn through integral absolute error criterion and frequency response plots. Finally, the obtained output responses through simulations are verified experimentally on real-time liquid level control system using Yokogawa Distributed Control System CENTUM CS3000 set up.

This paper presents a novel control and identification scheme based on adaptive dynamic programming for nonlinear dynamical systems. The aim of control in this paper is to make output of the plant to follow the desired reference trajectory. The dynamics of plants are assumed to be unknown, and to tackle the problem of unknown plant’s dynamics, parameter variations and disturbance signal effects, a separate neural network-based identification model is set up which will work in parallel to the plant and the control scheme. Weights update equations of all neural networks present in the proposed scheme are derived using both gradient descent (GD) and Lyapunov stability (LS) criterion methods. Stability proof of LS-based algorithm is also given. Weight update equations derived using LS criterion ensure the global stability of the system, whereas those obtained through GD principle do not. Further, adaptive learning rate is employed in weight update equation instead of constant one in order to have fast learning of weight vectors. Also, LS- and GD-based weight update equations are also tested against parameter variation and disturbance signal. Three nonlinear dynamical systems (of different complexity) including the forced rigid pendulum trajectory control are used in this paper on which the proposed scheme is applied. The results obtained with LS method are found more accurate than those obtained with the GD-based method.