Input-output Measurement Data Research Articles

Impact of Uncertainty Quantification on Small Signal Stability of Power System

The statistical uncertainty quantification of the injected uncertainties into a power system has been done to study the response of the power system to such injected randomness. The dominant electromechanical modes have been analysed and the frequency spectrum of the trajectories obtained for various state variables has been used to measure the impact of injected randomness on the system. The idea of Statistical Distance between the point of input perturbations and output measurement data has been explored. The simulation results indicate that there is a direct relationship between the input uncertainty and output measurement variable and is greatly affected by the magnitude and location of random injection. The above framework has been validated using Kundur's two area system and IEEE 14 Bus system.

Data-Driven Finite Control-Set Model Predictive Control for Modular Multilevel Converter

This article investigates a data-driven-based predictive current control (DD-PCC) approach for a modular multilevel converter (MMC) to circumvent the sensitiveness to parameter variation and unmodeled dynamics of a finite control-set model predictive control (FCS-MPC) method. By integrating a model-free adaptive control (MFAC)-based data-driven solution into the FCS-MPC framework, the performance deterioration caused by model uncertainties is suppressed. The design of the suggested controller is only based on input–output measurement data, where neither the parameter information nor the knowledge of detailed MMC models is required, leading to improved robustness against parameter drifts and model uncertainness. Moreover, a simplified cost function formula that takes into account output current tracking and circulating current regulation is constructed to efficiently determine the optimal insertion index of each arm. Finally, simulation and experimental results are obtained to verify the steady-state, dynamics, and robustness performance of the proposed approach.

Uncertainty quantification of structural flexibility identified from input–output measurement data for reliability analysis

AbstractIn contrast to the traditional uncertainty quantification of the modal parameters identified from the output‐only measurement data, this study develops an uncertainty propagation and quantification method for structural flexibility identified from input–output measurement data and then uses the derived flexibility matrix for structural reliability analysis. First, a novel procedure for variance estimations of the complete system matrix of the state‐space model is derived based on first‐order perturbation theory. Then, the mode scaling factors and integrated modal flexibility are subsequently derived and quantified. Finally, the predicted displacements and their uncertainty intervals are used to implement the reliability analysis in terms of the structural displacement serviceability limit state (SLS). The accuracies of the derived expressions are verified by a numerical example through the comparison of variance values estimated from Monte Carlo simulations. The simulation example also indicates that we can expect to identify structural parameters with higher reliability if the input force to the structure is measured, and the identified modal scaling and corresponding flexibility matrix undergo more uncertainty than those of the modal parameters. Finally, we apply the proposed uncertainty quantification method to a scaled three‐story frame structure to demonstrate its practicability. The proposed method bridges a gap between the identified structural flexibility and structural reliability analysis directly by the probability category.

Composite Data Driven-Based Adaptive Control for a Piezoelectric Linear Motor

Piezoelectric linear motors play an important role in ultraprecision manufacturing technology. However, the complex nonlinear relationship between the input and output of the piezoelectric linear motors limits their further application. In this article, to achieve precise motion control for a piezoelectric linear motor, a composite data-driven-based adaptive control method is proposed, consisting of a correction controller, model-free adaptive control (MFAC), and low pass filter. The proposed control method addresses the demand for a precise model of the piezoelectric linear motor and solely relies on the linear model and input–output measurement data. First, an experimental test is implemented to analyze the complex nonlinearity between the input and output signals of the controlled system, and a correction control is employed based on the dynamic linear sub-model of the piezoelectric linear motor to improve its dynamic and static characteristics. Then, to avoid the influence of unmodeled dynamics, such as inherent nonlinearity and external vibration, an MFAC is established as a feedback controller using data-driven technology. In addition, a low pass filter is incorporated into the feedback loop to eliminate high-frequency measurement noise in the system, thus improving the transient response of the MFAC method. Finally, the theoretical analysis of the error convergence is presented. The effectiveness of the proposed method is verified via comparisons with a correction control method, correction control-based digital sliding-mode control (DSMC) method, and correction control-based MFAC method. The experimental results indicate that the proposed control method is suitable for engineering applications. In particular, the root-mean-square error (RMSE) for the third-order S-curve tracking using the proposed is reduced by more than 15%, compared with the RMSEs for the cases with contrast control methods.

Tracking Control of a Miniature 2-DOF Manipulator With Hydrogel Actuators

Due to the nature of the complex spatiotemporal dynamics of stimuli-responsive soft materials, closed-loop control of hydrogel-actuated mechanisms has remained a challenge. This letter demonstrates, for the first time, closed-loop trajectory tracking control in real-time of a millimeter-scale, two degree-of-freedom manipulator via independently-controllable, temperature-responsive hydrogel actuators. A linear state-space model of the manipulator is developed from input-output measurement data, enabling the straightforward application of control techniques to the system. The Normalized Mean Absolute Error (NMAE) between the modeled and measured displacement of the manipulator's tip is below 10%. We propose an Observer-based controller and a robust H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -optimal controller and evaluate their performance in a trajectory tracking output-feedback framework, compared with and without sinusoidal disturbances and noise. We demonstrate in simulation that the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -optimal controller, which is computed using Linear Matrix Inequality (LMI) methods, tracks an elliptical trajectory more accurately than the Observer controller and is more robust to disturbances and noise. We also show experimentally that the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -optimal controller can be used to track different trajectories with an NMAE below 15%, even when the manipulator is subject to a 3 g load, 12.5 times an actuator's weight. Finally, a payload transport scenario is presented as an exemplar application; we demonstrate that an array of four manipulators is capable of moving a payload horizontally by applying the proposed H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> -optimal trajectory-tracking controller to each manipulator in a decoupled manner.

Forecasting-based data-driven model-free adaptive sliding mode attitude control of combined spacecraft

In this paper, a novel forecasting-based data-driven model-free adaptive sliding mode attitude control (FMFASMC) method is proposed for the postcapture combined spacecraft in the presence of unknown mathematical model and external disturbance. First, a forecasting-based data-driven model-free adaptive controller (FMFAC) is developed to ensure its adaptation to various working conditions by simultaneous adjustment of controller parameters with online and offline input–output measurement data. Then, a sliding-mode-based supplementary controller is introduced to improve the tracking performance of FMFAC in terms of robustness. Compared with existing works, the designed control scheme only utilizes the attitude angle and attitude angular velocity of the combined spacecraft, not requiring corresponding mechanism model, thereby dramatically curtailing the complexity and difficulty of relevant controller design. Finally, simulation comparisons between the FMFASMC, FMFAC, and traditional model-free adaptive control (MFAC) are presented for two groups of illustrative examples. The simulation results verify the effectiveness of the proposed control method.

Local learning‐based model‐free adaptive predictive control for adjustment of oxygen concentration in syngas manufacturing industry

During the united gas improvement (UGI) gasification process in the syngas industry, the oxygen-enriched technique plays an important role, since the obtained oxygen-enriched air with a high oxygen concentration can enhance the production efficiency of the syngas. However, satisfactory control performance for the oxygen concentration of the oxygen-enriched air is hard to achieve because an accurate dynamical model of the oxygen concentration control process by the first principles is fairly difficult to obtain due to strong non-linearity and unknown disturbances in practice. A novel data-driven control method called compact-form-dynamic-linearisation-based model-free adaptive predictive control approach combined with the local learning (LL-CFDL-MFAPC) is proposed to address the control problem. In LL-CFDL-MFAPC, the online and offline input-output measurement data of the plant are fully and simultaneously utilised during the control process, and the design of the controller is model free by means of compact-form-dynamic-linearisation technique. Moreover, the controller has strong robustness because the prediction mechanism participates in control design and only the input/output measurement data are used. The stability and convergence of LL-CFDL-MFAPC are guaranteed by theoretical analysis under several reasonable assumptions, and simulation experiments using real data collected from a practical UGI gasifier verify that the oxygen concentration control problem can be effectively addressed by the proposed method.

Application of Kalman Filtering Methods to Online Real-Time Structural Identification: A Comparison Study

System identification refers to the process of building or improving mathematical models of dynamical systems from the observed experimental input–output data. In the area of civil engineering, the estimation of the integrity of a structure under dynamic loadings and during service condition has become a challenge for the engineering community. Therefore, there has been a great deal of attention paid to online and real-time structural identification, especially when input–output measurement data are contaminated by high-level noise. Among real-time identification methods, one of the most successful and widely used algorithms for estimation of system states and parameters is the Kalman filter and its various nonlinear extensions such as extended Kalman filter (EKF), Iterated EKF (IEKF), the recently developed unscented Kalman filter (UKF) and Iterated UKF (IUKF). In this paper, an investigation has been carried out on the aforementioned techniques for their effectiveness and efficiencies through a highly nonlinear single degree of freedom (SDOF) structure as well as a two-storey linear structure. Although IEKF is an improved version of EKF, results show that IUKF generally produces better results in terms of structural parameters and state estimation than UKF and IEKF. Also IUKF is more robust to noise levels compared to the other approaches.

Lazy-Learning-Based Data-Driven Model-Free Adaptive Predictive Control for a Class of Discrete-Time Nonlinear Systems.

In this paper, a novel data-driven model-free adaptive predictive control method based on lazy learning technique is proposed for a class of discrete-time single-input and single-output nonlinear systems. The feature of the proposed approach is that the controller is designed only using the input-output (I/O) measurement data of the system by means of a novel dynamic linearization technique with a new concept termed pseudogradient (PG). Moreover, the predictive function is implemented in the controller using a lazy-learning (LL)-based PG predictive algorithm, such that the controller not only shows good robustness but also can realize the effect of model-free adaptive prediction for the sudden change of the desired signal. Further, since the LL technique has the characteristic of database queries, both the online and offline I/O measurement data are fully and simultaneously utilized to real-time adjust the controller parameters during the control process. Moreover, the stability of the proposed method is guaranteed by rigorous mathematical analysis. Meanwhile, the numerical simulations and the laboratory experiments implemented on a practical three-tank water level control system both verify the effectiveness of the proposed approach.

Newton iterative identification for a class of output nonlinear systems with moving average noises

This paper discusses iterative identification problems for a class of output nonlinear systems (i.e., Wiener nonlinear systems) with moving average noises from input–output measurement data, based on the Newton iterative method. The basic idea is to decompose a nonlinear system into two subsystems, to replace the unknown variables in the information vectors with their corresponding estimates at the previous iteration, and to present a Newton iterative identification method using the hierarchical identification principle. The numerical simulation results indicate that the proposed algorithms are effective.

Model Selection of Hammerstein System Nonlinearity under Heavy Noise

Abstract The paper deals with the problem of automatic model selection of the nonlinear characteristic in a block-oriented dynamic system. We look for the parametric model of Hammerstein system nonlinearity. From the finite set of candidate classes of parametric models we select the best one on the basis of the input-output measurement data, using the concept of nearest neighbour borrowed from pattern recognitions techniques. The algorithm uses the pattern of the true characteristic generated by its nonparametric estimates on the grid of fixed (e.g. equidistant) points. Each class generates parametrized learning sequence through the values on the same grid of points. Next, for each class, the optimal parameters are computed by the least squares method. Finally, the nearest neighbour approach is applied for the selection of the best model in the mean square sense. The idea is presented on the exemplary competition between polynomial, exponential and piece-wise linear models of the same complexity (i.e. number of parameters needed to be stored in memory). For all classes, the upper bounds of the integrated approximation errors of the true characteristic are computed and compared.

An Approximate Wind Turbine Control System Model for Wind Farm Power Control

Wind farm power control is key to reliable large-scale wind integration. The design of a sophisticated wind farm controller, however, is challenging partly because there is a lack of models that appropriately simplify the complex overall dynamics of a large number of wind turbine control systems (WTCSs). In this paper, using system identification approaches, we develop a simple approximate model that attempts to mimic the active and reactive power dynamics of two generic WTCS models under normal operating conditions: an analytical model described by nonlinear differential equations, and an empirical one by input-output measurement data. The approximate model contains two parts-one for active power and one for reactive-each of which is a third-order system that would have been linear if not for a static nonlinearity. For each generic model, we also provide an identification scheme that sequentially determines the approximate model parameters. Finally, we show via simulation that, despite its structural simplicity, the approximate model is accurate and versatile, capable of closely imitating several different analytical and empirical WTCS models from the literature and from real data. The results suggest that the approximate model may be used to facilitate research on wind farm power control.

An iterative numerical algorithm for modeling a class of Wiener nonlinear systems

This letter presents an iterative estimation algorithm for modeling a class of output nonlinear systems. The basic idea is to derive an estimation model and to solve an optimization problem using the gradient search. The proposed iterative numerical algorithm can estimate the parameters of a class of Wiener nonlinear systems from input–output measurement data. The proposed algorithm has faster convergence rates compared with the stochastic gradient algorithm. The numerical simulation results indicate that the proposed algorithm works well.

Direct multivariate subspace time identification

The paper presents a time domain method to identify structural modal parameters by fitting a discrete multivariate space-time model into noise corrupted, input–output measurement data. The subspace identification scheme proposed is an important characteristic of the method, leading to a deterministic and statistically bias free estimation of parameters. The identification model in this scheme, is kept to the minimum description order, in spite of the presence of noise in the system's input and output The method is used to estimate natural frequencies, damping factors and mode shapes in an experimental modal analysis test.

MULTIPLE MODEL WEIGHT ESTIMATION FOR MODELS WITH NO COMMON STATE

In this paper the problem of model weight estimation is considered for systems represented by convex combinations of a set of multiple linear models with time-varying weights. As opposed by the majority of the existing methods, the present paper considers the more general case when the models in the model set do not necessarily share the same state basis and may even have different state dimension. Basically the method collects a batch of input-output measurement data within some fixed time interval, which is subsequently projected in such a way, that the influence of the state vector is removed. The resulting nonlinear constraint optimization problem, that in a particular special case takes the form of a convex optimization problem, is then solved with respect to the model weights.

Robust Isolation Of Sensor Failures

ABSTRACTSensor self‐validity check is a critical step in system control and fault diagnostics. In this paper, a robust approach to isolate sensor failures is proposed. First, a residual model for a given system is built off‐line and directly based on input‐output measurement data. The residual model outputs are called “primary residuals” and are zero when there is no fault. Most conventional approaches to residual model generation are indirect, as they first require the determination of state‐space or other models using standard system identification algorithms. Second, a new max‐min design of structured residuals, which can maximize the sensitivity of structured residuals with respect to sensor failures, is proposed. Based on the structured residuals, one can then isolate the sensor failures. This design can also be done in an off‐line manner. It is an optimization procedure that avoids local optimal solutions. Simulation and experimental results demonstrated the effectiveness of the proposed method.

Robust quasi-LPV control based on neural state-space models

We derive a synthesis result for robust linear parameter varying (LPV) output feedback controllers for nonlinear systems modeled by neural state-space models. This result is achieved by writing the neural state-space model on a linear fractional transformation (LFT) form in a nonconservative way, separating the system description into a linear part and a nonlinear part. Linear parameter-varying control synthesis methods are then applied to design a nonlinear control law for this system. Since the model is assumed to have been identified from input-output measurement data only, it must be expected that there is some uncertainty on the identified nonlinearities. The control law is therefore made robust to noise perturbations. After formulating the controller synthesis as a set of linear matrix inequalities (LMIs) with added constraints, some implementation issues are addressed and a simulation example is presented.

Identification of non-linear characteristics of a class of block-oriented non-linear systems via Daubechies wavelet-based models

The paper deals with the identification of non-linear characteristics of a class of block-oriented dynamical systems. The systems are driven by random stationary white processes (i.i.d. random input sequences) and disturbed by a zero-mean stationary, white or coloured, random noise. The prior knowledge about non-linear characteristics is non-parametric excluding implementation of standard parametric identification methods. To recover non-linearities, a class of Daubechies wavelet-based models using only input-output measurement data is introduced and their accuracy is investigated in the global MISE error sense. It is shown that the proposed models converge with a growing collection of data to the true non-linear characteristics (or their versions), provided that the complexity of the models is appropriately fitted to the number of measurements. Suitable rules for optimum model size selection, maximizing the convergence speed, are given and the asymptotic rate of convergence of the MISE error for optimum models is established. It is shown that in some circumstances the rate is the best possible that can be achieved in non-parametric inference. We also show that the convergence conditions and the asymptotic rate of convergence are insensitive to the correlation of the noise and are the same for known and unknown input probability density function (assumed to exist). The theory is illustrated by simulation examples.

Modular neural networks for non-linearity recovering by the Haar approximation

The paper deals with the design of a composite neural system for recovering non-linear characteristics from random input–output measurement data. It is assumed that non-linearity output measurements are corrupted by an additive zero-mean white random noise and that the input excitation is an i.i.d. random sequence with an arbitrary (and unknown) probability density function. A class of modular networks is developed. The class is based on the Haar approximation of functions with piecewise constant functions on a refinable grid and consists of the networks composed of perceptron-like modules connected in parallel. The networks provide a local mean value estimators of functions. The relationship between complexity and accuracy of modular networks is analysed. It is shown that under mild conditions on the non-linearities and input probability density functions the networks yield pointwise consistent estimates of non-linear characteristics, provided that complexity of the networks grows appropriately with the number of training data. Efficiency of the networks is examined and the asymptotic rate of convergence of the network estimates is established. Specifically, local ability of the networks to recover non-linear characteristics in dependence on local smoothness of the underlying non-linear function and the input probability density is discussed. Optimum complexity selection rules, guaranteeing the best performance of the networks, are given. Illustrative simulation examples are provided.

Optimized Improvement of Convergency in Least Squares Parameter Estimation of Biomedical Models

This paper investigates a method for optimally improving the convergency and stabil ity in the least squares (LS) estimation of model parameters in biological models. When we have to use a strongly correlated input for the identification and we are given only short length of noisy input-output measurement data, as arises in actual ph0ysiological experiments, the LS parameter estimates tend to fluctuate excessively and not to settle in steady-state values for the given data length. We focus on a method of adding nonnegative weighting constants into the diagonal elements of the data covariance matrix, which is normally used to mitigate the ill-conditions of a nearly singular covariance matrix. The aim of this paper is to theoretically describe the effects of the weighting constants on the mean square error (MSE) of the parameter estimates, and clarify the existence of the optimal weighting constant minimizing the MSE. Furthermore, we give a data-adaptive way of determining the optimal value of the weighting constant, which makes use of only available input-output measurement data. The effectiveness of the optimal selection of the weighting constant in the convergency improvement is investigated through both numerical simulations and modell ing of kinetics in diabetes using depancreatized dogs.