Adaptive Parameter Estimation Method Research Articles

A Novel Adaptive Model Predictive Control Strategy for DFIG Wind Turbine With Parameter Variations in Complex Power Systems

In this paper, a novel adaptive model predictive control (MPC) strategy is proposed for doubly fed induction generator (DFIG) wind turbine (WT), which is integrated into a complex power system, in order to improve the power output tracking precision and dynamic performance. Considering the existence of parameter variations in DFIG, an adaptive parameter estimation method is firstly designed. By analysis of stability, the convergence of the parameter estimation algorithm is rigorously proved. Furthermore, the parameter estimation algorithm is effectively integrated into MPC to realize real-time optimal control of DFIG with the adaptive model. To achieve the rotor side converter (RSC) design based on adaptive MPC, the DFIG model is linearized. In addition, a virtual output compensation (VOC) strategy is adopted to alleviate the impact of model linearization errors on the MPC, especially the variation of the model parameter. The newly proposed adaptive MPC-based RSC is capable of greatly improving the tracking performance, meanwhile taking into account the realistic constraints under various operation conditions. The simulation results demonstrate the effectiveness and superiority of the proposed control method.

Robust adaptive parameter estimator design for a multi-sinusoidal signal with fixed-time stability and guaranteed prescribed performance boundary of estimation error

In online parameter estimation, fast and accurate estimation without dependence on the initial conditions with good transient properties is an essential issue. Hence, in this paper, a robust adaptive parameter estimation method is proposed that not only can estimate online the unknown parameters of a multi sinusoidal signal in a finite-time without dependence on the initial conditions but also can guarantee the transient and steady-state performance of the estimated parameters with a predefined boundary. At first, to estimate the frequency, offset, phase and amplitude, the multi-sinusoidal signal is represented in a linear regression form. Then, by defining an auxiliary regressor matrix and vector derived by the original regressor vector and output signal, the estimation error is computed indirectly. By employing this parameter estimation error and utilizing the fixed-time stability property of the terminal sliding mode method and Prescribed-Performance concept, robust adaptation laws are designed in such a way that the convergence time of the estimation error is finite so that its settling time is independent from the initial guess, and this time can be predefined. Moreover the estimation error boundary can be predefined. Furthermore, in this method, only the output measurement is needed and there is no need for any observer or predictor. Using the Lyapunov stability theorem it is proved that the estimation error converges to zero when the measurement is free of noise and for the noisy case the estimation error is uniformly ultimately bounded and converges to a compact set around the origin. Three illustrative examples are provided to demonstrate the advantages and effectiveness of the designed estimation laws compared to some existing methods. Simulation results show that the proposed method can estimate the unknown parameters in fixed-time with a pre-adjustable estimation error boundary, even in the presence of measurement noise and confirms the superiority of the proposed method compared to the two existing methods.

Adaptive finite-time parameter estimation for grid-connected converter with LCL filter

This study proposes an online finite-time adaptive parameter estimation method for grid-connected voltage source inverter (VSI) with LCL filter. In this framework, a time-continuous state-space system model is first developed, which is different from the nonparametric frequency-response-based method. Then, the parameter estimation error information is extracted by introducing a series of filter operations. Moreover, a sliding mode term is studied and cooperated with the obtained parameter estimation error to design the parameter estimation error-based finite-time adaptive parameter estimation algorithm. With the help of this novel adaptive law, the physical parameters of electrical components can be driven to their true values accurately and fast. Theoretical analysis is conducted in terms of the Lyapunov theory to demonstrate the convergence of the parameter estimation error. Finally, numerical simulation results are provided to illustrate the superiority of the proposed method over the conventional method.

Parameter Estimation and Adaptive Control of Super-coiled Polymer Artificial Muscles

Super-coiled polymer (SCP) artificial muscles demonstrate desirable properties such as high-power density, compliance, and low-cost. However, their performance is dependent on the ambient environment. It is necessary but difficult to re-identify key SCP parameters to maintain desired performance. In this work, an adaptive parameter estimation method is proposed to identify unknown SCP parameters. The SCP model identification demonstrates parameter convergence which indicates that SCPs could be controlled effectively and used in environments with variations. We further develop a control law using direct model reference adaptive control (MRAC) on the output position of the SCP actuator. The MRAC performance is compared to a proportional-integral (PI) and proportional-derivative (PD) with feedforward controllers to demonstrate the ability to reduce tracking errors. The MRAC showed superior performance in comparison to the two controllers when the SCP actuator parameters were unknown.

A novel adaptive linear prediction-based parameter estimation method for monitoring sub-/inter-harmonics during SSI events

Sub-Synchronous Interaction (SSI), caused by the increasing penetration of inverter connected nonsynchronous generation, is posing a serious threat on power system stability. As the sub-/inter-harmonics emerging during SSI are featured by time-varying frequencies, concerns regarding the accuracy and the robustness of existing methods for monitoring oscillations of such phenomenon have been raised. This paper proposes a novel two-stage numerical method for monitoring the sub-harmonics, harmonics and inter-harmonics during SSI events. In the first stage, the model order of the estimation is selected using the proposed Adaptive Model Order Selection (AMOS) scheme, which is achieved through singular value decomposition. In the second stage, a linear prediction-based parameter estimation approach with high accuracy and strong robustness is used to estimate the signal parameters of the sub-harmonics, harmonics and inter-harmonics. By merging the above two stages, a novel and effective numerical method is obtained, namely the Adaptive Linear Prediction-based Parameter Estimation (ALPPE) method. In addition, an estimation calibration is implemented to increase the estimation accuracy. The proposed ALPPE method has then been tested using computer simulated signals and field measured data from an actual SSI event. The results of the tests confirmed the ALPPE method's high accuracy, adaptability, processing speed and immunity to noise.

Fast Parameter Estimation for Adaptive Maximum Torque Per Ampere Control of Interior Permanent Magnet Synchronous Motor Drives

This paper proposes a fast adaptive parameter estimation method for maximum torque per ampere control (MTPA) of interior permanent magnet synchronous motor (IPMSM) drives. The adaptive features that enable fast parameter estimation are investigated, and an adaptive observer with a structure capable of achieving fast estimation is proposed. A cascade adaptive observer design strategy is presented in order to obtain the benefit of fast estimation from the proposed adaptive scheme. The MTPA is achieved through the solution of the stator current optimization problem combined with the IPMSM estimated parameters. Simulation and experimental results are provided in order to validate the proposed fast estimation adaptive MTPA algorithm.

Application of autoregressive dynamic adaptive (ARDA) model in real-time wind power forecasting

Wind prediction technology has been the focus on the national research with the basis of the power system planning, a reference for power dispatch, and the optimal power flow distribution. Prediction technology is moving into the direction of controlling refinement with the development of the information technology, the artificial intelligence technology, and the improvement of edge computing devices. The wind farms can use real-time wind power prediction to improve their overall efficiency through advanced planning of the wind turbine adjustment and the pre-setting of the yaw, pitch, and generation excitation control systems. This paper proposes an innovative autoregressive dynamic adaptive (ARDA) model based on the improvement of the autoregressive (AR) model. The fixed parameter estimation method of the AR model is improved in the proposed model to a dynamically adaptive stepwise parameter estimation method. Meanwhile, the coefficients of the model are updated adaptively based on the characteristics of wind power data, which improves the accuracy of the proposed model. The prediction accuracy of the proposed model is further improved by the residual function. It was observed that the model adapts well to wind power data with different degrees of volatility. The ARDA model and two other models were tested by using stationary and fluctuating wind power data (unit: seconds), and the wind power prediction results at different forecasting step lengths were compared. It was observed that the ARDA model is more accurate, with faster calculation rate, and better dynamic adaptability to data fluctuations than the ARIMA and LSTM models. This paper proposes an important method for real-time power prediction that can be employed for the advanced control and improved the power generation of wind farms.

Robust real‐time parameter estimation for linear systems affected by external noises and uncertainties

SummaryA robust adaptive parameter estimation method, based on the application of a full‐order filter capable of rejecting exogenous disturbances, is proposed in this article. A linear matrix inequality condition is proposed to synthesize the desired robust filter, assuming the presence of a known input control with constraints. The filter uses the output of the system to estimate the desired signal that will be employed in the adaptive estimation procedure and, to assure robustness to exogenous noise and unstructured uncertainties, the guaranteed cost is minimized in the synthesis condition. The filtered signals are then applied to an adaptive procedure to estimate the unknown system's internal parameters, which is also proposed in this article. It is shown that lower values for the guaranteed cost from the disturbance input to the error output of the filter imply more accurate estimations of the parameters. The efficiency of the proposed estimation technique is illustrated through a simulated model and a physical system has been considered to validate real‐time estimation.

Parameter Estimation and Adaptive Control for Servo Mechanisms With Friction Compensation

This article presents a new adaptive parameter estimation method and the corresponding control design for nonlinear servo mechanisms with friction compensation. A continuous friction model is employed to capture the friction dynamics of servo mechanisms. Then, the unknown system parameters including friction model parameters are online estimated via the derived adaptive law. Hence, a new adaptive law is proposed to achieve faster and more accurate parameter estimation over classical adaptive laws so as to suppress the undesired transient dynamics. For this purpose, an auxiliary filter is introduced to extract the estimation error for driving the parameter updating law. Moreover, an adaptive control is designed in conjunction with a robust integral of the sign of the error feedback term to address the bounded disturbances and enhance the tracking precision. Simulations and experiments are given to validate the efficiency of the developed control scheme.

Real-Time Adaptive Parameter Estimation for a Polymer Electrolyte Membrane Fuel Cell

In this paper, we propose real-time adaptive parameter estimation methods for a polymer electrolyte membrane fuel cell (PEMFC) to facilitate the modeling and the subsequent control synthesis. Specifically, the electrochemical model of this fuel cell is in a nonlinearly parametric formulation. Hence, most of existing parameter estimation techniques for PEMFC mainly rely on the optimization approaches, requiring heavy computational costs or even offline implementation. In comparison to those methods, real-time adaptive parameter estimation methods for nonlinearly parametric system are developed in this paper. First, the nonlinearly parametric function is linearized by using the Taylor series expansion. Then, adaptive parameter estimation methods are proposed for estimating the constant or time-varying parameters of PEMFC. Different from the well-recognized adaptive parameter estimation methods, the proposed adaptive laws are driven by the extracted estimation errors, so that exponential convergence of the parameter estimation error can be guaranteed, without using any predictors or observers. Finally, practical experiments in a H-100 PEMFC system are conducted, which illustrate satisfactory performances of the presented parameter estimation methods under different operation scenarios.

Efficient Parameter Estimation for Sparse SAR Imaging Based on Complex Image and Azimuth-Range Decouple.

Sparse signal processing theory has been applied to synthetic aperture radar (SAR) imaging. In compressive sensing (CS), the sparsity is usually considered as a known parameter. However, it is unknown practically. For many functions of CS, we need to know this parameter. Therefore, the estimation of sparsity is crucial for sparse SAR imaging. The sparsity is determined by the size of regularization parameter. Several methods have been presented for automatically estimating the regularization parameter, and have been applied to sparse SAR imaging. However, these methods are deduced based on an observation matrix, which will entail huge computational and memory costs. In this paper, to enhance the computational efficiency, an efficient adaptive parameter estimation method for sparse SAR imaging is proposed. The complex image-based sparse SAR imaging method only considers the threshold operation of the complex image, which can reduce the computational costs significantly. By utilizing this feature, the parameter is pre-estimated based on a complex image. In order to estimate the sparsity accurately, adaptive parameter estimation is then processed in the raw data domain, combining with the pre-estimated parameter and azimuth-range decouple operators. The proposed method can reduce the computational complexity from a quadratic square order to a linear logarithm order, which can be used in the large-scale scene. Simulated and Gaofen-3 SAR data processing results demonstrate the validity of the proposed method.

Classification of Data Stream in Sensor Network With Small Samples

Compared with conventional narrowband radar, ultra-wideband (UWB) radar has strong anti-interference performance, low-frequency, and wide-frequency characteristics, a good penetrating ability, a high-resolution range, and a good target-recognition ability. It is effective for the detection of weak signals such as breathing or motion of the human body. Therefore, a UWB radar is widely used in the field of human body target detection for earthquake and snow disasters. At present, through-wall human target detection technology using a UWB radar has two challenging problems. First, UWB radar data has a dynamic acquisition process, whereas existing detection technologies mostly employ static learning models, resulting in an inability to process and learn data features online. There is a need to address target recognition based on sensor-network data stream. Second, UWB radar faces the problem of unbalanced data classification in the identification of through-wall targets, i.e., target recognition under small-sample conditions. To address these issues, this paper proposes an adaptive incremental recursive least-squares regression parameter estimation method based on an adaptive variable sliding window, which performs Gaussian function fitting on the data streams and adapts to the mean square error and self-adaptation variable sliding window threshold comparison to adaptively block dynamic data streams. The tensor space theory is used to extract and fuse the multisensor data, and the tensor depth learning algorithm is used to improve the recognition accuracy of the target detection under small-sample conditions. To evaluate the feasibility of the proposed algorithm, we use three UWB radars to build a multistate recognition system for human targets. The data stream adaptive block effect, single and multisensor classification effects were tested under small-sample conditions. The experimental results indicate that the proposed algorithm not only accurately segmented the dynamic data streams but also effectively realized multisensor information fusion and improved the real-time target monitoring under small-sample conditions.

Extracting Cardiac Information From Medical Radar Using Locally Projective Adaptive Signal Separation.

Electrocardiography is the gold standard for electrical heartbeat activity, but offers no direct measurement of mechanical activity. Mechanical cardiac activity can be assessed non-invasively using, e.g., ballistocardiography and recently, medical radar has emerged as a contactless alternative modality. However, all modalities for measuring the mechanical cardiac activity are affected by respiratory movements, requiring a signal separation step before higher-level analysis can be performed. This paper adapts a non-linear filter for separating the respiratory and cardiac signal components of radar recordings. In addition, we present an adaptive algorithm for estimating the parameters for the non-linear filter. The novelty of our method lies in the combination of the non-linear signal separation method with a novel, adaptive parameter estimation method specifically designed for the non-linear signal separation method, eliminating the need for manual intervention and resulting in a fully adaptive algorithm. Using the two benchmark applications of (i) cardiac template extraction from radar and (ii) peak timing analysis, we demonstrate that the non-linear filter combined with adaptive parameter estimation delivers superior results compared to linear filtering. The results show that using locally projective adaptive signal separation (LoPASS), we are able to reduce the mean standard deviation of the cardiac template by at least a factor of 2 across all subjects. In addition, using LoPASS, 9 out of 10 subjects show significant (at a confidence level of 2.5%) correlation between the R-T-interval and the R-radar-interval, while using linear filters this ratio drops to 6 out of 10. Our analysis suggests that the improvement is due to better preservation of the cardiac signal morphology by the non-linear signal separation method. Hence, we expect that the non-linear signal separation method introduced in this paper will mostly benefit analysis methods investigating the cardiac radar signal morphology on a beat-to-beat basis.

An Adaptive Prognostic Approach for Newly Developed System With Three-Source Variability

Remaining useful life (RUL) estimation is the key of prognostics and health management (PHM) technology and is an effective way to ensure the safe and reliable operation of equipment. Aiming at the lack of historical data and prior information for the newly developed small-sample systems, an adaptive RUL estimation method based on the expectation maximization (EM) algorithm is proposed with three-source variability. First, a degradation model based on a Wiener process is established to incorporate three-source variability and dynamic sampling interval, and the analytical solution of RUL distribution is derived in the sense of the first hitting time. Second, an adaptive parameter estimation method based on the EM algorithm is proposed to update the model parameters by using the condition monitoring (CM) data from one working system running up to the current moment. Finally, a practical example of a gyroscope in an inertial navigation system is provided to substantiate the effectiveness and superiority of the proposed method. The results indicate that the proposed method can efficiently improve the accuracy of the RUL estimation.

Two novel robust adaptive parameter estimation methods with prescribed performance and relaxed PE condition

Two novel robust adaptive parameter estimation methods with prescribed performance and relaxed PE condition

Adaptive nonlinear generalized predictive control for hypersonic vehicle with unknown parameters and control constraints

In this article, an adaptive nonlinear generalized predictive controller is developed for the hypersonic vehicle with unknown parameter uncertainties and control surface constraints. A nominal controller based on generalized predictive control is first developed for the nominal longitudinal model. Considering the complicated practical flying environment and outer unknown disturbances, a parametric longitudinal dynamics model of the hypersonic vehicle is constructed. Then, an adaptive parameter estimation method is proposed to update the unknown controller parameters and parameter projection is applied to solve the control singularity and the parameter boundedness problem. Control surface is restricted to deflect in a range. To satisfy the above constraint, the predictive time should be selected appropriately. Through a large number of simulation experiments, the suitable ranges of the predictive time under different commands are given. All closed-loop signals are guaranteed to be bounded. And simulation results verify the effectiveness of the proposed controller.

Control of Thermal Power Plant Combustion Distribution Using Extremum Seeking

High demands for increasing robustness, safety, and efficiency in thermal power plants are the main motivation behind ongoing attempts to optimize combustion. This paper presents a study of modeling and control of the combustion process in a tangentially fired pulverized-coal boiler. It proposes an approach to flame geometry and position control by means of reallocation of firing. Such control ensures flame focus maintenance away from the walls of the boiler, and thus prevents many unwanted by-products of combustion. In addition, uniform heat dissipation over mills enhances the energy efficiency and reliability of the boiler. First, experimental data obtained from the 350-MW boiler of the Nikola Tesla power plant, Serbia, are analyzed in detail. This results in a model identification procedure using an adaptive parameter estimation method. Second, constrained multivariate extremum seeking (ES) is proposed in this paper, to optimally tune boiler operation in order to maintain the desired flame configuration in the furnace. Finally, the effectiveness of the ES adaptive controller in the presence of disturbances is demonstrated through simulations performed on the experimentally identified model of the boiler.

Probability hypothesis density filter with adaptive parameter estimation for tracking multiple maneuvering targets

The probability hypothesis density (PHD) filter has been recognized as a promising technique for tracking an unknown number of targets. The performance of the PHD filter, however, is sensitive to the available knowledge on model parameters such as the measurement noise variance and those associated with the changes in the maneuvering target trajectories. If these parameters are unknown in advance, the tracking performance may degrade greatly. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the PHD filter so that the model parameters, which may be static and/or time-varying, can be estimated jointly with target states. The resulting APE-PHD algorithm is implemented using the particle filter (PF), which leads to the PF-APE-PHD filter. Simulations show that the newly proposed algorithm can correctly identify the unknown measurement noise variances, and it is capable of tracking multiple maneuvering targets with abrupt changing parameters in a more robust manner, compared to the multi-model approaches.

Modified reconstruction algorithm based on space-time adaptive processing for multichannel synthetic aperture radar systems in azimuth

A spectrum reconstruction algorithm based on space–time adaptive processing (STAP) can effectively suppress azimuth ambiguity for multichannel synthetic aperture radar (SAR) systems in azimuth. However, the traditional STAP-based reconstruction approach has to estimate the covariance matrix and calculate matrix inversion (MI) for each Doppler frequency bin, which will result in a very large computational load. In addition, the traditional STAP-based approach has to know the exact platform velocity, pulse repetition frequency, and array configuration. Errors involving these parameters will significantly degrade the performance of ambiguity suppression. A modified STAP-based approach to solve these problems is presented. The traditional array steering vectors and corresponding covariance matrices are Doppler-variant in the range-Doppler domain. After preprocessing by a proposed phase compensation method, they would be independent of Doppler bins. Therefore, the modified STAP-based approach needs to estimate the covariance matrix and calculate MI only once. The computation load could be greatly reduced. Moreover, by combining the reconstruction method and a proposed adaptive parameter estimation method, the modified method is able to successfully achieve multichannel SAR signal reconstruction and suppress azimuth ambiguity without knowing the above parameters. Theoretical analysis and experiments showed the simplicity and efficiency of the proposed methods.

Adaptive parameter particle CBMeMBer tracker for multiple maneuvering target tracking

Cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter has been demonstrated as a promising algorithm for multi-target tracking, and the multi-model (MM) method has been incorporated into the CBMeMBer filter to solve the problem of multiple maneuvering target tracking. However, it is difficult to construct a proper set of models due to the unknown maneuvering parameters of the targets. Moreover, the number of models may increase exponentially if more unknown parameters have to be taken into account to match the target motion modes, which may lead to prohibitive computational complexity. To address this aspect, this paper proposes to incorporate the adaptive parameter estimation (APE) method in the framework of CBMeMBer filters, so that the model with unknown maneuvering parameter can be modified adaptively by using the selected parameter particles. Moreover, a particle labeling technique is introduced in the proposed algorithm in order to obtain the individual target track, which results in the adaptive parameter particle filter CBMeMBer (APPF-CBMeMBer) tracker. Simulation results show that the proposed algorithm can effectively track multiple maneuvering targets with abruptly changing parameters and exhibit better robustness than those of the well-known MM-based approaches.