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This study addresses the critical challenges of voltage stabilization in DC microgrids, where the inherent variability of renewable energy sources significantly complicates reliable operation. The focus is on optimizing the fractional-order proportional-integral (FO-PI) controller using four advanced techniques a whale optimization algorithm (WOA), grey wolf optimizer (GWO), genetic algorithm (GA), and sine cosine algorithm (SCA). Voltage instability poses substantial risks to the reliability and efficiency of DC microgrids, making the optimization of the FO-PI controller an essential task. Through comparative analysis, the study demonstrates that WOA outperforms the other methods, achieving superior voltage stability, resilience, and overall system performance. Notably, WOA achieves the lowest average cost function at 0.0004, compared to 0.892 for GWO, 0.659 for GA, and 0.096 for SCA, showcasing its effectiveness in fine-tuning the controller’s parameters. These findings highlight WOA robustness as a powerful tool for enhancing microgrid performance, especially in voltage regulation. The study underscores WOA potential in ensuring the reliable and efficient integration of renewable energy systems into DC microgrids and lays the groundwork for further research into its application in more complex and dynamic grid scenarios. By optimizing the FO-PI controller, WOA significantly contributes to the long-term stability and efficiency of DC microgrids.

We consider the identification of a nonlinear friction law in a one-dimensional damped wave equation from additional boundary measurements. Well-posedness of the governing semilinear hyperbolic system is established via semigroup theory and contraction arguments. We then investigte the inverse problem of recovering the unknown nonlinear damping law from additional boundary measurements of the pressure drop along the pipe. This coefficient inverse problem is shown to be ill-posed and a variational regularization method is considered for its stable solution. We prove existence of minimizers for the Tikhonov functional and discuss the convergence of the regularized solutions under an approximate source condition. The meaning of this condition and some arguments for its validity are discussed in detail and numerical results are presented for illustration of the theoretical findings.

This thesis focuses on the subject of the accuracy of parameter estimation and system identification techniques. Motivated by a complicated load measurement from NASA Dryden Flight Research Center, advanced system identification techniques are needed. The objective of this problem is to accurately predict the load experienced by the aircraft wing structure during flight determined from a set of calibrated load and gage response relationship. We can then model the problem as a black box input-output system identification from which the system parameter has to be estimated. Traditional LS (Least Square) techniques and the issues of noisy data and model accuracy are addressed. A statistical bound reflecting the change in residual is derived in order to understand the effects of the perturbations on the data. Due to the intrinsic nature of the LS problem, LS solution faces the dilemma of the trade off between model accuracy and noise sensitivity. A method of conflicting performance indices is presented, thus allowing us to improve the noise sensitivity while at the same time configuring the degredation of the model accuracy. SVD techniques for data reduction are studied and the equivalence of the Correspondence Analysis (CA) and Total Least Squares Criteria are proved. We also looked at nonlinear LS problems with NASA F-111 data set as an example. Conventional methods are neither easily applicable nor suitable for the specific load problem since the exact model of the system is unknown. Neural Network (NN) does not require prior information on the model of the system. This robustness motivated us to apply the NN techniques on our load problem. Simulation results for the NN methods used in both the single load and the 'warning signal' problems are both useful and encouraging. The performance of the NN (for single load estimate) is better than the LS approach, whereas no conventional approach was tried for the 'warning signals' problems. The NN design methodology is also presented. The use of SVD, CA and Collinearity Index methods are used to reduce the number of neurons in a layer.

This thesis focuses on the subject of the accuracy of parameter estimation and system identification techniques. Motivated by a complicated load measurement from NASA Dryden Flight Research Center, advanced system identification techniques are needed. The objective of this problem is to accurately predict the load experienced by the aircraft wing structure during flight determined from a set of calibrated load and gage response relationship. We can then model the problem as a black box input-output system identification from which the system parameter has to be estimated. Traditional LS (Least Square) techniques and the issues of noisy data and model accuracy are addressed. A statistical bound reflecting the change in residual is derived in order to understand the effects of the perturbations on the data. Due to the intrinsic nature of the LS problem, LS solution faces the dilemma of the trade off between model accuracy and noise sensitivity. A method of conflicting performance indices is presented, thus allowing us to improve the noise sensitivity while at the same time configuring the degredation of the model accuracy. SVD techniques for data reduction are studied and the equivalence of the Correspondence Analysis (CA) and Total Least Squares Criteria are proved. We also looked at nonlinear LS problems with NASA F-111 data set as an example. Conventional methods are neither easily applicable nor suitable for the specific load problem since the exact model of the system is unknown. Neural Network (NN) does not require prior information on the model of the system. This robustness motivated us to apply the NN techniques on our load problem. Simulation results for the NN methods used in both the single load and the 'warning signal' problems are both useful and encouraging. The performance of the NN (for single load estimate) is better than the LS approach, whereas no conventional approach was tried for the 'warning signals' problems. The NN design methodology is also presented. The use of SVD, CA and Collinearity Index methods are used to reduce the number of neurons in a layer.

This thesis focuses on the subject of the accuracy of parameter estimation and system identification techniques. Motivated by a complicated load measurement from NASA Dryden Flight Research Center, advanced system identification techniques are needed. The objective of this problem is to accurately predict the load experienced by the aircraft wing structure during flight determined from a set of calibrated load and gage response relationship. We can then model the problem as a black box input-output system identification from which the system parameter has to be estimated. Traditional LS (Least Square) techniques and the issues of noisy data and model accuracy are addressed. A statistical bound reflecting the change in residual is derived in order to understand the effects of the perturbations on the data. Due to the intrinsic nature of the LS problem, LS solution faces the dilemma of the trade off between model accuracy and noise sensitivity. A method of conflicting performance indices is presented, thus allowing us to improve the noise sensitivity while at the same time configuring the degredation of the model accuracy. SVD techniques for data reduction are studied and the equivalence of the Correspondence Analysis (CA) and Total Least Squares Criteria are proved. We also looked at nonlinear LS problems with NASA F-111 data set as an example. Conventional methods are neither easily applicable nor suitable for the specific load problem since the exact model of the system is unknown. Neural Network (NN) does not require prior information on the model of the system. This robustness motivated us to apply the NN techniques on our load problem. Simulation results for the NN methods used in both the single load and the 'warning signal' problems are both useful and encouraging. The performance of the NN (for single load estimate) is better than the LS approach, whereas no conventional approach was tried for the 'warning signals' problems. The NN design methodology is also presented. The use of SVD, CA and Collinearity Index methods are used to reduce the number of neurons in a layer.

This thesis focuses on the subject of the accuracy of parameter estimation and system identification techniques. Motivated by a complicated load measurement from NASA Dryden Flight Research Center, advanced system identification techniques are needed. The objective of this problem is to accurately predict the load experienced by the aircraft wing structure during flight determined from a set of calibrated load and gage response relationship. We can then model the problem as a black box input-output system identification from which the system parameter has to be estimated. Traditional LS (Least Square) techniques and the issues of noisy data and model accuracy are addressed. A statistical bound reflecting the change in residual is derived in order to understand the effects of the perturbations on the data. Due to the intrinsic nature of the LS problem, LS solution faces the dilemma of the trade off between model accuracy and noise sensitivity. A method of conflicting performance indices is presented, thus allowing us to improve the noise sensitivity while at the same time configuring the degredation of the model accuracy. SVD techniques for data reduction are studied and the equivalence of the Correspondence Analysis (CA) and Total Least Squares Criteria are proved. We also looked at nonlinear LS problems with NASA F-111 data set as an example. Conventional methods are neither easily applicable nor suitable for the specific load problem since the exact model of the system is unknown. Neural Network (NN) does not require prior information on the model of the system. This robustness motivated us to apply the NN techniques on our load problem. Simulation results for the NN methods used in both the single load and the 'warning signal' problems are both useful and encouraging. The performance of the NN (for single load estimate) is better than the LS approach, whereas no conventional approach was tried for the 'warning signals' problems. The NN design methodology is also presented. The use of SVD, CA and Collinearity Index methods are used to reduce the number of neurons in a layer.

This thesis focuses on the subject of the accuracy of parameter estimation and system identification techniques. Motivated by a complicated load measurement from NASA Dryden Flight Research Center, advanced system identification techniques are needed. The objective of this problem is to accurately predict the load experienced by the aircraft wing structure during flight determined from a set of calibrated load and gage response relationship. We can then model the problem as a black box input-output system identification from which the system parameter has to be estimated. Traditional LS (Least Square) techniques and the issues of noisy data and model accuracy are addressed. A statistical bound reflecting the change in residual is derived in order to understand the effects of the perturbations on the data. Due to the intrinsic nature of the LS problem, LS solution faces the dilemma of the trade off between model accuracy and noise sensitivity. A method of conflicting performance indices is presented, thus allowing us to improve the noise sensitivity while at the same time configuring the degredation of the model accuracy. SVD techniques for data reduction are studied and the equivalence of the Correspondence Analysis (CA) and Total Least Squares Criteria are proved. We also looked at nonlinear LS problems with NASA F-111 data set as an example. Conventional methods are neither easily applicable nor suitable for the specific load problem since the exact model of the system is unknown. Neural Network (NN) does not require prior information on the model of the system. This robustness motivated us to apply the NN techniques on our load problem. Simulation results for the NN methods used in both the single load and the 'warning signal' problems are both useful and encouraging. The performance of the NN (for single load estimate) is better than the LS approach, whereas no conventional approach was tried for the 'warning signals' problems. The NN design methodology is also presented. The use of SVD, CA and Collinearity Index methods are used to reduce the number of neurons in a layer.

This thesis focuses on the subject of the accuracy of parameter estimation and system identification techniques. Motivated by a complicated load measurement from NASA Dryden Flight Research Center, advanced system identification techniques are needed. The objective of this problem is to accurately predict the load experienced by the aircraft wing structure during flight determined from a set of calibrated load and gage response relationship. We can then model the problem as a black box input-output system identification from which the system parameter has to be estimated. Traditional LS (Least Square) techniques and the issues of noisy data and model accuracy are addressed. A statistical bound reflecting the change in residual is derived in order to understand the effects of the perturbations on the data. Due to the intrinsic nature of the LS problem, LS solution faces the dilemma of the trade off between model accuracy and noise sensitivity. A method of conflicting performance indices is presented, thus allowing us to improve the noise sensitivity while at the same time configuring the degredation of the model accuracy. SVD techniques for data reduction are studied and the equivalence of the Correspondence Analysis (CA) and Total Least Squares Criteria are proved. We also looked at nonlinear LS problems with NASA F-111 data set as an example. Conventional methods are neither easily applicable nor suitable for the specific load problem since the exact model of the system is unknown. Neural Network (NN) does not require prior information on the model of the system. This robustness motivated us to apply the NN techniques on our load problem. Simulation results for the NN methods used in both the single load and the 'warning signal' problems are both useful and encouraging. The performance of the NN (for single load estimate) is better than the LS approach, whereas no conventional approach was tried for the 'warning signals' problems. The NN design methodology is also presented. The use of SVD, CA and Collinearity Index methods are used to reduce the number of neurons in a layer.

A unified approach is introduced using system realization theory to derive and correlate modal parameter identification methods for flexible structures. Several different time-domain and frequency-domain methods are analyzed and treated. A basic mathematical foundation is presented which provides insight into the field of modal parameter identification for comparison and evaluation. The relation among various existing methods is established and discussed. This report serves as a starting point to stimulate additional research towards the unification of the many possible approaches for modal parameter identification.

Identification of suitable or optimum operating parameters on a CNC machine is a non-trivial task. Especially when the material of the component changes, operating parameters need to be suitably varied. In this paper, a PC- based vision system is presented for the automatic identification of component material and appropriate selection of operating parameters. The objective of this work is to develop a support system to aid the operator in quick identification of machining parameters

There are three particularly notable accomplishments during the present reporting period. The first is the development of a substantial generalization of our SCORE algorithm for blind adaptive spatial filtering to the Programmable Canonical Correlation Analyzer (PCCA) which can exploit any of a number of signal properties to distinguish between signals of interest (to be beamformed on) and signals not of interest (to be nulled out). The second is a new algorithm for blind adaptive channel equalization for PAM and digital QAM signals, and for either single or multiple channels. The third notable achievement is the completion of the edited volume Cyclostationarity in Communications and Signal Processing

The cyclostationarity property of communications and telemetry signals enables the generation of spectral lines with appropriate nonlinear transformations and renders fluctuations in distinct spectral bands statistically dependent. The frequencies at which spectral lines can be generated are directly related to the separations between dependent spectral bands, which in turn are directly related to carrier frequencies, keying rates, pulse rates, and so on, in the signal. These inherent properties of cyclostationary signals can be exploited to great advantage for numerous tasks in signal processing. The objectives of the research being conducted are to investigate new cyclostationarity-exploiting methods for (1) signal-selective high-resolution direction finding using sensor arrays, (2) selectively locating emitters by time difference and frequency difference measurement with one or more pairs of sensors, (3) identifying the kernels in the Volterra series representation of nonlinear time-invariant and multiply-periodic systems.

In a high performance aircraft in Mach number, angle of attack and altitude can cause a large variation in the short-period transfer function. To provide the pilot with a constant pitch rate control characteristic an airborne computer, with inputs of elevator deflection angle and pitch rate is used to identify and track changes in the elevator effectiveness. Simulation with an aircraft whose elevator effectiveness varied over a range of 240:1 showed that the desired loop gain was maintained within a factor of two for both pilot command inputs and for random wind gust disturbances of root-mean-square magnitude 20 ft/sec. (Author)

Often in process simulation studies and process control it is desirable to have an approximate transfer function that could be used for dynamic simulation via analog computer or for feedforward control algorithms. The scope of the work is concerned with developing a general analytical technique that can be easily implemented in obtaining approximate transfer functions for distributive parameter systems of the convective type, where the spacial dependence of the dependent variable is of little interest from a control viewpoint. This technique relies on a distributed parameter model that is transformed into ordinary differential equations in the frequency domain. These equations are then analyzed in the frequency domain using well known classical techniques. The frequency solution of the ordinary differential equation provides the necessary data to carry out regressions on the parameters of the postulated transfer function.

This paper describes the Hebert-Mackin Parameter Identification Method (HMPIM). This methodology is applicable to testing both hardware and software and enables identification of system or algorithm performance modeling parameters through minimal dynamic stimulation of the hardware or software. Exposing hardware to extensive operation and testing to determine salient system or component level modeling parameters is both costly, time consuming, and potentially risky. Classical test waveforms such as steps, ramps, or sinusoids expose the asset being tested to continuous probing and shaking and each test by itself does not drive out the entire set of essential modeling parameters. The HMPIM, utilizing persistent spectral excitation and data processing, allows the analyst or modeler to determine all the essential system performance and modeling parameters with a single 5 or 10 second excitation of the hardware or software algorithm.