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The purpose of this paper is to deal with the anomaly detection problem in multi-unmanned aerial vehicles (UAVs) formation that can be transformed to identify some unknown parameters; a more general nonlinear dynamical model for each UAV is considered to include two terms. Due to an unknown parameter corresponding to the normal or abnormal state for each UAV, the bias-compensated approach is proposed to obtain the unbiased parameter estimation. Meanwhile, the biased error and accuracy analysis are also given in case of strict statistical description of the uncertainty or white noise. To relax this strict statistical description on external noise, an analytic center approach is proposed to identify the unknown parameters in presence of bounded noise, such that two inner and outer ellipsoidal approximations are constructed to include the membership set. To be precise, this paper is regarded as one extension and summary for the author’s previous research on the anomaly detection in multi-UAV formation. Finally, one simulation example is given to confirm the theoretical results.

Firstly, one extended nonlinear relation is constructed to embody the mutual relationship of UAVs. Secondly, to obtain the unbiased parameter estimations, the bias-compensated approach is applied to achieve it under the condition of white noise. Thirdly, in case of unknown but bounded noise, an analytic center approach is proposed to deal with this special case. Without loss of generality, the author thinks this paper can be used as one extension and summary for research on multi-UAVs formation anomaly detection.

An anomaly detection problem in multi-UAVs formation can be transformed into a problem of nonlinear system identification, and in modeling the nonlinear dynamical model for each UAV, two terms are considered simultaneously to embody the mutual relationships with other nearest UAV.

To the best knowledge of the authors, this problem of the anomaly detection problem in multi-UAVs formation is proposed by the authors’ previous work, and the problem of multi-UAVs formation anomaly detection can be transferred into one problem of parameter identification. In case of unknown but bounded noise, an analytic center approach is proposed to identify the unknown parameters, which correspond to achieve the goal of the anomaly detection.

This paper is concerned with an online parameter estimation algorithm for nonlinear uncertain time‐varying systems for which no stochastic information is available.

The estimation procedure, called nonlinear learning rate adaptation (NLRA), computes an individual adaptive learning rate for each parameter instead of using a single adaptive learning rate for all the parameters as done in stochastic approximation, each individual learning rate being controlled by a meta‐learning rate rule for the sake of minimizing the measurement prediction error. The method does not require stochastic information about the system model and the measurement noise covariance matrices contrarily to the Kalman filtering. Numerical results about aircraft navigation trajectory tracking show that the method is able to estimate reliably time‐varying parameters even in presence of measurement noise.

The proposed algorithm is practically insensitive to changes in the meta‐learning rate. Therefore, the performance of the method is stable with respect to the tuning parameter of the algorithm.

The proposed NLRA method may be adopted for recursive parameter estimation of uncertain systems when no stochastic information is available. It may also be used for process regulation and dynamic system stabilization in feedback control applications.

Provides a method for fast and practical computation of parameter estimates without requiring to know the model and measurement noise covariance matrices contrarily to existing stochastic estimation methods.

The global slack hypothesis is central to the discussion of the trade-offs that monetary policy faces in an increasingly more integrated world. The workhorse New Open Economy Macro (NOEM) model of Martínez-García and Wynne (2010), which fleshes out this hypothesis, shows how expected future local inflation and global slack affect current local inflation. In this chapter, I propose the use of the orthogonalization method of Aoki (1981) and Fukuda (1993) on the workhorse NOEM model to further decompose local inflation into a global component and an inflation differential component. I find that the log-linearized rational expectations model of Martínez-García and Wynne (2010) can be solved with two separate subsystems to describe each of these two components of inflation.

I estimate the full NOEM model with Bayesian techniques using data for the United States and an aggregate of its 38 largest trading partners from 1980Q1 until 2011Q4. The Bayesian estimation recognizes the parameter uncertainty surrounding the model and calls on the data (inflation and output) to discipline the parameterization. My findings show that the strength of the international spillovers through trade – even in the absence of common shocks – is reflected in the response of global inflation and is incorporated into local inflation dynamics. Furthermore, I find that key features of the economy can have different impacts on global and local inflation – in particular, I show that the parameters that determine the import share and the price-elasticity of trade matter in explaining the inflation differential component but not the global component of inflation.

To find martingale theory and methods of parameter vector estimation of multidimensional linear control system in dynamic system.

In parameter vector estimation of dynamic system, due to the limitation of conventional methods, the authors study the iterative formula and consistency of parameter vector estimation using the theory and methods of martingale, and extend its application field.

Some conditions of strongly consistent estimation and iterative formula are obtained. And the consistency of parameter vector estimation in multidimensional linear control system is discussed.

Solved some practical problems of parameter vector estimation in dynamic system.

The paper solve the accuracy problem of parameter vector estimation of multidimensional linear control system in dynamic system and extend parameter vector estimation application field.

The main purpose of this paper is to estimate the resistance and inductor in the RL electrical circuit when these are unavailable or missing data that it is a concern in electrical engineering. The input voltage is assumed to be corrupted by the noise and the current is observed at discrete time points.

The authors propose a computationally efficient framework for parameters estimation using least square estimator and Bayesian Monte Carlo scheme.

The explicit formulas for least square estimator are derived and the strong consistency of resistance estimator is verified when inductor is a known parameter, then Bayesian estimation of parameters governed by using Markov chain Monte Carlo methods. The applicability of the results is demonstrated by using numerical examples. Several numerical results and figures are presented via Matlab and R programming to illustrate the performance of the estimators.

The paper can be used in various types of electrical engineering real time projects. The projects include electrical circuits, electrical machines theory and drives, especially when the parameters are uncertain that it is a worry in electrical engineering.

To the author's best knowledge, least square and Bayesian estimation of resistance and inductor have not been studied before. The proposed model is nonlinear with respect to inductor (L); therefore the present work has fundamental difference in comparison with the similar models.

The purpose of this paper is to describe the longitudinal aerodynamic characterization of an unmanned cropped delta configuration from real flight data. In order to perform this task an unmanned configuration with cropped delta planform and rectangular cross-section has been designed, fabricated, instrumented and flight tested at flight laboratory in Indian Institute of Technology Kanpur (IITK), India.

As a part of flight test program a real flight database, through various maneuvers, have been generated for the designed unmanned configuration. A dedicated flight data acquisition system, capable of onboard logging and telemetry to ground station, has been used to record the flight data during these flight test experiments. In order to identify the systematic errors in the measurements, the generated flight data has been processed through data compatibility check.

It is observed from the flight path reconstruction that the obtained biases are negligible and the scale factors are almost close to unity. The linear aerodynamic model along with maximum likelihood and least-square methods have been used to perform the parameter estimation from the obtained compatible flight data. The lower values of Cramer-Rao bounds obtained for various parameters has shown significant confidence in the estimated parameters using maximum likelihood method. In order to validate the aerodynamic model used and to increase the confidence in the estimated parameters a proof-of-match exercise has been carried out.

The entire work presented is original and all the experiments have been carried out in Flight laboratory of IITK.

This study aims to propose a novel method based on model learning with sparsity inducing norms for estimating dynamic gravity terms of the serial manipulators. This method is realized by operating the robot, acquiring data and filtering the features in signal acquisition to adapt to the dynamic gravity parameters.

The core principle of the method is to analyze the dictionary composition of the basis function of the model based on the dynamic equation and the Jacobian matrix of an arm. According to the structure of the basis function and the sparsity of the features, combined with joint-angle and driving-torque data acquisition, the effective features of dynamic gravity parameters are screened out using L1-norm optimization and learning algorithms.

The theoretical analysis revealed that training data obtained based on joint angles and driving torques could rapidly update dynamic gravity parameters. The simulation experiment was carried out by using the publicly available robot model and compared with the previous disassembly method to evaluate the feasibility and performance. The real 7-degree of freedom (DOF) industrial manipulator was used to further discuss the effects of the feature selection. The results show that this estimation method can be fully operational and efficient in industrial applications.

This approach is applicable to most serial robots with multi-DOF and the dynamic gravity parameters of the robot are estimated through learning and optimization. The method does not require prior knowledge of the robot arm structure and only requires joint-angle and driving-torque data acquisition under low-speed motion. Furthermore, as it is a data-driven-based method, it can be applied to gravity parameters updating.

Different from previous general robot dynamic modelling methods, the sparsity of the analytical form of dynamic equations was exploited and model learning was formulated as a convex optimization problem to achieve effective gravity parameters screening. The novelty of this estimation approach is that the method does not only require any prior knowledge but also does not require a specifically designed trajectory. Thus, this method can avoid the laborious work of parameter calibration and the induced modelling errors. By using a data-driven learning approach, the new parameter updating process can be completed conveniently when the robot carries additional mass or the end-effector changes for different tasks.

The purpose of this paper is to propose an efficient method for detection and modification of erroneous branch parameters in real time power system simulators. The aim of the proposed method is to minimize the sum of squared errors (SSE) due to mismatches between simulation results and corresponding field measurements. Assuming that the network configuration is known, a limited number of erroneous branch parameters will be detected and corrected in an optimization procedure.

Proposing a novel formulation that utilizes network voltages and last modified admittance matrix of the simulation model, suspected branch parameters are identified. These parameters are more likely to be responsible for large values of SSE. Utilizing a Gauss-Newton (GN) optimization method, detected parameters will be modified in order to minimize the value of SSE. Required sensitivities in optimization procedure will be calculated numerically by the real time simulator. In addition, by implementing an efficient orthogonalization method, the more effective parameter will be selected among a set of correlated parameters to avoid singularity problems.

Unlike state estimation-based methods, the proposed method does not need the mathematical functions of measurements to simulation model parameters. The method can enhance other parameter estimation methods that are based on state estimation. Simulation results demonstrate the high efficiency of the proposed optimization method.

Incorrect branch parameter detection and correction procedures are investigated in real time simulators.

The purpose of this paper is to compare the performances of np-VP control chart with estimated parameter to the np-VP control chart with known parameter using average time-to-signal (ATS), standard deviation of the time-to-signal (SDTS), and average number of observations to signal (ANOS) as performance measures.

The approach used in this study is probabilistic in which the expected values of performance measures are calculated using probabilities of different estimators used to estimate process parameter.

Numerical results indicate different performances for the np-VP control chart in known and estimated parameter cases. It is obvious that when process parameter is not known and is estimated using Phase I data, the chart does not perform as user expects. To tackle this issue, optimal Phase I estimation scenarios are recommended to obtain the best performance from the chart in the parameter estimation case in terms of performance measures.

This research adds to the body of knowledge in quality control of process monitoring systems. This paper may be of particular interest to practitioners of quality systems in factories where products are monitored to reduce the number of defectives and np chart parameter needs to be estimated.

The originality of this paper lies within the context in which an adaptive np control chart is studied and the process parameter unlike previous studies is assumed unknown. Although other types of control charts have been studied when process parameter is unknown but this is the first time that adaptive np chart performance with estimated process parameter is studied.

The purpose of this paper is to focus on perfect trajectory tracking control of 2 DOF non‐holonomic mobile robots in which the guidance and control commands are imposed through independent driver wheels. Model‐based nonlinear controllers for these robots with unknown parameters require estimation of a specified set of the robot parameters. The effects of the proposed model dynamics in both local and global coordinate systems are fully examined on the parameter estimation and tracking performance.

Design of suitable feedback linearization (FL) controllers for trajectory tracking control of wheeled mobile robots (WMRs) is first reviewed. A FL controller whose parameters are tuned using fuzzy computations (fuzzy if‐then rules) is then developed. In the line of the other contributions of the paper, a pure fuzzy controller that is merely based on fuzzy if‐then rules is proposed to trajectory tracking control of the mobile robots.

Use of global dynamics for designing a suitable FL control system leads to a perfect compensation for initial off‐tracks. Furthermore, the estimated parameters are unbiased because the corresponding regressor/signal matrix indicates a high rank of persistent excitation. Fuzzy tuning of the controller instead of keeping the gains fixed makes the overall system more robust against measurement noises while upper bounds and fluctuations of the input torques are both remarkably reduced. The pure fuzzy controller is naturally independent of the robot dynamics and therefore, the necessity of parameter estimation algorithm is removed.

The paper provides some new nonlinear controllers for WMRs, in order to make perfect trajectory tracking and initial off‐tracks compensation.