This paper considers the problem of load frequency control (LFC) of power systems when the states of the system are not available for implementation and in the presence of unknown disturbances. Conventional proportional observer (PO) fails to solve this problem unless the disturbances are known or can be modeled. Therefore, we provide a detailed analysis of the generalized proportional integral observer (GPIO) for load frequency control of a single area power system. This study demonstrates that the proposed observer can reliably be used. Since the suggested observer is able to estimate both the states and unknown disturbances, it can be integrated in LFC with disturbance accommodation to ensure system stability and satisfy specified performance measures. Numerical examples are given to illustrate the advantage of GPI observer-based LFC for a single area power system model.

This paper considers the problem of positive stabilization and tracking of linear continuous-time singular systems. First, preliminary results on singular systems are provided. Then, the class of positive singular systems is defined through its equivalent input derivative systems. An elimination procedure for input derivatives is given, which transfers the derivative terms from the state equation to the output equation allowing stabilization by state feedback to be performed using its standard representation. It is also shown that the positive stabilization can be achieved by proportional derivative (PD) state feedback by two steps of normalization and subsequent stabilization using LMI. Finally, the proportional integral (PI) feedback is considered for tracking design of positive systems. Numerical examples are included to support the theoretical results.

This paper introduces a subclass of positive systems involving input derivatives, which we formally define it as input derivative positive systems. Due to the presence of input derivatives, we provide an algebraic transformation to eliminate the derivative inputs to accommodate the process of stabilization by state feedback. This elimination transfers the input derivative in the output equation, which does not interfere with the design process. Stabilization of input derivative positive systems is performed through its equivalent transformed positive systems in standard form using LMI. To take advantage of this stabilization process, we utilize it for stabilization of positive singular systems. Consequently, we analyze singular systems and its equivalent transformations, which admit derivative input. Thus, algebraic transformation is employed to eliminate these derivative inputs. Finally, we establish the connection between stabilization of positive singular systems and stabilization of input derivative systems by a modified LMI. Numerical examples are included to support the theoretical result.

The rapid advancement of automatic control technology has sparked significant interest among researchers in creating more reliable and simplified models of unmanned aerial vehicles (UAVs). This interest is motivated by the need to enhance the performance and resilience of these systems in challenging conditions, such as wind gusts and adverse weather. This paper presents novel strategies for enhancing the resilience of unmanned aerial systems (UAS) with fault-tolerant control (FTC) by learning-oriented control and a constructive fault estimation with Proportional-Integral (PI) observer. The learning-control is deep-deterministic policy gradient (DDPG) which is trained in only one state but used beyond its environment for other states to control. The faults are designed in three divergent conditions and the augmented PI observer is responsible in capturing them. The success of estimating the faults is used for this FTC to compensate the faulty system with learning-oriented control as the advancement of the FTC. The proposed approach has the potential to enhance the performance and resilience of UAVs, thus contributing to the development of more robust and reliable systems.

This paper proposes a nonnegative matrix factorization-based approach for positive system identification. Direct application of subspace system identification (SSID) to collected data of positive system does not guarantee the positivity of the resulting state space parameters. The proposed method in this paper offers a procedure to obtain the state-space representation of a positive system by applying nonnegative matrix factorization (NMF) to the Hankel matrix. Simulation results show that positivity and stability remain with this approach.

This paper considers the data-driven control problem for the important class of positive systems. Due to the fact that such systems appear in diverse application areas whereby data are collected for identification and control, they are qualified candidates for data-driven control. Using fundamental concept of persistently exciting data and formulas for data-driven control, we provide an initial attempt to solve the positive stabilization of linear systems by input-output data. The result is useful in the sense that no subspace identification is required to obtain system matrices. With the aid of available results of positive systems and recent development of data-driven analysis of dynamic system, we formulate and solve data-driven positive stabilization using data-dependent linear matrix inequalities. The structural constraint of positivity makes the task challenging. Nevertheless it is possible to use this framework for positive output feedback and robust optimal control problems as well.

This paper considers the design of an integrated observer structure termed as Proportional Integral Fading Unknown Input Observer (PIFUIO). The advantages of PIO and UIO observers are used in robust fault detection. The UIO decouples the unknown input disturbance while PIO allows to estimates the faults. It is shown that the fading term of this observer plays a distinct role in reliable estimation of faults decoupled from the unknown disturbance or vice versa. The robust detection of sensor fault is also considered with the presence of unknown inputs. Indirect and direct design procedures for sensor fault detection are provided. Numerical examples are included to illustrate the advantage of PIFUIO.

This paper considers robust fault detection in distributed consensus systems with agents that are subjected to simultaneous faults and unknown disturbances. First, a distributed system model is introduced so that the relationship between the agents and the unknown disturbances and the faults can be precisely captured. In this model, the local interactions are captured through an undirected graph and the collective dynamics are represented by a positive dynamic system. Next, necessary and sufficient conditions to decouple the unknown disturbances from the agents residual generators are derived in terms of an observer gain matrices. Specifically, it is shown that the problem of discriminating between unknown disturbances and faults in a distributed system under consensus dynamics can be reduced to the problem of determining a set of constraints on the spectrum of the residual generator coefficient matrices. The approach is illustrated through an example.

In this paper, a novel type of equalization technique, called "double decision feedback equalizer" (DDFE) is applied to multilevel turbo codes (MLTC) to improve BER performance, and the entire system is called "multilevel turbo equalization" (MLTEQ). The parallel input data sequences are encoded at each level by turbo encoders, and then the coded data sequences are mapped to M-PSK signals, where M depends on the level quantity. After these modulated signals are passed through severe ISI and fading channels, the corrupted signals are equalized by an innovative iterative double decision feedback equalization technique, which uses an adaptive LMS algorithm to estimate the channel taps with a new equalization design. Then, the equalized signals are sent to the turbo decoders. The performance of new proposed MLTEQ system is investigated under non-frequency selective fading and frequency selective fading channels. As an application, two level turbo codes are simulated using 4-PSK modulation over AWGN, Rician, Rayleigh and Proakis B channels with 800 frame sizes. The simulation results show that satisfactory performance improvement is obtained with the proposed system over severe ISI and non-frequency selective fading channels.

Contactless electrical conductivity imaging (CECI) collects magnetic field measurements of the induced currents from a biological subject. Because magnetic induction strength is highly dependent on the distance between the source and the measurements, information about the deeper conductivity variations are highly vulnerable to measurement noise. In this study, a novel form of the bounded data uncertainties (BDU) algorithm is designed to improve the depth resolution of CECI. The column weighted bounded data uncertainties (CWBDU) algorithm defines column-specific regularization parameters for ill-posed problems, without increasing the computational complexity. The performance of CWBDU is tested on a simulated CECI setup, and results are presented with Tikhonov regularization, Moore Penrose inversion, and BDU results for comparison. The mean square error of the images are at least 1.25 dB better than the mentioned algorithm results for all our test cases. In general, CWBDU seems to be promising tool for other ill-posed problems, such as electrical impedance tomography, and eddy current imaging.

This paper deals with the design of a Luenberger-like observers in a class of nonlinear differential-algebraic systems (DAS) described by the so-called semi-explicit forms with the differential variables being coupled with algebraic variables. The key point of designing the observer for the DAS is the reconstruction of the algebraic variables because its distribution matrix is singular. The reconstruction consists of a serial elementary matrices followed by differentiation such that the algebraic variables can be directly estimated in the observer. The stability of the proposed observer is proved and an illustrative example and simulations are given to describe the design of the observer.

In this paper we propose a method for tracking of partials in music signals using Kalman filtering. Our observations are frequency and amplitude of peaks from spectral representation of short segments of music signal. These peaks are detected using a novel technique. We also introduce a set of state-space models for evolution of partials in time. Parameters of these models can be estimated by statistical analysis of a large database of musical sounds. Since Kalman filter is sensitive to the accuracy of these parameters, we propose some robust filters that can improve the performance of our tracker and discuss the possibility of using them.

Diffuse optical tomography (DOT) is an emerging medical imaging modality offering the possibility of recovering the distribution of optical absorption and scattering coefficients, and from them localize metabolic parameters such as oxygen saturation or neural activity, using nonionizing near-IR light. However it requires solving a badly ill-posed inverse problem. In this article we discuss the formulation of the DOT problem that uses an efficient interior-point-type optimization algorithm to find a DOT inverse solution in an admissible solution scenario. We present simulation results verifying the effectiveness of the approach.

Robust control has long been the purview of quantitative linear control techniques, while qualitative symbolic control has been deemed more suitable to obtaining complex control objectives that require only low-output precision. The intelligent techniques of fuzzy control have, however, shown promise in obtaining results comparable to those obtained from H/sub /spl infin// and H/sub 2/ robust control techniques. Often though, these fuzzy control techniques ignore the original intent of fuzzy logic: implementation of symbolic linguistic control laws based on qualitative models of the plant and control behaviors. We show that robust control objectives, even for simple plants, can be achieved by first developing qualitative behaviors that stabilize the plant and then superimposing tracking behaviors that achieve control objectives. Specifically, by superimposing qualitative stability and tracking behaviors, we can achieve robustness and tracking stability comparable to the best published linear compensators for the 1992 ACC robust control benchmark.

It is shown how a Kalman filter can be applied to the problem of adaptive deconvolution and system identification for a non-Gaussian white noise driven linear, nonminimum phase finite impulse response (FIR) system. The adaptive scheme is, in fact, a blind equalization (deconvolution) scheme, based on approximating the FIR system by noncausal autoregressive (AR) models and using higher-order cumulants of the system output. Without prior knowledge about the channel, the filter algorithm leads to faster convergence than other methods, its speed of convergence depending only on the number of data. Theoretical results are given and computer simulations are used to corroborate the theory and to compare the algorithm with the classical steepest descent method.>

The authors consider the problem of reducing the effects of roundoff noise and coefficient sensitivity in digital filters when implemented with fixed point arithmetic. The minimum roundoff noise and minimum sensitivity structures have generally many more coefficients than the other canonical structures. A direct method is provided for constructing a reduced sensitivity and roundoff noise state space realization of a digital filter with a saving of 1/2n(n-1) multiplies over the optimal structures. The method avoids expensive transformation of the original realization to the balanced form as well as iterative algorithms of constrained and unconstrained noise minimization. A systolic array implementation is also presented.>