The classical eigenvalue assignment problem involves obtaining static feedback matrices that assign the closed-loop eigenvalues to certain desired locations. The problem of eigenvalue assignment by means of output feedback recently was investigated in the framework of second-order mechanical systems with the aim of minimizing the number of independent actuators and sensors needed. The current work continues this investigation and provides additional improvements: the number of sensors and actuators required to implement the control law is reduced by one, and the design is done with receptance-based measurements, avoiding the need to know the acceleration, velocity and position matrices of the system.

We consider the use of linear multivariable output feedback control to achieve a nonovershooting step response from a linear time-invariant system. A method is given for designing a linear time invariant output feedback controller to asymptotically track a constant step reference with zero overshoot and arbitrarily small rise time, from a known initial state. Earlier work using state feedback control is here adapted to the use of output feedback control, in which only a subset of the system states are required to be available for use in controller design. The domain of initial states from which the controller will achieve a nonovershooting response is precisely identified.

We consider the analytical control design for a pair of switched linear multiple-input multiple-output (MIMO) systems that are subject to arbitrary switching signals. A state feedback controller design method is proposed to obtain an eigenstructure assignment that ensures that the closed-loop switched system is globally asymptotically stable, and the outputs achieve the non-overshooting tracking of a step reference. Our analysis indicates whether non-overshooting or even monotonic tracking is achievable for the given system and considered outputs and provides a choice of possible eigenstructures to be assigned to the constituent subsystems. We derive a structural condition that verifies the feasibility of the chosen assignment. A constructive algorithm to obtain suitable feedback matrices is provided, and the method is illustrated with numerical examples.

The stability of interconnected linear time-invariant systems using singular values and the small gain theorem has been studied for many decades. The methods of mu-analysis and synthesis has been extensively developed to provide robustness guarantees for a plant subject to structured perturbations, with components in the structured perturbation satisfying a bound on their largest singular value. Recent results on phase-based stability measures have led to a counterpart of the small gain theorem, known as the small phase theorem. To date these phase-based methods have only been used to provide stability robustness measures for unstructured perturbations. In this paper, we define a phase robustness metric for multivariable linear time-invariant systems in the presence of a structured perturbation. We demonstrate its relationship to a certain class of multiplier functions for integral quadratic constraints, and show that a upper bound can be calculated via a linear matrix inequality problem. When combined with robustness measures from the small gain theorem, the new methods are able provide less conservative robustness metrics than can be obtained via conventional mu-analysis methods.

The classical eigenvalue assignment problem involves obtaining static feedback matrices that will assign the closed-loop eigenvalues to certain desired locations. State-feedback solutions require full state measurement, while output-feedback solutions use sensor measurements of the system outputs. For physical systems some degrees of freedom may be inaccessible to sensing, and others may be inaccessible to actuation. This motivates the search for solutions that minimize the numbers of independent actuators and sensors employed. The current paper investigates the eigenvalue assignment problem within the framework of second-order mechanical systems. We use output-feedback control-structures to obtain lower bounds for the number of position and velocity sensors and actuators required. For a given actuator/sensor configuration, we obtain a parametric formula for the output feedback matrices that assign a certain desired set of closed-loop eigenvalues, and these formulae are used to investigate some optimization problems. Separate results are obtained for the case when accelerometer measurements are additionally assumed to be available. Finally an example shows the methods can provide eigenvalue assignment with fewer sensors and actuators than some alternative methods from the literature.

Optimizing wind turbine performance involves maximizing or regulating power generation while minimizing fatigue load on the tower structure, blades, and rotor. In this article, we explore the application of a novel turbine control methodology referred to as nonlinear output regulation (NOR) for improving turbine control performance. NOR constructs a torque balance equation under which the closed loops follow desired stable dynamics, and solves it for the generator torque in partial load operation and for the blade pitch angles in full load operation, in a unified manner across both operating regions. The regulation relies on an estimate of rotor-effective wind speed (REWS). We consider estimation based on the turbine's SCADA, in particular the immersion and invariance (I&I) estimator, as well as lidar. Furthermore, we propose to use an average of the I&I and lidar estimates, where the lidar buffer time is chosen to compensate I&I's delay, to obtain a real-time low-variation estimate. The performance of the NOR controller with the averaged I&I and lidar estimate is compared against a state-of-the-art baseline reference controller known as ROSCO in both its standard feedback-only configuration as well as an existing lidar-assisted control (LAC) version of ROSCO that uses a pitch feedforward. NOR, with the averaged I&I and lidar estimate, matches this lidar-assisted ROSCO rotor speed tracking improvements over feedback-only ROSCO, but also significantly reduces fatigue loads and actuator usage. In particular, the blade flapwise damage equivalent loads (DELs) reduction corresponds to a doubled lifespan, and pitch rate is reduced by more than a third. The reductions are achieved without sacrificing power generation.

Recently, there has been great interest in the development of stability criteria for grid-connected power converters and generators using frequency-domain impedance models. We consider two recently introduced stability analysis methods and compare their small-signal stability performance for a DFIG wind farm connected to a series-compensated grid. The first method, known as the Low-Complexity Global Nyquist Condition, uses Gershgorin disks derived from the loop gain matrix to provide a stability criterion. The second method, introduced by the authors, uses a combined matrix gain and phase stability analysis. The two methods are compared by using them to predict the onset of instability in a series-compensated wind farm under a tripped line. The results reveal that the gain/phase based approach achieves less conservatism than the Nyquist-based method.

We consider the analytical controller design for a class of switched linear systems. Under suitable system assumptions, we propose a method using eigenstructure assignment that guarantees the closed-loop switched system is globally asymptotically stable under arbitrary switching signals, and the outputs achieve monotonic step reference tracking from all initial conditions. Additionally, the output signals from both subsystems can be made identical, so the effects of the system switching are not noticeable from the output. A constructive algorithm is provided that yields suitable feedback matrices, and the method is illustrated with a numerical example.

While optimizing wind energy harvesting has always been the principal goal of wind turbine control, extensive research has been conducted on the use of advanced control methodologies aiming to maximize the turbine's power generation while also achieving secondary performance objectives, such as fatigue load minimization. Some recent studies have investigated the application of a classical control methodology known as exact output regulation for improved wind turbine control. This control method is designed to achieve the rejection of known input disturbances, while also ensuring the system output tracks a desired reference signal. These studies all assumed wind preview information from LIDAR measurements was available to support the controller design. In this paper we investigate the use of exact output regulation for turbine control, using wind speed estimates obtained from rotor speed measurements. Simulated performance comparisons conducted over a wide range of mean wind speeds show that the exact output regulation methodology using wind speed estimates derived from rotor measurements can deliver similar reductions in fatigue loads to those obtainable with LIDAR measurements, without compromising power generation.

A novel conception of phase for linear time-invariant multivariable systems was recently introduced. It enables robustness of such systems to be determined in terms of a phase-bounded set of perturbations via a so-called small phase theorem, in analogy to the well-known small gain theorem. However, it requires the system's frequency response to satisfy the relatively strong condition known as "sectoriality", which not all practical systems have. This letter aims to show that if the perturbation is assumed to have a block diagonal structure, a matrix-valued multiplier function can be calculated that can enable phase-based robustness margins to be defined in some cases when the original system is not sectorial. A real-world power systems example is presented to show how the small phase criterion using a multiplier can significantly reduce the conservatism of the small gain theorem, providing computationally straightforward methods to inform further nonlinear stability analysis of power systems.

We propose a novel nonlinear control method for the voltage regulation of a buck converter to achieve a rapid and nonovershooting step response. The method combines feedback linearisation with a linear static state feedback controller to shape the system transient response. Experimental results involving performance comparisons with conventional linear controllers show the proposed method can deliver substantial improvement in the step reference response of a buck converter circuit, while also providing improved transient response in the rejection of both line and load disturbances.

Multiple-input multiple-output (MIMO) frequency-domain models are often used to analyse the small-signal stability of grid-connected converters. The limitations of standard MIMO analysis tools have spurred the development of methods that reduce systems to equivalent single-input single-output (SISO) systems. This paper presents a novel equivalent model structure for a grid-following converter containing two SISO inner feed-back loops and a single SISO outer feedback loop. The simple algebraic relationship between the original and equivalent model allows insight into effects such as that of varying power angle, as well as controller design based on classic loop shaping principles.

IEEE Topical Meetings on Silicon Monolithic Integrated Circuits in RF Systems have been at the forefront of moving Silicon technologies into microwave, millimeter-wave and THz applications -a development now widely accepted, and of great importance.RF CMOS and Si/SiGe BiCMOS technologies are well established in commercial and defense applications.SiRF 2023 will mark the 23 rd topical meeting on SiRF, with a renewed emphasis on promoting a dialogue between IC designers and researchers promoting non-standard technologies, exploiting the maturity of Silicon processes, but addressing the challenges of tomorrow.The three days of SiRF 2023 will chronicle recent advances in our dynamic field, and provide the platform for developing new ideas, and candid exchange, facilitated by SiRF's single-session format.As in past years, a line-up of reputed invited speakers will stimulate our discussions, with an emphasis on emerging technologies.

The recent proliferation of converter based wind turbine generators into the power grid has increase the risk of system stability issues. Thus, it is important to have a reliable control strategy for Doubly Fed Induction Generator based wind turbines to simultaneously provide frequency and power factor control. Due to the existence of various uncertainties coming from machine parameters, grid voltage and frequency fluctuations, and turbulence from wind signals a robust controller is preferred to handle these uncertainties. This paper proposes a robust output regulation control strategy that can handle the disturbances and track the required active and reactive power command signals in the presence of these uncertainties. Simulation results illustrate the effectiveness of the proposed control algorithm compared with the standard proportional integral control law.

We propose a rapid nonovershooting tracking controller for the continuous infusion of anesthetics and analgesics to prevent overdosing and other harmful side effects on patients. The controller utilizes a state feedback control design methodology for multi-input multi-output systems to achieve a closed-loop eigenstructure that yields a nonovershooting transient response. The method is combined with a global optimization method to achieve a rapid nonovershooting response. The controller uses an extended Kalman filter to estimate system states from measurable outputs, and integral control is added to achieve robust tracking. The performance of the method is simulated on 20 patient models in two groups, and the results are compared against another recent study from the biomedical control literature.

Optimizing wind turbine performance involves maximizing energy harvesting while seeking to minimize load fatigues on the tower structure, blades, and rotor. To improve turbine control performance, wind preview measurement technologies, such as light detection and ranging (LIDAR), have been a point of interest for researchers in recent years. In this article, we explore the application of a classical control methodology known as exact output regulation (EOR) for improving the control performance of a LIDAR-enhanced wind turbine. The EOR controller is designed to achieve the rejection of known input disturbances, while also ensuring the system output tracks the desired reference signal. The controller is comprised of a state feedback controller together with a feedforward gain. The LIDAR wind preview information is used to obtain a low-order exosystem for modeling wind dynamics. This wind exosystem is used to obtain the feedforward gain matrix that enables the EOR controller to effectively reject the input disturbance and achieve the desired reference tracking. Extensive simulations of the EOR controller with a broad range of wind speeds in both partial load and full load operating regions are performed on the full nonlinear aero-elastic model of the National Renewable Energy Laboratory 5-MW reference wind turbine. For performance comparisons, we also implement a Baseline torque controller and a commonly used feedforward control method known as disturbance accommodation control (DAC). The results show that, in comparison with a baseline and DAC controller, the EOR controller can provide a substantially improved reduction of fatigue loads and smoother power output, without compromising energy production levels.

The recent proliferation of converter-based generators for the grid connection of renewable energy sources has introduced a number of security challenges, including frequency stability. Frequency regulation is achieved through active power generation, requiring power generators to regulate their power output in real time to match a time-varying power command curve specified by the transmission system operator. Control methodologies to enable the turbine power output regulation have been investigated in the wind energy literature for more than a decade. In this paper, we investigate a classical control methodology known as exact output regulation that has been widely used for the twin problems of output regulation and disturbance rejection. We demonstrate how the method may be adapted to achieve output power regulation for a wind turbine. The method assumes that wind preview information from LIDAR measurements is available for the design of the torque and pitch control input signals. To evaluate the potential of the method to improve wind turbine capability for the provision of frequency regulation services, we compare its performance with that of two baseline controllers, one pitch-based and one torque-based, for the tracking of a specified time-varying power command signal. Our simulations show that the proposed methodology can substantially improve the output power regulation, reduce fatigue loads and also reduce actuator usage, relative to the baseline controllers.

为了寻求更加优化的直流降压变压器控制系统,搭建了一台直流降压变压器并根据其参数建模,在此基础上分别设计了一种基于NOUS算法的状态空间控制系统、使用观测器的NOUS算法控制系统和PID控制系统.用示波器展示变压器在这3种控制系统下的阶跃响应输出和负载发生突变时的瞬态响应,以对比它们的控制性能.结果表明,基于NOUS算法的状态空间控制系统具有更好的瞬态响应和控制精确度,而PID控制系统在鲁棒性方面具有优势,使用观测器的NOUS算法控制系统仍保留了收敛速度快的优点,但会导致控制系统建模中的误差被放大,影响输出结果.

We develop a geometric approach for fractional linear time-invariant systems with Caputo-type derivatives. In particular, we generalize the fundamental notions of invariance and controlled invariance to the fractional setting. We then exploit this new geometric framework to address the disturbance decoupling problem via static pseudostate feedback, with and without stability. Our main contribution is a set of necessary and sufficient conditions for the disturbance decoupling problem that are related to the input-output properties of the closed-loop system, and hence they are applicable not just to Caputo-type derivatives but, more broadly, to any type of fractional system. These results show that, while the conditions for guaranteeing the existence of a decoupling pseudostate feedback remain essentially unchanged, the underlying theoretical framework is substantially different, because the fractional derivative is a nonlocal operator and this property plays a major role in the characterization of the evolution of the pseudostate trajectory. In particular, we show that, unlike the integer case, the infinite-dimensional nature of fractional systems means that feedback control is insufficient to maintain the pseudostate trajectory on a controlled invariant subspace, unless the entire past history of the pseudostate has evolved on that subspace. However, feed-forward control can achieve this task under certain necessary and sufficient geometric conditions.

The recent proliferation of converter-based power generating systems used within renewable energy sources has increased the need to consider how renewable energy sources, including wind turbines, may participate in ancillary services to ensure the security of the power grid. In this paper, we discuss a modern control methodology known as exact output regulation (EOR) and investigate its application to the problem of wind turbine provision of grid frequency support services. The EOR method assumes that both wind preview information from LIDAR measurements and the power demand curve from the grid are available in the design of the turbine control law. Our simulation study will compare the performance of EOR with a baseline controller, for the tracking of a time-varying power demand curve. Our work focuses on region 3 wind signals, however it has the capability to extend to other wind speed region. The simulations will employ the FAST simulator for a 5MW wind turbine, with wind signals generated by TurbSim. The simulations show that the LIDAR-enhanced EOR method has the potential to substantially improve power demand tracking performance, relative to a baseline controller.