Discrete-time zeroing neural network for solving time-varying Sylvester-transpose matrix inequation via exp-aided conversion

Time-varying linear matrix equations and inequations have been widely studied in recent years. Time-varying Sylvester-transpose matrix inequation, which is an important variant, has not been fully investigated. Solving the time-varying problem in a constructive manner remains a challenge. This study considers an exp-aided conversion from time-varying linear matrix inequations to equations to solve the intractable problem. On the basis of zeroing neural network (ZNN) method, a continuous-time zeroing neural network (CTZNN) model is derived with the help of Kronecker product and vectorization technique. The convergence property of the model is analyzed. Two discrete-time ZNN models are obtained with the theoretical analyses of truncation error by using two Zhang et al.’s discretization (ZeaD) formulas with different precision to discretize the CTZNN model. The comparative numerical experiments are conducted for two discrete-time ZNN models, and the corresponding numerical results substantiate the convergence and effectiveness of two ZNN discrete-time models

Full- and reduced-order observer design for rectangular descriptor systems with unknown inputs

In this paper, methods are proposed to design Luenberger type full-and reduced-order observers for rectangular descriptor systems with unknown inputs. These methods are based on the effect of pre- and post-multiplicative operation of a linear transformation, derived here by means of simple matrix theory. Sufficient conditions for the existence of observers are given and proved. Numerical examples are given to illustrate the effectiveness of the proposed method

Liquid Transport Pipeline Monitoring Architecture Based on State Estimators for Leak Detection and Location

This research presents the implementation of optimization algorithms to build auxiliary signals that can be injected as inputs into a pipeline in order to estimate —by using state observers—physical parameters such as the friction or the velocity of sound in the fluid. For the state estimator design, the parameters to be estimated are incorporated into the state vector of a Liénard-type model of a pipeline such that the observer is constructed from the augmented model. A prescribed observability degree of the augmented model is guaranteed by optimization algorithms by building an optimal input for the identification. The minimization of the input energy is used to define the optimality of the input, whereas the observability Gramian is used to verify the observability. Besides optimization algorithms, a novel method, based on a Liénard-type model, to diagnose single and sequential leaks in pipelines is proposed. In this case, the Liénard-type model that describes the fluid behavior in a pipeline is given only in terms of the flow rate. This method was conceived to be applied in pipelines solely instrumented with flowmeters or in conjunction with pressure sensors that are temporarily out of service. The design approach starts with the discretization of the Liénard-type model spatial domain into a prescribed number of sections. Such discretization is performed to obtain a lumped model capable of providing a solution (an internal flow rate) for every section. From this lumped model, a set of algebraic equations (known as residuals) are deduced as the difference between the internal discrete flows and the nominal flow (the mean of the flow rate calculated prior to the leak). The residual closest to zero will indicate the section where a leak is occurring. The main contribution of our method is that it only requires flow measurements at the pipeline ends, which leads to cost reductions. Some simulation-based tes

Control Theory: On the Way to New Application Fields

Control theory is an interdisciplinary field that is located at the crossroads of pure and applied mathematics with systems engineering and the sciences. Recently, deep interactions are emerging with new application areas, such as systems biology, quantum control and information technology. In order to address the new challenges posed by the new application disciplines, a special focus of this workshop has been on the interaction between control theory and mathematical systems biology. To complement these more biology oriented focus, a series of lectures in this workshop was devoted to the control of networks of systems, fundamentals of nonlinear control systems, model reduction and identification, algorithmic aspects in control, as well as open problems in control

Design of an optimal preview controller for linear discrete-time periodic systems

In this paper, the preview tracking control problem for linear discrete-time periodic systems is considered. First, to overcome the difficulty arising from periodicity of the system, the linear discrete-time periodic system is transformed into an ordinary time-invariant system by lifting method. Secondly, the difference between a system state and its steady-state value is used to derive an augmented system instead of the usual difference between system states. Then, the preview controller for the augmented system is proposed by the preview control theory, which solves the preview tracking control problem for the periodic systems. Moreover, an integrator is introduced to ensure that the output can track the reference signal without static error. Finally, the obtained results are illustrated by the simulation examples

미지의 정현파 외부 입력을 갖는 선형시스템을 위한 적응 출력 제어

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 심형보.This dissertation investigates the output regulation problem (which is equivalent to the problem of asymptotic tracking and disturbance rejection when the reference inputs and the disturbances are generated by an autonomous differential equation, the so-called exosystem) for linear systems driven by unknown sinusoidal exosystems. Unlike previous researches, our ultimate goal is to achieve asymptotic regulation of the plant output to the origin for the sinusoidal exogenous signals (representing the reference inputs and disturbances) generated by the exosystems whose magnitudes, phases, bias, frequencies, and even the number of frequencies are all unknown. Here, the plant is linear time-invariant (LTI) single-input-single-output (SISO) systems (including non-minimum phase systems) without uncertainty. Before achieving the final control goal, we first start by considering an output regulation problem under the assumption that the number of frequencies contained in the exogenous inputs is known but magnitudes, phases, bias, and frequencies are unknown. To solve this problem, an add-on type output regulator with an adaptive observer is presented. The adaptive observer, based on the persistently exciting (PE) property, is used to estimate the frequencies of sinusoidal exogenous inputs as well as the states of plant and exosystem. Also, by add-on controller we mean an additional controller which runs harmonically with a preinstalled controller that has been in operation for the plant. When the desired performance of the preinstalled controller is not satisfactory, the add-on controller can be used. Some advantages of the proposed add-on controller include that it can be designed without much information about the preinstalled controller and it can be plugged in the feedback loop any time in operation without causing unnecessary transient response. Both simulation and experimental results of the track-following control for commercial optical disc drive (ODD) systems confirm the effectiveness of the proposed method. As the next step, we deal with the case where, as well as magnitudes, phases, bias, and frequencies, the number of frequencies contained in the exogenous inputs is unknown. To this end, a closed-form solution is given under the assumptions that the plant has hyperbolic zero dynamics (i.e., there is no zero on the imaginary axis of the complex plane), and that the number of unknown frequencies has known upper bound. In particular, the PE property is not necessary for the estimation of the unknown frequencies. For this, an adaptive observer is proposed to estimate the frequencies and the number of frequencies, simultaneously. This is important contribution, because, sufficient persistency of excitation is usually required since the unknown parameters are estimated by the adaptive control. Moreover, we propose a suitable dead-zone function with a computable dead-band only using the plant parameters to avoid the singularity problem in the transient-state and, at the same time, to achieve output regulation in the steady-state.Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Contributions and Outline of the Dissertation 5 Chapter 2 Reviews of Related Prior Studies 9 2.1 Control Methods for Rejecting of Sinusoidal Disturbance 9 2.1.1 Adaptive Feedforward Cancellation (AFC) 9 2.1.2 Repetitive Control 12 2.1.3 Disturbance Observer (DOB) with Internal Model 15 2.2 Frequency Estimation Algorithms for Indirect Approach 19 2.2.1 Adaptive Notch Filtering 19 2.2.2 Phase-Locked Loops 20 2.2.3 Extended Kalman Filtering 21 2.2.4 Marinos Frequency Estimator 23 Chapter 3 Highlights of Output Regulation for Linear Systems 27 3.1 Problem Formulation 27 3.2 Output Regulation via Full Information 29 3.3 Output Regulation via Error Feedback 31 Chapter 4 Adaptive Add-on Output Regulator for Unknown Sinusoidal Exogenous Inputs 37 4.1 Add-on Output Regulator 39 4.1.1 Problem Formulation 39 4.1.2 Controller Design and Stability Analysis 41 4.2 Adaptive Add-on Output Regulator 44 4.2.1 Problem Formulation 44 4.2.2 Controller Design and Analysis 46 4.3 Industrial Application: Optical Disc Drive (ODD) Systems 54 4.3.1 Introduction of ODD Systems 54 4.3.2 Simulation Results 58 4.3.3 Experimental Results 63 Chapter 5 Adaptive Output Regulator for Unknown Number of Unknown Sinusoidal Exogenous Inputs 69 5.1 Problem Formulation 71 5.2 Adaptive Output Regulator 72 5.3 Constructive Proof of Theorem 5.2.1 75 5.4 Numerical Examples 88 Chapter 6 Conclusions and Further Issues 93 6.1 Conclusions 93 6.2 Further Issues 94 APPENDIX 97 A.1 Stabilizability and Detectability of the Plant in Chapter 4 97 A.2 Nonsingularity of the Matrix T(θ) in Chapter 4 99 A.3 Pseudo Code Implemented on the DSP Board in Chapter 4 99 A.4 Observability Property of the Pair (S, γ) in Chapter 5 101 A.5 Structure of the Matrix Tc(θ) in Chapter 5 102 A.6 Convergence Property of det2(i(t)) in Lemma 5.3.2 104 BIBLIOGRAPHY 109 국문초록 121Docto

Automated Model Generation and Observer Design for Interconnected Systems : a Port-Hamiltonian Approach

Vernetzte Systeme stellen einen unverzichtbaren Teil moderner Gesellschaften dar. Mit dem Ausrollen neuer Kommunikationstechnologien und in Folge der fortgeschrittenen Nutzung von Synergiepotenzialen entstanden in den letzten Jahren vernetzte Systeme ungeahnten Ausmaßes. Aufgrund der Komplexität dieser Systeme, gelangen bestehende Modellierungs- und Beobachterentwurfsmethoden an ihre Grenzen. Modelle und Beobachter können deshalb häufig nur unter erheblichen Vereinfachungen entwickelt werden. Die vorliegende Dissertation schafft Abhilfe. Leitgedanke ist es, die Vorgänge der Modellerzeugung und des Beobachterentwurfs zu automatisieren. Hierzu werden in dieser Arbeit automatisierbare Modellierungs- und Beobachtermethoden auf Basis der Port-Hamiltonschen Systemtheorie entwickelt. Diese Methoden sind in einem Software-Prototyp namens AMOTO implementiert. In zwei Fallstudien wird AMOTO jeweils zur automatisierten Modellherleitung und zum automatisierten Beobachterentwurf eingesetzt. Computersimulationen weisen in beiden Fallstudien die Funktionstüchtigkeit der erzeugten Modelle und Beobachter nach und zeigen, dass diese genauere Ergebnisse liefern, als Modelle und Beobachter, die mit Methoden des bisherigen Stands der Technik entwickelt wurden. Dies unterstreicht die praktische Nutzbarkeit des vorgestellten Ansatzes. Es zeigt sich ferner, dass der Ansatz auf eine große Klasse vernetzter Systeme anwendbar ist. Somit leisten die Methoden, Algorithmen und Werkzeuge aus dieser Arbeit einen wichtigen Beitrag zur Bewältigung zukünftiger Herausforderungen in vernetzen Systemen

Fault estimation algorithms: design and verification

The research in this thesis is undertaken by observing that modern systems are becoming more and more complex and safety-critical due to the increasing requirements on system smartness and autonomy, and as a result health monitoring system needs to be developed to meet the requirements on system safety and reliability. The state-of-the-art approaches to monitoring system status are model based Fault Diagnosis (FD) systems, which can fuse the advantages of system physical modelling and sensors' characteristics. A number of model based FD approaches have been proposed. The conventional residual based approaches by monitoring system output estimation errors, however, may have certain limitations such as complex diagnosis logic for fault isolation, less sensitiveness to system faults and high computation load. More importantly, little attention has been paid to the problem of fault diagnosis system verification which answers the question that under what condition (i.e., level of uncertainties) a fault diagnosis system is valid. To this end, this thesis investigates the design and verification of fault diagnosis algorithms. It first highlights the differences between two popular FD approaches (i.e., residual based and fault estimation based) through a case study. On this basis, a set of uncertainty estimation algorithms are proposed to generate fault estimates according to different specifications after interpreting the FD problem as an uncertainty estimation problem. Then FD algorithm verification and threshold selection are investigated considering that there are always some mismatches between the real plant and the mathematical model used for FD observer design. Reachability analysis is drawn to evaluate the effect of uncertainties and faults such that it can be quantitatively verified under what condition a FD algorithm is valid. First the proposed fault estimation algorithms in this thesis, on the one hand, extend the existing approaches by pooling the available prior information such that performance can be enhanced, and on the other hand relax the existence condition and reduce the computation load by exploiting the reduced order observer structure. Second, the proposed framework for fault diagnosis system verification bridges the gap between academia and industry since on the one hand a given FD algorithm can be verified under what condition it is effective, and on the other hand different FD algorithms can be compared and selected for different application scenarios. It should be highlighted that although the algorithm design and verification are for fault diagnosis systems, they can also be applied for other systems such as disturbance rejection control system among many others

Control of fluid flows and other systems governed by partial differential-algebraic equations

The motion of fluids, such as air or water, is central to many engineering systems of significant economic and environmental importance. Examples range from air/fuel mixing in combustion engines to turbulence induced noise and fatigue on aircraft. Recent advances in novel sensor/actuator technologies have raised the intriguing prospect of actively sensing and manipulating the motion of the fluid within these systems, making them ripe for feedback control, provided a suitable control model exists. Unfortunately, the models for many of these systems are described by nonlinear, partial differential-algebraic equations for which few, if any, controller synthesis techniques exist. In stark contrast, the majority of established control theory assumes plant models of finite (and typically small) state dimension, expressed as a linear system of ordinary differential equations. Therefore, this thesis explores the problem of how to apply the mainstream tools of control theory to the class of systems described by partial differential-algebraic equations, that are either linear, or for which a linear approximation is valid. The problems of control system design for infinite-dimensional and algebraically constrained systems are treated separately in this thesis. With respect to the former, a new method is presented that enables the computation of a bound on the n-gap between a discretisation of a spatially distributed plant, and the plant itself, by exploiting the convergence rate of the v-gap metric between low-order models of successively finer spatial resolution. This bound informs the design, on loworder models, of H[infinity] loop-shaping controllers that are guaranteed to robustly stabilise the actual plant. An example is presented on a one-dimensional heat equation. Controller/estimator synthesis is then discussed for finite-dimensional systems containing algebraic, as well as differential equations. In the case of fluid flows, algebraic constraints typically arise from incompressibility and the application of boundary conditions. A numerical algorithm is presented, suitable for the semi-discrete linearised Navier-Stokes equations, that decouples the differential and algebraic parts of the system, enabling application of standard control theory without the need for velocity-vorticity type methods. This algorithm is demonstrated firstly on a simple electrical circuit, and secondly on the highly non-trivial problem of flow-field estimation in the transient growth region of a flat-plate boundary layer, using only wall shear measurements. These separate strands are woven together in the penultimate chapter, where a transient energy controller is designed for a channel-flow system, using wall mounted sensors and actuators