Autonomous pointing control of a large satellite antenna subject to parametric uncertainty

With the development of satellite mobile communications, large antennas are now widely used. The precise pointing of the antenna’s optical axis is essential for many space missions. This paper addresses the challenging problem of high-precision autonomous pointing control of a large satellite antenna. The pointing dynamics are firstly proposed. The proportional–derivative feedback and structural filter to perform pointing maneuvers and suppress antenna vibrations are then presented. An adaptive controller to estimate actual system frequencies in the presence of modal parameters uncertainty is proposed. In order to reduce periodic errors, the modified controllers, which include the proposed adaptive controller and an active disturbance rejection filter, are then developed. The system stability and robustness are analyzed and discussed in the frequency domain. Numerical results are finally provided, and the results have demonstrated that the proposed controllers have good autonomy and robustness

Nonlinear control synthesis by convex optimization

A stability criterion for nonlinear systems, recently derived by the third author, can be viewed as a dual to Lyapunov's second theorem. The criterion is stated in terms of a function which can be interpreted as the stationary density of a substance that is generated all over the state-space and flows along the system trajectories toward the equilibrium. The new criterion has a remarkable convexity property, which in this note is used for controller synthesis via convex optimization. Recent numerical methods for verification of positivity of multivariate polynomials based on sum of squares decompositions are used

Discrete-time adaptive learning control for parametric uncertainties with unknown periods

In this paper, we approach the problem of unknown periods for a class of discrete-time parametric nonlinear systems with nonlinearities which do not necessarily satisfy the sector-bounded condition. The unknown periods hide in the parametric uncertainties, which is difficult to estimate. By incorporating a logic-based switching mechanism, we estimate the period and bound of unknown parameter simultaneously under Lyapunov-based analysis. Rigorous proof is given to demonstrate that a finite number of switchings can guarantee the asymptotic regulation of the nonlinear system considered. The simulation result also shows the efficacy of the proposed switching periodic adaptive control method.Peer reviewe

Nonlinear adaptive estimation with application to sinusoidal identification

Parameter estimation of a sinusoidal signal in real-time is encountered in applications in numerous areas of engineering. Parameters of interest are usually amplitude, frequency and phase wherein frequency tracking is the fundamental task in sinusoidal estimation. This thesis deals with the problem of identifying a signal that comprises n (n ≥ 1) harmonics from a measurement possibly affected by structured and unstructured disturbances. The structured perturbations are modeled as a time-polynomial so as to represent, for example, bias and drift phenomena typically present in applications, whereas the unstructured disturbances are characterized as bounded perturbation. Several approaches upon different theoretical tools are presented in this thesis, and classified into two main categories: asymptotic and non-asymptotic methodologies, depending on the qualitative characteristics of the convergence behavior over time. The first part of the thesis is devoted to the asymptotic estimators, which typically consist in a pre-filtering module for generating a number of auxiliary signals, independent of the structured perturbations. These auxiliary signals can be used either directly or indirectly to estimate—in an adaptive way—the frequency, the amplitude and the phase of the sinusoidal signals. More specifically, the direct approach is based on a simple gradient method, which ensures Input-to-State Stability of the estimation error with respect to the bounded-unstructured disturbances. The indirect method exploits a specific adaptive observer scheme equipped with a switching criterion allowing to properly address in a stable way the poor excitation scenarios. It is shown that the adaptive observer method can be applied for estimating multi-frequencies through an augmented but unified framework, which is a crucial advantage with respect to direct approaches. The estimators’ stability properties are also analyzed by Input-to-State-Stability (ISS) arguments. In the second part we present a non-asymptotic estimation methodology characterized by a distinctive feature that permits finite-time convergence of the estimates. Resorting to the Volterra integral operators with suitably designed kernels, the measured signal is processed, yielding a set of auxiliary signals, in which the influence of the unknown initial conditions is annihilated. A sliding mode-based adaptation law, fed by the aforementioned auxiliary signals, is proposed for deadbeat estimation of the frequency and amplitude, which are dealt with in a step-by-step manner. The worst case behavior of the proposed algorithm in the presence of bounded perturbation is studied by ISS tools. The practical characteristics of all estimation techniques are evaluated and compared with other existing techniques by extensive simulations and experimental trials.Open Acces

Signal processing with Fourier analysis, novel algorithms and applications

Fourier analysis is the study of the way general functions may be represented or approximated by sums of simpler trigonometric functions, also analogously known as sinusoidal modeling. The original idea of Fourier had a profound impact on mathematical analysis, physics and engineering because it diagonalizes time-invariant convolution operators. In the past signal processing was a topic that stayed almost exclusively in electrical engineering, where only the experts could cancel noise, compress and reconstruct signals. Nowadays it is almost ubiquitous, as everyone now deals with modern digital signals. Medical imaging, wireless communications and power systems of the future will experience more data processing conditions and wider range of applications requirements than the systems of today. Such systems will require more powerful, efficient and flexible signal processing algorithms that are well designed to handle such needs. No matter how advanced our hardware technology becomes we will still need intelligent and efficient algorithms to address the growing demands in signal processing. In this thesis, we investigate novel techniques to solve a suite of four fundamental problems in signal processing that have a wide range of applications. The relevant equations, literature of signal processing applications, analysis and final numerical algorithms/methods to solve them using Fourier analysis are discussed for different applications in the electrical engineering/computer science. The first four chapters cover the following topics of central importance in the field of signal processing: • Fast Phasor Estimation using Adaptive Signal Processing (Chapter 2) • Frequency Estimation from Nonuniform Samples (Chapter 3) • 2D Polar and 3D Spherical Polar Nonuniform Discrete Fourier Transform (Chapter 4) • Robust 3D registration using Spherical Polar Discrete Fourier Transform and Spherical Harmonics (Chapter 5) Even though each of these four methods discussed may seem completely disparate, the underlying motivation for more efficient processing by exploiting the Fourier domain signal structure remains the same. The main contribution of this thesis is the innovation in the analysis, synthesis, discretization of certain well known problems like phasor estimation, frequency estimation, computations of a particular non-uniform Fourier transform and signal registration on the transformed domain. We conduct propositions and evaluations of certain applications relevant algorithms such as, frequency estimation algorithm using non-uniform sampling, polar and spherical polar Fourier transform. The techniques proposed are also useful in the field of computer vision and medical imaging. From a practical perspective, the proposed algorithms are shown to improve the existing solutions in the respective fields where they are applied/evaluated. The formulation and final proposition is shown to have a variety of benefits. Future work with potentials in medical imaging, directional wavelets, volume rendering, video/3D object classifications, high dimensional registration are also discussed in the final chapter. Finally, in the spirit of reproducible research we release the implementation of these algorithms to the public using Github

Frequency-Locked Loop Based Estimation of Single-Phase Grid Voltage Parameters

Estimation of amplitude, instantaneous phase, and frequency of a single-phase grid voltage signal are studied in this letter. The proposed approach uses a novel circular limit cycle oscillator (CLO) coupled with a frequency-locked loop. Due to the nonlinear structure of the CLO, the proposed frequency adaptive CLO technique is robust against various perturbations faced in the practical settings, e.g., the discontinuous jump of phase, frequency, and amplitude. The global stability analysis of the CLO and local stability analysis of the frequency adaptive CLO are performed. Experimental results demonstrate the effectiveness of the proposed technique over a very recent technique proposed in the literature

Levitation chassis dynamic analysis and robust position control for maglev vehicles under nonlinear periodic disturbance

In this paper, an experiment for decoupling the dynamic behavior of the levitation chassis of maglev vehicle with four electromagnetic suspension (EMS) modules is implemented, which validated that the stable suspension of maglev vehicle can be achieved by controlling individual EMS modules. Then, a dynamic model for single EMS module is established. A PD controller is designed to control the vertical position of the maglev vehicle. Simulations illustrate that the robustness of the controller is weak against the periodic disturbance. To improve the robustness of the controller, a nonlinear control law for disturbance rejection is applied by combining with a periodic disturbance estimator with an adaptive notch filter, which is capable of compensating exogenous nonlinear periodic disturbance. Different from using the existing control laws, the structure, parameters and period of the disturbance is not required. Moreover, the controller designed in this work satisfies the requirement of unidirectional force input. Simulation results are presented to demonstrate the excellent dynamic performance with the proposed robust controller

Nonlinear dynamics of DC-DC converters

Power electronic converters are time-varying, nonlinear dynamical systems. They exhibit a wide range of steady-state responses. The desired behaviour is a stable periodic motion around a predefined value with a frequency that is equal to that of the external clock. However, as parameters vary the operation can lose stability and go from one regime to another. Such phenomena are termed bifurcations and can degrade the output performance of the converter. Hence, it is of practical importance to know the conditions that cause such bifurcations to occur and to design the system so that it operates in the desired region. In the past, engineers have typically analysed the stability of power electronic systems by linearising the model about a fixed point. This captures the low-frequency properties while ignoring the detailed dynamics occurring at frequencies higher than the external clock. However, the demand for better functionality, reliability and performance means an in-depth analysis into the complex behaviour exhibited by dc-dc converters is required. Traditionally, dc-dc converters are employed with analog controllers whose function is to regulate the circuit. With advances in technology, digital control has become a potentially advantageous alternative to analog control. One of the main advantages of digital control is the ability to design more sophisticated design strategies to enable high performance dc-dc converters e.g. digital state-feedback control. Unfortunately, little work exists in the area of the effect of noise on digital control. This is a field that requires intensive study as to completely understand the nonlinear dynamics so as to enable accurate and economic designs. The aim of this thesis is to address these issues through the application of advanced nonlinear mathematics. The stability of power electronic systems is assessed with a view to developing design guidelines in order to ensure stable operation over a wide operating region

Advanced control of active magnetic bearings with learning control schemes

Master'sMASTER OF ENGINEERIN

Three-phase phase-locked loop algorithms based on sliding modes

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis article proposes a family of phase-locked loop schemes based on sliding modes. The use of sliding mode algorithms ensures fast response and global stability. In particular, two new algorithms are presented, both based on a complex framework for representing three-phase signals. This article compares the obtained algorithms with the traditional schemes, and a faster response is obtained when sliding modes are used. Additionally, as an application example, the algorithm is combined with a complex-coefficient filter that allows an easy identification of both positive and negative sequence harmonics. The proposed algorithms are illustrated by numerical simulations and experimentally validated using a digital signal processor.Peer ReviewedPostprint (published version