Adaptive control of sinusoidal brushless DC motor actuators

Electrical Power Assisted Steering system (EPAS) will likely be used on future automotive power steering systems. The sinusoidal brushless DC (BLDC) motor has been identified as one of the most suitable actuators for the EPAS application. Motor characteristic variations, which can be indicated by variations of the motor parameters such as the coil resistance and the torque constant, directly impart inaccuracies in the control scheme based on the nominal values of parameters and thus the whole system performance suffers. The motor controller must address the time-varying motor characteristics problem and maintain the performance in its long service life. In this dissertation, four adaptive control algorithms for brushless DC (BLDC) motors are explored. The first algorithm engages a simplified inverse dq-coordinate dynamics controller and solves for the parameter errors with the q-axis current (iq) feedback from several past sampling steps. The controller parameter values are updated by slow integration of the parameter errors. Improvement such as dynamic approximation, speed approximation and Gram-Schmidt orthonormalization are discussed for better estimation performance. The second algorithm is proposed to use both the d-axis current (id) and the q-axis current (iq) feedback for parameter estimation since id always accompanies iq. Stochastic conditions for unbiased estimation are shown through Monte Carlo simulations. Study of the first two adaptive algorithms indicates that the parameter estimation performance can be achieved by using more history data. The Extended Kalman Filter (EKF), a representative recursive estimation algorithm, is then investigated for the BLDC motor application. Simulation results validated the superior estimation performance with the EKF. However, the computation complexity and stability may be barriers for practical implementation of the EKF. The fourth algorithm is a model reference adaptive control (MRAC) that utilizes the desired motor characteristics as a reference model. Its stability is guaranteed by Lyapunov’s direct method. Simulation shows superior performance in terms of the convergence speed and current tracking. These algorithms are compared in closed loop simulation with an EPAS model and a motor speed control application. The MRAC is identified as the most promising candidate controller because of its combination of superior performance and low computational complexity. A BLDC motor controller developed with the dq-coordinate model cannot be implemented without several supplemental functions such as the coordinate transformation and a DC-to-AC current encoding scheme. A quasi-physical BLDC motor model is developed to study the practical implementation issues of the dq-coordinate control strategy, such as the initialization and rotor angle transducer resolution. This model can also be beneficial during first stage development in automotive BLDC motor applications

Advanced Computational-Effective Control and Observation Schemes for Constrained Nonlinear Systems

Constraints are one of the most common challenges that must be faced in control systems design. The sources of constraints in engineering applications are several, ranging from actuator saturations to safety restrictions, from imposed operating conditions to trajectory limitations. Their presence cannot be avoided, and their importance grows even more in high performance or hazardous applications. As a consequence, a common strategy to mitigate their negative effect is to oversize the components. This conservative choice could be largely avoided if the controller was designed taking all limitations into account. Similarly, neglecting the constraints in system estimation often leads to suboptimal solutions, which in turn may negatively affect the control effectiveness. Therefore, with the idea of taking a step further towards reliable and sustainable engineering solutions, based on more conscious use of the plants' dynamics, we decide to address in this thesis two fundamental challenges related to constrained control and observation. In the first part of this work, we consider the control of uncertain nonlinear systems with input and state constraints, for which a general approach remains elusive. In this context, we propose a novel closed-form solution based on Explicit Reference Governors and Barrier Lyapunov Functions. Notably, it is shown that adaptive strategies can be embedded in the constrained controller design, thus handling parametric uncertainties that often hinder the resulting performance of constraint-aware techniques. The second part of the thesis deals with the global observation of dynamical systems subject to topological constraints, such as those evolving on Lie groups or homogeneous spaces. Here, general observability analysis tools are overviewed, and the problem of sensorless control of permanent magnets electrical machines is presented as a case of study. Through simulation and experimental results, we demonstrate that the proposed formalism leads to high control performance and simple implementation in embedded digital controllers

Advances in Computer Science and Engineering

The book Advances in Computer Science and Engineering constitutes the revised selection of 23 chapters written by scientists and researchers from all over the world. The chapters cover topics in the scientific fields of Applied Computing Techniques, Innovations in Mechanical Engineering, Electrical Engineering and Applications and Advances in Applied Modeling

Adaptive Control

Adaptive control has been a remarkable field for industrial and academic research since 1950s. Since more and more adaptive algorithms are applied in various control applications, it is becoming very important for practical implementation. As it can be confirmed from the increasing number of conferences and journals on adaptive control topics, it is certain that the adaptive control is a significant guidance for technology development.The authors the chapters in this book are professionals in their areas and their recent research results are presented in this book which will also provide new ideas for improved performance of various control application problems

Adaptive nonlinear control using fuzzy logic and neural networks

The problem of adaptive nonlinear control, i.e. the control of nonlinear dynamic systems with unknown parameters, is considered. Current techniques usually assume that either the control system is linearizable or the type of nonlinearity is known. This results in poor control quality for many practical problems. Moreover, the control system design becomes too complex for a practicing engineer. The objective of this thesis is to provide a practical, systematic approach for solving the problem of identification and control of nonlinear systems with unknown parameters, when the explicit linear parametrization is either unknown or impossible. Fuzzy logic (FL) and neural networks (NNs) have proven to be the tools for universal approximation, and hence are considered. However, FL requires expert knowledge and there is a lack of systematic procedures to design NNs for control. A hybrid technique, called fuzzy logic adaptive network (FLAN), which combines the structure of an FL controller with the learning aspects of the NNs is developed. FLAN is designed such that it is capable of both structure learning and parameter learning. Gradient descent based technique is utilized for the parameter learning in FLAN, and it is tested through a variety of simulated experiments in identification and control of nonlinear systems. The results indicate the success of FLAN in terms of accuracy of estimation, speed of convergence, insensitivity against a range of initial learning rates, robustness against sudden changes in the input as well as noise in the training data. The performance of FLAN is also compared with the techniques based on FL and NNs, as well as several hybrid techniques

Deterministic Artificial Intelligence

Kirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book

Relaxing Fundamental Assumptions in Iterative Learning Control

Iterative learning control (ILC) is perhaps best decribed as an open loop feedforward control technique where the feedforward signal is learned through repetition of a single task. As the name suggests, given a dynamic system operating on a finite time horizon with the same desired trajectory, ILC aims to iteratively construct the inverse image (or its approximation) of the desired trajectory to improve transient tracking. In the literature, ILC is often interpreted as feedback control in the iteration domain due to the fact that learning controllers use information from past trials to drive the tracking error towards zero. However, despite the significant body of literature and powerful features, ILC is yet to reach widespread adoption by the control community, due to several assumptions that restrict its generality when compared to feedback control. In this dissertation, we relax some of these assumptions, mainly the fundamental invariance assumption, and move from the idea of learning through repetition to two dimensional systems, specifically repetitive processes, that appear in the modeling of engineering applications such as additive manufacturing, and sketch out future research directions for increased practicality: We develop an L1 adaptive feedback control based ILC architecture for increased robustness, fast convergence, and high performance under time varying uncertainties and disturbances. Simulation studies of the behavior of this combined L1-ILC scheme under iteration varying uncertainties lead us to the robust stability analysis of iteration varying systems, where we show that these systems are guaranteed to be stable when the ILC update laws are designed to be robust, which can be done using existing methods from the literature. As a next step to the signal space approach adopted in the analysis of iteration varying systems, we shift the focus of our work to repetitive processes, and show that the exponential stability of a nonlinear repetitive system is equivalent to that of its linearization, and consequently uniform stability of the corresponding state space matrix.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133232/1/altin_1.pd

Modeling and robust adaptive tracking control of a planar precision positioning system

Precision positioning systems constitute an essential prerequisite for modern production processes in the diverse applications of micro- and nanotechnology. Associated with the control of these systems there are high demands with respect to bandwidth, accuracy, robustness and stability. The most important requirement, however, is dynamic tracking of complex reference trajectories with highest precision. To achieve these objectives, usually a good knowledge of system parameters is necessary, whereby their identification is mostly laborious and expensive. In addition, depending on the production process or plant, parameters may change with time which may endanger the achievement of these goals. From an economic perspective, it is therefore desirable that parameter identification is carried out during operation, within the control scheme. This reduces the effort for system identification and also ensures that the controller may also adapt to parametric changes. Based on this motivation, the present thesis deals with the development of an adaptive tracking control concept for the planar precision positioning system PPS1405 build by the motor manufacturer Tetra. The development and identification of detailed system models of the most important components of the PPS1405 is the foundation for this. The developed model serves firstly as a basis for model-based control design and secondly as a realistic simulation environment for testing and evaluation of the controllers designed. Furthermore, the model gives insights about the potential applicability of adaptive control which is confirmed throughout the analysis. Following this, the aspired tracking control design is based on the idea of a two-stage approach, comprising a nominal tracking controller and an adaptive augmentation exploiting ideas from $\mathcal{L}_1$ adaptive control. The latter seems promising in view of remarkable performance and robustness properties. For the adaptive tracking controller, both, state and output feedback schemes are developed, whereas in view of the available measurement signals only the output feedback scheme is implemented at the test rig. Experimental results confirm the efficiency of the proposed control scheme. It meets all specifications with regard to tracking errors and yields tracking performance that has not been obtained by any of the existing controllers so far.Präzisionspositioniersysteme bilden eine wesentliche Grundvoraussetzung für moderne Produktionsprozesse in den vielschichtigen Anwendungen der Mikro- und Nanotechnologie. An die Regelung dieser Systeme werden hohe Anforderungen bzgl. Bandbreite, Genauigkeit, Robustheit und Stabilität gestellt. Die wichtigste Anforderung jedoch, bildet die dynamische Verfolgung komplexer Referenztrajektorien mit höchster Präzision. Zur Erreichung dieser Ziele ist zumeist eine möglichst genaue Kenntnis der wesentlichen Systemparameter erforderlich, deren Identifikation in der Regel aufwändig und teuer ist. Zudem können sich je nach Produktionsprozess oder Anlage Parameter mit der Zeit verändern, was die Erreichung dieser Ziele gefährdet. Aus betriebswirtschaftlicher Sicht ist es daher erstrebenswert, die Parameteridentifikation während des Betriebs innerhalb der Regelung durchzuführen. Dies reduziert den Aufwand bei der Systemidentifikation und stellt zudem sicher, dass die Regelung sich auch gegenüber Veränderungen anpassen kann. Aus dieser Motivation heraus beschäftigt sich die vorliegende Dissertation mit der Entwicklung eines adaptiven Folgeregelungskonzepts für das planare Präzisionspositioniersystem PPS1405 der Firma Tetra. Die Grundlage hierfür bildet die Entwicklung sowie die Identifikation detaillierter Systemmodelle der wesentlichen Komponenten des PPS1405. Das entwickelte Modell dient zum einen als Grundlage für modellbasierte Regelungsentwürfe und zum anderen als realistische Simulationsumgebung zur Erprobung und Bewertung dieser Verfahren. Aufbauend darauf, basiert der angestrebte Folgeregelungsentwurf auf der Idee eines zweistufigen Ansatzes, bestehend aus einem nominellen Folgeregler und einer adaptiven Erweiterung mittels L1 adaptiver Regelung. Letztere erscheint im Hinblick auf herausragenden Performance- und Robustheitseigenschaften vielversprechend. Für die adaptive Folgeregelung werden sowohl Ansätze für Zustands- als auch Ausgangsrückführungen entwickelt, wobei aufgrund der zur Verfügung stehenden Messsignale nur letztere am Versuchsstand implementiert werden. Experimentelle Ergebnisse bestätigen die Leistungsfähigkeit der entwickelten Regelung. Diese erfüllt alle gestellten Anforderungen hinsichtlich der Positionsabweichung und erzielt Regelgüten, die mit existierenden Reglern bisher nicht erreicht wurden

Tools for Nonlinear Control Systems Design

This is a brief statement of the research progress made on Grant NAG2-243 titled "Tools for Nonlinear Control Systems Design", which ran from 1983 till December 1996. The initial set of PIs on the grant were C. A. Desoer, E. L. Polak and myself (for 1983). From 1984 till 1991 Desoer and I were the Pls and finally I was the sole PI from 1991 till the end of 1996. The project has been an unusually longstanding and extremely fruitful partnership, with many technical exchanges, visits, workshops and new avenues of investigation begun on this grant. There were student visits, long term.visitors on the grant and many interesting joint projects. In this final report I will only give a cursory description of the technical work done on the grant, since there was a tradition of annual progress reports and a proposal for the succeeding year. These progress reports cum proposals are attached as Appendix A to this report. Appendix B consists of papers by me and my students as co-authors sorted chronologically. When there are multiple related versions of a paper, such as a conference version and journal version they are listed together. Appendix C consists of papers by Desoer and his students as well as 'solo' publications by other researchers supported on this grant similarly chronologically sorted