Nonlinear Control - an Overview
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Basic Nonlinear Control Systems - D
CONTROL SYSTEMS, ROBOTICS, AND AUTOMATION – Vol. III – Basic Nonlinear Control Systems - D. P. Atherton BASIC NONLINEAR CONTROL SYSTEMS D. P. Atherton University of Sussex, UK Keywords: Nonlinearity, friction, saturation, relay, backlash, frequency response, describing function, limit cycle, stability, phase plane, subharmonics, chaos, synchronization, compensation. Contents 1. Introduction 2. Forms of nonlinearity 3. Structure and behavior 4. Stability 5. Aspects of design 6. Conclusions Glossary Bibliography Biographical Sketch Summary This section is written to introduce the reader to nonlinearity and its effect in control systems, and also to ‘set the stage’ for the subsequent articles. Nonlinearity is first defined and then some of the physical effects which cause nonlinear behavior are discussed. This is followed by some comments on the stability of nonlinear systems, the existence of limit cycles, and the unique behavioral aspects which nonlinear feedback systems may possess. Finally a few brief comments are made on designing controllers for nonlinear systems. 1. Introduction Linear systems have the important property that they satisfy the superposition principle. This leads to many important advantages in methods for their analysis. For example, in circuit theory when an RLC circuit has both d.c and a.c input voltages, the voltage or current elsewhereUNESCO in the circuit can be– found EOLSS by summing the results of separate analyses for the d.c. and a.c. inputs taken individually, also if the magnitude of the a.c. voltage is doubledSAMPLE then the a.c. voltages or CHAPTERScurrents elsewhere in the circuit will be doubled. Thus, mathematically a linear system with input x()t and output yt() satisfies the property that the output for an input ax12() t+ bx () t is ay12() t+ by () t , if y1()t and y2 ()t are the outputs in response to the inputs x1()t and x2 ()t , respectively, and a and b are constants. -
Coping with the Bounds: a Neo-Clausewitzean Primer / Thomas J
About the CCRP The Command and Control Research Program (CCRP) has the mission of improving DoD’s understanding of the national security implications of the Information Age. Focusing upon improving both the state of the art and the state of the practice of command and control, the CCRP helps DoD take full advantage of the opportunities afforded by emerging technologies. The CCRP pursues a broad program of research and analysis in information superiority, information operations, command and control theory, and associated operational concepts that enable us to leverage shared awareness to improve the effectiveness and efficiency of assigned missions. An important aspect of the CCRP program is its ability to serve as a bridge between the operational, technical, analytical, and educational communities. The CCRP provides leadership for the command and control research community by: • articulating critical research issues; • working to strengthen command and control research infrastructure; • sponsoring a series of workshops and symposia; • serving as a clearing house for command and control related research funding; and • disseminating outreach initiatives that include the CCRP Publication Series. This is a continuation in the series of publications produced by the Center for Advanced Concepts and Technology (ACT), which was created as a “skunk works” with funding provided by the CCRP under the auspices of the Assistant Secretary of Defense (NII). This program has demonstrated the importance of having a research program focused on the national security implications of the Information Age. It develops the theoretical foundations to provide DoD with information superiority and highlights the importance of active outreach and dissemination initiatives designed to acquaint senior military personnel and civilians with these emerging issues. -
Neural Control for Nonlinear Dynamic Systems
Neural Control for Nonlinear Dynamic Systems Ssu-Hsin Yu Anuradha M. Annaswamy Department of Mechanical Engineering Department of Mechanical Engineering Massachusetts Institute of Technology Massachusetts Institute of Technology Cambridge, MA 02139 Cambridge, MA 02139 Email: [email protected] Email: [email protected] Abstract A neural network based approach is presented for controlling two distinct types of nonlinear systems. The first corresponds to nonlinear systems with parametric uncertainties where the parameters occur nonlinearly. The second corresponds to systems for which stabilizing control struc tures cannot be determined. The proposed neural controllers are shown to result in closed-loop system stability under certain conditions. 1 INTRODUCTION The problem that we address here is the control of general nonlinear dynamic systems in the presence of uncertainties. Suppose the nonlinear dynamic system is described as x= f(x , u , 0) , y = h(x, u, 0) where u denotes an external input, y is the output, x is the state, and 0 is the parameter which represents constant quantities in the system. The control objectives are to stabilize the system in the presence of disturbances and to ensure that reference trajectories can be tracked accurately, with minimum delay. While uncertainties can be classified in many different ways, we focus here on two scenarios. One occurs because the changes in the environment and operating conditions introduce uncertainties in the system parameter O. As a result, control objectives such as regulation and tracking, which may be realizable using a continuous function u = J'(x, 0) cannot be achieved since o is unknown. Another class of problems arises due to the complexity of the nonlinear function f. -
Control Theory
Control theory S. Simrock DESY, Hamburg, Germany Abstract In engineering and mathematics, control theory deals with the behaviour of dynamical systems. The desired output of a system is called the reference. When one or more output variables of a system need to follow a certain ref- erence over time, a controller manipulates the inputs to a system to obtain the desired effect on the output of the system. Rapid advances in digital system technology have radically altered the control design options. It has become routinely practicable to design very complicated digital controllers and to carry out the extensive calculations required for their design. These advances in im- plementation and design capability can be obtained at low cost because of the widespread availability of inexpensive and powerful digital processing plat- forms and high-speed analog IO devices. 1 Introduction The emphasis of this tutorial on control theory is on the design of digital controls to achieve good dy- namic response and small errors while using signals that are sampled in time and quantized in amplitude. Both transform (classical control) and state-space (modern control) methods are described and applied to illustrative examples. The transform methods emphasized are the root-locus method of Evans and fre- quency response. The state-space methods developed are the technique of pole assignment augmented by an estimator (observer) and optimal quadratic-loss control. The optimal control problems use the steady-state constant gain solution. Other topics covered are system identification and non-linear control. System identification is a general term to describe mathematical tools and algorithms that build dynamical models from measured data. -
Observation of Nonlinear Verticum-Type Systems Applied to Ecological Monitoring
2nd Reading June 4, 2012 13:53 WSPC S1793-5245 242-IJB 1250051 International Journal of Biomathematics Vol. 5, No. 6 (November 2012) 1250051 (15 pages) c World Scientific Publishing Company DOI: 10.1142/S1793524512500519 OBSERVATION OF NONLINEAR VERTICUM-TYPE SYSTEMS APPLIED TO ECOLOGICAL MONITORING S. MOLNAR´ Institute of Mathematics and Informatics Szent Istv´an University P´ater K. u. 1., H-2103 Godollo, Hungary [email protected] M. GAMEZ´ ∗ and I. LOPEZ´ † Department of Statistics and Applied Mathematics University of Almer´ıa La Ca˜nada de San Urbano 04120 Almer´ıa, Spain ∗[email protected] †[email protected] Received 4 November 2011 Accepted 13 January 2012 Published 7 June 2012 In this paper the concept of a nonlinear verticum-type observation system is introduced. These systems are composed from several “subsystems” connected sequentially in a particular way: a part of the state variables of each “subsystem” also appears in the next “subsystem” as an “exogenous variable” which can also be interpreted as a con- trol generated by an “exosystem”. Therefore, these “subsystems” are not observation systems, but formally can be considered as control-observation systems. The problem of observability of such systems can be reduced to rank conditions on the “subsystems”. Indeed, under the condition of Lyapunov stability of an equilibrium of the “large”, verticum-type system, it is shown that the Kalman rank condition on the linearization of the “subsystems” implies the observability of the original, nonlinear verticum-type system. For an illustration of the above linearization result, a stage-structured fishery model with reserve area is considered. -
Nonlinear System Stability and Behavioral Analysis for Effective
S S symmetry Article Nonlinear System Stability and Behavioral Analysis for Effective Implementation of Artificial Lower Limb Susmita Das 1 , Dalia Nandi 2, Biswarup Neogi 3 and Biswajit Sarkar 4,* 1 Narula Institute of Technology, Kolkata 700109, India; [email protected] 2 Indian Institute of Information Technology Kalyani, Kalyani 741235, India; [email protected] 3 JIS College of Engineering., Kalyani 741235, India; [email protected] 4 Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea * Correspondence: [email protected] or [email protected] Received: 8 September 2020; Accepted: 14 October 2020; Published: 19 October 2020 Abstract: System performance and efficiency depends on the stability criteria. The lower limb prosthetic model design requires some prerequisites such as hardware design functionality and compatibility of the building block materials. Effective implementation of mathematical model simulation symmetry towards the achievement of hardware design is the focus of the present work. Different postures of lower limb have been considered in this paper to be analyzed for artificial system design of lower limb movement. The generated polynomial equations of the sitting and standing positions of the normal limb are represented with overall system transfer function. The behavioral analysis of the lower limb model shows the nonlinear nature. The Euler-Lagrange method is utilized to describe the nonlinearity in the field of forward dynamics of the artificial system. The stability factor through phase portrait analysis is checked with respect to nonlinear system characteristics of the lower limb. The asymptotic stability has been achieved utilizing the most applicable Lyapunov method for nonlinear systems. -
Spectral Properties of the Koopman Operator in the Analysis of Nonstationary Dynamical Systems
UNIVERSITY of CALIFORNIA Santa Barbara Spectral Properties of the Koopman Operator in the Analysis of Nonstationary Dynamical Systems A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Mechanical Engineering by Ryan M. Mohr Committee in charge: Professor Igor Mezi´c,Chair Professor Bassam Bamieh Professor Jeffrey Moehlis Professor Mihai Putinar June 2014 The dissertation of Ryan M. Mohr is approved: Bassam Bamieh Jeffrey Moehlis Mihai Putinar Igor Mezi´c,Committee Chair May 2014 Spectral Properties of the Koopman Operator in the Analysis of Nonstationary Dynamical Systems Copyright c 2014 by Ryan M. Mohr iii To my family and dear friends iv Acknowledgements Above all, I would like to thank my family, my parents Beth and Jim, and brothers Brennan and Gralan. Without their support, encouragement, confidence and love, I would not be the person I am today. I am also grateful to my advisor, Professor Igor Mezi´c,who introduced and guided me through the field of dynamical systems and encouraged my mathematical pursuits, even when I fell down the (many) proverbial (mathematical) rabbit holes. I learned many fascinating things from these wandering forays on a wide range of topics, both contributing to my dissertation and not. Our many interesting discussions on math- ematics, research, and philosophy are among the highlights of my graduate school career. His confidence in my abilities and research has been a constant source of encouragement, especially when I felt uncertain in them myself. His enthusiasm for science and deep, meaningful work has set the tone for the rest of my career and taught me the level of research problems I should consider and the quality I should strive for. -
Rethinking the International System As a Complex Adaptive System
Munich Personal RePEc Archive A New Taxonomy for International Relations: Rethinking the International System as a Complex Adaptive System Scartozzi, Cesare M. University of Tokyo, Graduate School of Public Policy 2018 Online at https://mpra.ub.uni-muenchen.de/95496/ MPRA Paper No. 95496, posted 12 Aug 2019 10:50 UTC 1SFQVCMJDBUJPOJournal on Policy and Complex Systems • Volume 4, Number 1 • Spring 2018 A New Taxonomy for International Relations: Rethinking the International System as a Complex Adaptive System Cesare Scartozzi Graduate School of Public Policy, University of Tokyo [email protected] 7-3-1, Hongo, Bunkyo, Tokyo 113-0033, Japan ORCID number: 0000-0002-4350-4386. Abstract The international system is a complex adaptive system with emer- gent properties and dynamics of self-organization and informa- tion processing. As such, it is better understood with a multidis- ciplinary approach that borrows methodologies from the field of complexity science and integrates them to the theoretical perspec- tives offered by the field of international relations (IR). This study is set to formalize a complex systems theory approach to the study of international affairs and introduce a new taxonomy for IR with the two-pronged aim of improving interoperability between differ- ent epistemological communities and outlining a formal grammar that set the basis for modeling international politics as a complex adaptive system. Keywords: international politics, international relations theory, complex systems theory, taxonomy, adaptation, fitness, self-orga- nization This is a prepublication version of: Scartozzi, Cesare M. “A New Taxonomy for International Relations: Rethinking the International System as a Complex Adaptive System.” Journal on Policy and Complex Systems 4, no. -
Chapter 8 Nonlinear Systems
Chapter 8 Nonlinear systems 8.1 Linearization, critical points, and equilibria Note: 1 lecture, §6.1–§6.2 in [EP], §9.2–§9.3 in [BD] Except for a few brief detours in chapter 1, we considered mostly linear equations. Linear equations suffice in many applications, but in reality most phenomena require nonlinear equations. Nonlinear equations, however, are notoriously more difficult to understand than linear ones, and many strange new phenomena appear when we allow our equations to be nonlinear. Not to worry, we did not waste all this time studying linear equations. Nonlinear equations can often be approximated by linear ones if we only need a solution “locally,” for example, only for a short period of time, or only for certain parameters. Understanding linear equations can also give us qualitative understanding about a more general nonlinear problem. The idea is similar to what you did in calculus in trying to approximate a function by a line with the right slope. In § 2.4 we looked at the pendulum of length L. The goal was to solve for the angle θ(t) as a function of the time t. The equation for the setup is the nonlinear equation L g θ�� + sinθ=0. θ L Instead of solving this equation, we solved the rather easier linear equation g θ�� + θ=0. L While the solution to the linear equation is not exactly what we were looking for, it is rather close to the original, as long as the angleθ is small and the time period involved is short. You might ask: Why don’t we just solve the nonlinear problem? Well, it might be very difficult, impractical, or impossible to solve analytically, depending on the equation in question. -
NONLINEARITIES, FEEDBACKS and CRITICAL THRESHOLDS WITHIN the EARTH's CLIMATE SYSTEM 1. Introduction Nonlinear Phenomena Charac
NONLINEARITIES, FEEDBACKS AND CRITICAL THRESHOLDS WITHIN THE EARTH’S CLIMATE SYSTEM JOSÉ A. RIAL 1,ROGERA.PIELKESR.2, MARTIN BENISTON 3, MARTIN CLAUSSEN 4, JOSEP CANADELL 5, PETER COX 6, HERMANN HELD 4, NATHALIE DE NOBLET-DUCOUDRÉ 7, RONALD PRINN 8, JAMES F. REYNOLDS 9 and JOSÉ D. SALAS 10 1Wave Propagation Laboratory, Department of Geological Sciences CB#3315, University of North Carolina, Chapel Hill, NC 27599-3315, U.S.A. E-mail: [email protected] 2Atmospheric Science Dept., Colorado State University, Fort Collins, CO 80523, U.S.A. 3Dept. of Geosciences, Geography, Univ. of Fribourg, Pérolles, Ch-1700 Fribourg, Switzerland 4Potsdam Institute for Climate Impact Research, Telegrafenberg C4, 14473 Potsdam, P.O. Box 601203, Potsdam, Germany 5GCP-IPO, Earth Observation Centre, CSIRO, GPO Box 3023, Canberra, ACT 2601, Australia 6Met Office Hadley Centre, London Road, Bracknell, Berkshire RG12 2SY, U.K. 7DSM/LSCE, Laboratoire des Sciences du Climat et de l’Environnement, Unité mixte de Recherche CEA-CNRS, Bat. 709 Orme des Merisiers, 91191 Gif-sur-Yvette, France 8Dept. of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139-4307, U.S.A. 9Department of Biology and Nicholas School of the Environmental and Earth Sciences, Phytotron Bldg., Science Dr., Box 90340, Duke University, Durham, NC 27708, U.S.A. 10Dept. of Civil Engineering, Colorado State University, Fort Collins, CO 80523, U.S.A. Abstract. The Earth’s climate system is highly nonlinear: inputs and outputs are not proportional, change is often episodic and abrupt, rather than slow and gradual, and multiple equilibria are the norm. -
Modeling Vascular Homeostasis and Improving Data Filtering Methods for Model Calibration
Modeling Vascular Homeostasis and Improving Data Filtering Methods for Model Calibration by Jiacheng Wu A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Engineering − Mechanical Engineering and the Designated Emphasis in Computational Science and Engineering in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Shawn C. Shadden, Chair Professor Grace D. O'Connell Professor Peter L. Bartlett Spring 2019 Modeling Vascular Homeostasis and Improving Data Filtering Methods for Model Calibration Copyright 2019 by Jiacheng Wu 1 Abstract Modeling Vascular Homeostasis and Improving Data Filtering Methods for Model Calibration by Jiacheng Wu Doctor of Philosophy in Engineering − Mechanical Engineering and the Designated Emphasis in Computational Science and Engineering University of California, Berkeley Professor Shawn C. Shadden, Chair Vascular homeostasis is the preferred state that blood vessels try to maintain against external mechanical and chemical stimuli. The vascular adaptive behavior around the homeostatic state is closely related to cardiovascular disease progressions such as arterial aneurysms. In this work, we develop a multi-physics computational framework that couples vascular growth & remodeling (G&R), wall mechanics and hemodynamics to describe the overall vascular adaptive behavior. The coupled simulation is implemented in patient-specific geometries to predict aneurysm progression. Lyapunov stability analysis of the governing -
Fundamentals of Backstepping Control
Appendix Fundamentals of Backstepping Control This Appendix provides the fundamentals of the recursive backstepping control method. The presented material is a summary of more detailed descriptions that may be found in [43, 49]. Lyapunov-based controller design may be systematically tack- led by a recursive design procedure called backstepping. Backstepping is suitable for strict-feedback systems that are also known as “lower triangular”. An example of a strict-feedback systems is: ˙ ξ1 = f1(ξ1) + g1(ξ1)ξ2 ˙ ξ2 = f2(ξ1,ξ2) + g2(ξ1,ξ2)ξ3 . (A.1) ˙ ξr−1 = fr−1(ξ1,ξ2,...,ξr−1) + gr−1(ξ1,ξ2,...,ξr−1)ξr ˙ ξr = fr (ξ1,ξ2,...,ξr ) + gr (ξ1,ξ2,...,ξr )u where ξ1,...,ξr ∈ R, u ∈ R is the control input and fi , gi for i = 1,...,r are known functions. A typical feedback linearization approach in most cases leads to cancellation of useful nonlinearities. The backstepping design exhibits more flexi- bility compared to feedback linearization since it does not require that the resulting input–output dynamics be linear. Cancellation of potentially useful nonlinearities can be avoided resulting in less complex controllers. The main idea is to use some of the state variables of (A.1) as “virtual controls” or “pseudo controls”, and de- pending on the dynamics of each state, design intermediate control laws. The back- stepping design is a recursive procedure where a Lyapunov function is derived for the entire system. The recursive procedure can be easily expanded from the nominal case of a system augmented by an integrator. This is also referred to as integra- tor backstepping.