Synchronization of chaotic systems by using occasional coupling

Ankara : The Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1997.Thesis (Master's) -- Bilkent University, 1997.Includes bibliographical references leaves 84-88.Nonlinear and chaotic systems are difficult to control due to their unstable and unpredictable nature. Although, much work has been done in this area, synchronization of chaotic systems still remains a worthwhile endeavor. In this thesis, a method to synchronize systems, inherently operating in a chaotic mode, by using occasional coupling is presented. We assume that a masterslave synchronizing scheme is available. This approach consists of coupling and uncoupling the drive and response systems during some alternated intervals. It is then shown how this synchronization method can be used to transmit information on a chaotic carrier. The applicability of this method will be illustrated using Lorenz system as the chaotic oscillator.Feki, MoezM.S

Control of chaos in nonlinear circuits and systems

Nonlinear circuits and systems, such as electronic circuits (Chapter 5), power converters (Chapter 6), human brains (Chapter 7), phase lock loops (Chapter 8), sigma delta modulators (Chapter 9), etc, are found almost everywhere. Understanding nonlinear behaviours as well as control of these circuits and systems are important for real practical engineering applications. Control theories for linear circuits and systems are well developed and almost complete. However, different nonlinear circuits and systems could exhibit very different behaviours. Hence, it is difficult to unify a general control theory for general nonlinear circuits and systems. Up to now, control theories for nonlinear circuits and systems are still very limited. The objective of this book is to review the state of the art chaos control methods for some common nonlinear circuits and systems, such as those listed in the above, and stimulate further research and development in chaos control for nonlinear circuits and systems. This book consists of three parts. The first part of the book consists of reviews on general chaos control methods. In particular, a time-delayed approach written by H. Huang and G. Feng is reviewed in Chapter 1. A master slave synchronization problem for chaotic Lur’e systems is considered. A delay independent and delay dependent synchronization criteria are derived based on the H performance. The design of the time delayed feedback controller can be accomplished by means of the feasibility of linear matrix inequalities. In Chapter 2, a fuzzy model based approach written by H.K. Lam and F.H.F. Leung is reviewed. The synchronization of chaotic systems subject to parameter uncertainties is considered. A chaotic system is first represented by the fuzzy model. A switching controller is then employed to synchronize the systems. The stability conditions in terms of linear matrix inequalities are derived based on the Lyapunov stability theory. The tracking performance and parameter design of the controller are formulated as a generalized eigenvalue minimization problem which is solved numerically via some convex programming techniques. In Chapter 3, a sliding mode control approach written by Y. Feng and X. Yu is reviewed. Three kinds of sliding mode control methods, traditional sliding mode control, terminal sliding mode control and non-singular terminal sliding mode control, are employed for the control of a chaotic system to realize two different control objectives, namely to force the system states to converge to zero or to track desired trajectories. Observer based chaos synchronizations for chaotic systems with single nonlinearity and multi-nonlinearities are also presented. In Chapter 4, an optimal control approach written by C.Z. Wu, C.M. Liu, K.L. Teo and Q.X. Shao is reviewed. Systems with nonparametric regression with jump points are considered. The rough locations of all the possible jump points are identified using existing kernel methods. A smooth spline function is used to approximate each segment of the regression function. A time scaling transformation is derived so as to map the undecided jump points to fixed points. The approximation problem is formulated as an optimization problem and solved via existing optimization tools. The second part of the book consists of reviews on general chaos controls for continuous-time systems. In particular, chaos controls for Chua’s circuits written by L.A.B. Tôrres, L.A. Aguirre, R.M. Palhares and E.M.A.M. Mendes are discussed in Chapter 5. An inductorless Chua’s circuit realization is presented, as well as some practical issues, such as data analysis, mathematical modelling and dynamical characterization, are discussed. The tradeoff among the control objective, the control energy and the model complexity is derived. In Chapter 6, chaos controls for pulse width modulation current mode single phase H-bridge inverters written by B. Robert, M. Feki and H.H.C. Iu are discussed. A time delayed feedback controller is used in conjunction with the proportional controller in its simple form as well as in its extended form to stabilize the desired periodic orbit for larger values of the proportional controller gain. This method is very robust and easy to implement. In Chapter 7, chaos controls for epileptiform bursting in the brain written by M.W. Slutzky, P. Cvitanovic and D.J. Mogul are discussed. Chaos analysis and chaos control algorithms for manipulating the seizure like behaviour in a brain slice model are discussed. The techniques provide a nonlinear control pathway for terminating or potentially preventing epileptic seizures in the whole brain. The third part of the book consists of reviews on general chaos controls for discrete-time systems. In particular, chaos controls for phase lock loops written by A.M. Harb and B.A. Harb are discussed in Chapter 8. A nonlinear controller based on the theory of backstepping is designed so that the phase lock loops will not be out of lock. Also, the phase lock loops will not exhibit Hopf bifurcation and chaotic behaviours. In Chapter 9, chaos controls for sigma delta modulators written by B.W.K. Ling, C.Y.F. Ho and J.D. Reiss are discussed. A fuzzy impulsive control approach is employed for the control of the sigma delta modulators. The local stability criterion and the condition for the occurrence of limit cycle behaviours are derived. Based on the derived conditions, a fuzzy impulsive control law is formulated so that the occurrence of the limit cycle behaviours, the effect of the audio clicks and the distance between the state vectors and an invariant set are minimized supposing that the invariant set is nonempty. The state vectors can be bounded within any arbitrary nonempty region no matter what the input step size, the initial condition and the filter parameters are. The editors are much indebted to the editor of the World Scientific Series on Nonlinear Science, Prof. Leon Chua, and to Senior Editor Miss Lakshmi Narayan for their help and congenial processing of the edition

Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...

고차원 로렌츠 시스템, 대기예측성 및 자료동화

학위논문(박사) -- 서울대학교대학원 : 자연과학대학 지구환경과학부, 2021.8. 문승주.로렌츠 시스템은 레일리 베나르 대류 현상의 단순한 모형으로 처음 고안되었으나, 이후 야릇한 끌개의 발견 및 혼돈 이론의 급속한 발전에 대한 기여 등을 통해 그 중요성이 꾸준히 부각되어 왔다. 본 연구에서는 두 가지 접근 방식을 통해 로렌츠 시스템을 고차원으로 확장하고자 하였다. 첫번째 접근 방식은 유도 과정에서 비롯되는 푸리에 급수의 절단에 있어 추가 모드를 통해 차수를 확장하는 방법으로, 본 연구에서는 이를 일반화 하여 임의의 자연수 $N$에 대한 $(3N)$ 및 $(3N+2)$차 로렌츠 시스템을 유도하였다. 두번째는 물리적 확장이라 불리는 방식으로, 레일리 베나르 대류 현상을 관장하는 지배방정식에 나타내고자 하는 물리 성분을 추가하여 더 높은 차수의 방정식계를 얻는 과정이다. 이에 추가 물리 성분으로 모형 프레임의 회전과 내부에 부유하는 오염 물질 따위의 스칼라를 고려하여 새로운 6차 로렌츠 시스템을 유도 할 수 있었다. 이렇게 얻어진 고차원 로렌츠 시스템은 비선형성, 대칭성, 소산성 등의 공통된 특징을 지닌다. 새롭게 확장된 로렌츠 시스템의 해의 특성 및 그들이 나타내는 다양한 비선형 현상의 규명은 수치 적분을 통해 얻은 해의 분석을 바탕으로 이루어졌다. 이를 위해 카오스 이론에 입각한 여러가지 분석 방법이 활용되었는데, 이러한 분석방법에는 파라미터 공간 상의 주기성도표, 분기도표 및 리아푸노프 지수 그리고 위상 공간 내 해의 궤도 및 프렉탈 흡인경계 등이 있다. 밝혀진 비선형 동역학적 현상 중 특히 주목할 만한 현상에는 파라미터 값에 따른 분기 구조의 변동, 하나의 위상 공간 내 존재하는 여러 타입의 해의 공존, 카오스의 동기화 등이 있다. 본 연구에서는 이러한 현상의 수학적~$\cdot$~수치적 분석뿐만 아니라 이것이 대기과학 특히 자료동화와 대기예측성 분야에 함의하는 바가 무엇인지도 탐구하였다. 본 연구에서 제안된 일반화 방식에 따라 로렌츠 시스템의 차수를 올리면 분기 구조에 변동이 일어나 임계 레일리 파라미터의 증가가 비롯된다. 여기서 임계 레일리 파라미터는 카오스가 처음 발생하는 가장 낮은 레일리 파라미터 값이므로 이것이 차수에 따라 증가한다는 것은 즉 고차원 로렌츠 시스템에서는 카오스의 발생이 저차원에서보다 더 어렵다는 것을 의미한다. 차수 및 파라미터 공간에 그려진 주기성 도표를 보면 카오스가 존재하는 영역이 차수에 따라 점점 줄어들고, 어느 차수 이상부터는 사라지는 것을 확인 할 수 있다. 마찬가지로 물리적으로 확장된 로렌츠 시스템에서는 임계 레일리 파라미터가 추가된 물리현상을 나타내는 새로운 파라미터의 값을 증가시킴에 따라 증가하는 것을 알 수 있다. 한편 유체 내 스칼라 효과와 연관된 파라미터만 점진적으로 올릴 경우에는 시스템의 불안정을 야기하는 레일리 파라미터와 안정을 야기하는 스칼라 관련 파라미터 간의 경쟁으로 인해 시스템이 완전히 안정화 되기 전 카오스 해가 한번 더 발생하는 현상이 일어난다. 이 두번째 카오스에 대응되는 끌개는 기존에 알려진 로렌츠 끌개와는 사뭇 다른 모양새를 보인다. 해의 공존 현상은 로렌츠에 의해 밝혀진 해의 초기조건에 대한 민감도와는 구분되는 개념으로, 초기조건으로 인한 카오스 해 간의 차이가 증폭되는 이른바 나비효과와는 달리 초기조건에 따라 완전히 다른 타입의 해를 나타내는 끌개가 같은 위상공간에 공존함을 의미한다. 따라서 만약 실제 날씨를 나타내는 시스템이 상존하는 위상공간에서 이러한 해의 공존이 실제한다면 이것은 카오스의 초기조건에 대한 민감성과 더불어 대기예측성 특히 앙상블 예보에 이론적으로 시사하는 바가 클 것으로 생각된다. 물리적으로 확장된 로렌츠 시스템에서는 기존 로렌츠 시스템과 같이 레일리 파라미터의 변화에 따른 분기 구조의 부정합으로 인해 비롯되는 해의 공존이 나타난다. 해의 공존 가능성이 높은 파라미터 조합을 찾아내기 위해 물리적으로 확장된 6차 로렌츠 시스템의 분기구조를 수치적~$\cdot$~해석적 방법으로 도출하였고 초기조건에 따라 서로 다른 두가지 종류의 분기 즉 호프 및 헤테로클리닉 분기가 엇갈리는 구간을 집중적으로 분석하였다. 기존 3차 로렌츠 시스템에서 하나의 변수에 대한 정보 전달 만으로도 자기동기화 현상이 일어남은 이미 잘 알려진 사실이다. 본 연구에서는 물리적으로 확장된 로렌츠 시스템에서도 기존 로렌츠 시스템과 같은 조건 하에서 카오스의 자기동기화가 일어나는 점을 적절한 리아푸노프 함수의 제시를 통해 증명하였다. 일반화된 로렌츠 시스템의 자기동기화에 대해서는 비록 수학적 증명이 동반되지는 않았지만 수치적 방법을 통해 역시 같은 조건 하에서 자기동기화가 일어남을 뒷받침 할 근거를 제시하였다. 또한 수치 실험을 통해 서로 다른 차수를 가진 일반화된 로렌츠 시스템 간 동기화가 일어나는 정도가 상호 차수를 기반으로 한 두 시스템 사이의 거리와 음의 상관관계를 가진다는 점도 확인하였다. 추가 푸리에 모드를 포함하여 더 작은 스케일의 운동을 분해할 수 있는 고차원 로렌츠 시스템과 그렇게 하지 못하는 저차원 로렌츠 시스템 간 동기화의 가능성은 대기과학에서 특히 대기 모형 및 자료동화에 있어 중요한 개념적인 함의를 가진다. 본 연구에서는 특별히 앙상블 칼만 필터 자료동화 기법을 일례로 일반화된 로렌츠 시스템이 자료동화 기법의 비교적 단순한 테스트베드로써의 역할을 할 수 있는지 탐구하였다. 카오스 동기화 현상에 기반을 둔 개념적 도식으로 발신자를 실제 대기 현상, 수신자를 대기 모형, 그리고 발신자에서 수신자로 전달되는 정보를 관측에 대응시킴으로써 수신자와 발신자 간의 오차, 발신자에서 수신자로 전달할 정보 추출 과정에서 비롯되는 오차 등을 통해 실제 대기 모형과 관측의 불완전함을 개념적으로 모의할 수 있었다. 일반화된 로렌츠 시스템에서 초기조건에 아주 작은 섭동을 준 해와 그렇지 않은 해 간의 비교를 통해 이것이 대기예측성에 함의하는 바가 무엇인지 탐구하였다. 이때 이렇게 두 해가 벌어지는 정도가 기준값을 넘게 되는 시간을 편차시간이라 칭한다. 본 연구에서는 편차시간이 적어도 주어진 파라미터 값 하에서는 로렌츠 시스템의 차수에 대한 강한 비단조적 의존성을 보임을 발견하였다. 또한 이렇게 정의된 편차시간을 활용하여 실제 날씨 사례의 수치 예보 모의에서 나타나는 대기예측성을 측정하였을때, 마찬가지로 대기예측성이 연직해상도에 대한 비단조적 의존성을 보이는 것을 확인할 수 있었다. 이에 이러한 비단조적 의존성의 근본적인 원인은 모형의 대기 나아가 실제 날씨에 내재된 카오스에 있을 수 있음을 제안하였다.The Lorenz system is a simplified model of Rayleigh--B\'{e}nard convection whose importance lies not only in understanding the fluid convection problem but also in its formative role in the discovery of strange attractors and the subsequent development of the modern theory of chaos. In this dissertation, two different approaches to extending the Lorenz system to higher dimensions are considered. First, by including additional wavenumber modes at the series truncation stage of the derivation, the so-called high-order Lorenz systems are obtained up to dimension 11, which are then generalized into $(3N)$ and $(3N+2)$ dimensions for any positive integer $N$. Second, by incorporating additional physical ingredients, namely, rotation and density-affecting scalar in the governing equations, a new 6-dimensional physically extended Lorenz system is derived. All of these high-dimensional extensions of the Lorenz system are shown to share some basic properties such as nonlinearity, symmetry, and volume contraction. The numerically obtained solutions of the extended Lorenz systems are studied through periodicity diagrams, bifurcation diagrams, and Lyapunov exponent spectra in parameter spaces and also through solution trajectories and basin boundaries in the phase space, illuminating various nonlinear dynamical phenomena such as shifts in the bifurcation structures, attractor coexistence, and chaos synchronization. Accompanying these results are discussions about their applicability and theoretical implications, particularly in the context of data assimilation and atmospheric predictability. The shifts in bifurcation structures induced by raising the dimension lead to higher critical Rayleigh parameter values, implying that it gets more difficult for chaos to emerge at higher dimensions. Periodicity diagrams reveal that the parameter ranges in which chaos resides tend to diminish with rising dimensions, eventually vanishing altogether. Likewise, simultaneously increasing the newly added parameters in the physically extended Lorenz system leads to higher critical Rayleigh parameter values; however, raising only the scalar-related parameter leads to an eventual return of chaos albeit with an attractor with qualitatively distinct features from the Lorenz attractor. The peculiar bifurcation structure shaped by the competition between the opposing effects of raising the Rayleigh and the scalar-related parameters helps explain this second onset of chaos. Attractor coexistence refers to the partition of the phase space by basin boundaries so that different types of attractors emerge depending on the initial condition. Similar to the original Lorenz system, the physically extended Lorenz system is found to exhibit attractor coexistence stemming from mismatches between the Hopf and heteroclinic bifurcations. If the atmosphere is found to exhibit such behavior, it can have grave implications for atmospheric predictability and ensemble forecasting beyond mere sensitive dependence on initial conditions, which only applies to chaotic solutions. Chaos synchronization is another curious phenomenon known to occur in the Lorenz system. By finding an appropriate Lyapunov function, the physically extended Lorenz system is shown to self-synchronize under the same condition that guarantees self-synchronization in the original Lorenz system. Regarding the generalized Lorenz systems, numerical evidence in support of self- as well as some degree of generalized synchronization, that is, synchronization between two Lorenz systems differing in their dimensions, is provided. Numerical results suggest that the smaller the dimensional difference between the two, the stronger they tend to synchronize. Some conceptual implications of such results are discussed in relation to atmospheric modeling and data assimilation. Especially, the feasibility of using the $(3N)$-dimensional Lorenz systems as a testbed for data assimilation methods is explored. For demonstration, the ensemble Kalman filter method is implemented to assimilate observations with ensembles of model outputs generated using the generalized Lorenz systems, whose imperfections are simulated through varying the severity of ensemble over- or underdispersion, dimensional differences, random forcing, and model or observation biases. Further investigation of the generalized Lorenz systems is carried out from the perspective of predictability, showing that predictability measured by deviation time, which is the time when the threshold-exceeding deviations among ensemble members occur, can respond non-monotonically to increases in the system's dimension. Accordingly, deviation time is put forward as a direct measure of predictability due to weather's sensitive dependence on initial conditions. Raising the dimension under the proposed generalizations is thought to be analogous to resolving smaller-scale motions in the vertical direction. The estimated deviation times in an ensemble of real-case simulations using a realistic numerical weather forecasting model reveal that the predictability of real-case simulations also depend non-monotonically on model vertical resolution. It is suggested that beneath this non-monotonicity fundamentally lies chaos inherent to the model atmospheres and, by extension, weather at large.1 Overview 1 1.1 Chaos and the Lorenz system 1 1.2 Extending the Lorenz system 6 1.3 Bifurcations and related phenomena 8 1.4 Chaos in the atmosphere 14 1.5 Organization of the dissertation 16 2 Chaos and Periodicity of the High-Order Lorenz Systems 18 2.1 Introduction 18 2.2 The high-order Lorenz systems 20 2.2.1 Derivation 22 2.2.2 Some properties of the Lorenz systems 24 2.3 Numerical methods 26 2.4 Results 32 2.4.1 Periodicity diagrams 32 2.4.2 Bifurcation diagrams and phase portraits 34 2.5 Discussion 40 3 A Physically Extended Lorenz System with Rotation and Density-Affecting Scalar 42 3.1 Introduction 42 3.2 Derivation 45 3.3 Effects of rotation and scalar 49 3.3.1 Fixed points and stability 49 3.3.2 Bifurcation structure in the rT-σ space 52 3.3.3 Bifurcations along rC and s 55 3.4 The case when β < 0 65 3.4.1 Bifurcation and the onset of chaos 67 3.4.2 Chaotic attractors and associated flow patterns 73 3.5 Self-synchronization 81 3.6 Discussion 85 4 Coexisting Attractors in the Physically Extended Lorenz System 87 4.1 Introduction 87 4.2 Methodology 89 4.3 Results 92 4.3.1 Coexisting attractors in the LorenzStenflo system 92 4.3.2 Coexisting attractors under rotation and scalar 100 4.4 Discussion 110 5 The (3N)- and (3N + 2)-Dimensional Generalizations of the Lorenz System 113 5.1 Introduction 113 5.2 The generalized Lorenz systems 115 5.2.1 The Pk- and Qk-sets for nonlinear terms 115 5.2.2 The (3N)- and (3N + 2)-dimensional systems 116 5.2.3 Choosing the nonlinear pairs 117 5.3 Derivation 119 5.3.1 The (3N)-dimensional generalization 121 5.3.2 The (3N + 2)-dimensional generalization 126 5.4 Effects of dimension in parameter spaces 126 5.4.1 Linear stability analysis 126 5.4.2 Chaos in dimension-parameter spaces 130 5.5 Perspectives on predictability 136 5.5.1 Notions of predictability 136 5.5.2 Twin experiments and deviation time 138 5.6 Discussion 144 6 Chaos Synchronization in the Generalized Lorenz Systems 147 6.1 Introduction 147 6.2 Self-synchronization 149 6.2.1 Numerical evidence 149 6.2.2 Error subsystems 155 6.3 Application in image encryption 157 6.3.1 Demonstration: A simple approach 157 6.3.2 Demonstration: An alternative approach 168 6.4 Beyond self-synchronization 172 6.5 Discussion 180 7 The Generalized Lorenz Systems as a Testbed for Data Assimilation: The Ensemble Kalman Filter 182 7.1 Introduction 182 7.2 Methodology 187 7.2.1 Implementation of the ensemble Kalman filter 188 7.3 Results 191 7.3.1 Effects of ensemble size and model accuracy 191 7.3.2 Effects of observation frequency and accuracy 205 7.3.3 Effects of observation and model biases 214 7.4 Discussion 218 8 Can Chaos Theory Explain Non-Monotonic Dependence of Atmospheric Predictability on Model Vertical Resolution 220 8.1 Introduction 220 8.2 Background 222 8.2.1 Lorenz's ideas about atmospheric predictability 222 8.2.2 Model vertical resolution and predictability in numerical weather prediction 224 8.3 Results 229 8.3.1 Deviation time in the Lorenz systems revisited 229 8.3.2 WRF model control simulations 232 8.3.3 WRF model ensemble experiments and deviation time 241 8.3.4 Spatial distribution of deviation time 254 8.4 Discussion 261 9 Summary and Final Remarks 264 Bibliography 271 Abstract in Korean 295 Acknowledgments 299 Index 303박

Discerning non-autonomous dynamics

Structure and function go hand in hand. However, while a complex structure can be relatively safely broken down into the minutest parts, and technology is now delving into nanoscales, the function of complex systems requires a completely different approach. Here the complexity clearly arises from nonlinear interactions, which prevents us from obtaining a realistic description of a system by dissecting it into its structural component parts. At best, the result of such investigations does not substantially add to our understanding or at worst it can even be misleading. Not surprisingly, the dynamics of complex systems, facilitated by increasing computational efficiency, is now readily tackled in the case of measured time series. Moreover, time series can now be collected in practically every branch of science and in any structural scale—from protein dynamics in a living cell to data collected in astrophysics or even via social networks. In searching for deterministic patterns in such data we are limited by the fact that no complex system in the real world is autonomous. Hence, as an alternative to the stochastic approach that is predominantly applied to data from inherently non-autonomous complex systems, theory and methods specifically tailored to non-autonomous systems are needed. Indeed, in the last decade we have faced a huge advance in mathematical methods, including the introduction of pullback attractors, as well as time series methods that cope with the most important characteristic of non-autonomous systems—their time-dependent behaviour. Here we review current methods for the analysis of non-autonomous dynamics including those for extracting properties of interactions and the direction of couplings. We illustrate each method by applying it to three sets of systems typical for chaotic, stochastic and non-autonomous behaviour. For the chaotic class we select the Lorenz system, for the stochastic the noiseforced Duffing system and for the non-autonomous the Poincaré oscillator with quasiperiodic forcing. In this way we not only discuss and review each method, but also present properties which help to clearly distinguish the three classes of systems when analysed in an inverse approach—from measured, or numerically generated data. In particular, this review provides a framework to tackle inverse problems in these areas and clearly distinguish non-autonomous dynamics from chaos or stochasticity

Computational Intelligence and Complexity Measures for Chaotic Information Processing

This dissertation investigates the application of computational intelligence methods in the analysis of nonlinear chaotic systems in the framework of many known and newly designed complex systems. Parallel comparisons are made between these methods. This provides insight into the difficult challenges facing nonlinear systems characterization and aids in developing a generalized algorithm in computing algorithmic complexity measures, Lyapunov exponents, information dimension and topological entropy. These metrics are implemented to characterize the dynamic patterns of discrete and continuous systems. These metrics make it possible to distinguish order from disorder in these systems. Steps required for computing Lyapunov exponents with a reorthonormalization method and a group theory approach are formalized. Procedures for implementing computational algorithms are designed and numerical results for each system are presented. The advance-time sampling technique is designed to overcome the scarcity of phase space samples and the buffer overflow problem in algorithmic complexity measure estimation in slow dynamics feedback-controlled systems. It is proved analytically and tested numerically that for a quasiperiodic system like a Fibonacci map, complexity grows logarithmically with the evolutionary length of the data block. It is concluded that a normalized algorithmic complexity measure can be used as a system classifier. This quantity turns out to be one for random sequences and a non-zero value less than one for chaotic sequences. For periodic and quasi-periodic responses, as data strings grow their normalized complexity approaches zero, while a faster deceasing rate is observed for periodic responses. Algorithmic complexity analysis is performed on a class of certain rate convolutional encoders. The degree of diffusion in random-like patterns is measured. Simulation evidence indicates that algorithmic complexity associated with a particular class of 1/n-rate code increases with the increase of the encoder constraint length. This occurs in parallel with the increase of error correcting capacity of the decoder. Comparing groups of rate-1/n convolutional encoders, it is observed that as the encoder rate decreases from 1/2 to 1/7, the encoded data sequence manifests smaller algorithmic complexity with a larger free distance value

Zero Speed Rotor Position Estimator based on Sliding Mode Control for Permanent Magnet Synchronous Motor

The permImplementation of an algorithm based on SMC that uses a unique technique for the rotor position estimation of a PMSM for low and zero speeds, by using inherit motor effect called "Saliency". The work proves that the rotor position estimation is possible with the information that is present in the system under SMC due to the saliency effect. Therefore, there is no need to inject any signal into the machine, which causes the increment in the losses of the machine, or to design dynamic observers. The algorithm is implemented in a DSP controller and the tests with the complete hardware platform validate the proposal in open loop and in sensorless operation

Data assimilation for conductance-based neuronal models

This dissertation illustrates the use of data assimilation algorithms to estimate unobserved variables and unknown parameters of conductance-based neuronal models. Modern data assimilation (DA) techniques are widely used in climate science and weather prediction, but have only recently begun to be applied in neuroscience. The two main classes of DA techniques are sequential methods and variational methods. Throughout this work, twin experiments, where the data is synthetically generated from output of the model, are used to validate use of these techniques for conductance-based models observing only the voltage trace. In Chapter 1, these techniques are described in detail and the estimation problem for conductance-based neuron models is derived. In Chapter 2, these techniques are applied to a minimal conductance-based model, the Morris-Lecar model. This model exhibits qualitatively different types of neuronal excitability due to changes in the underlying bifurcation structure and it is shown that the DA methods can identify parameter sets that produce the correct bifurcation structure even with initial parameter guesses that correspond to a different excitability regime. This demonstrates the ability of DA techniques to perform nonlinear state and parameter estimation, and introduces the geometric structure of inferred models as a novel qualitative measure of estimation success. Chapter 3 extends the ideas of variational data assimilation to include a control term to relax the problem further in a process that is referred to as nudging from the geoscience community. The nudged 4D-Var is applied to twin experiments from a more complex, Hodgkin-Huxley-type two-compartment model for various time-sampling strategies. This controlled 4D-Var with nonuniform time-samplings is then applied to voltage traces from current-clamp recordings of suprachiasmatic nucleus neurons in diurnal rodents to improve upon our understanding of the driving forces in circadian (~24) rhythms of electrical activity. In Chapter 4 the complementary strengths of 4D-Var and UKF are leveraged to create a two-stage algorithm that uses 4D-Var to estimate fast timescale parameters and UKF for slow timescale parameters. This coupled approach is applied to data from a conductance-based model of neuronal bursting with distinctive slow and fast time-scales present in the dynamics. In Chapter 5, the ideas of identifiability and sensitivity are introduced. The Morris-Lecar model and a subset of its parameters are shown to be identifiable through the use of numerical techniques. Chapter 6 frames the selection of stimulus waveforms to inject into neurons during patch-clamp recordings as an optimal experimental design problem. Results on the optimal stimulus waveforms for improving the identifiability of parameters for a Hodgkin-Huxley-type model are presented. Chapter 7 shows the preliminary application of data assimilation for voltage-clamp, rather than current-clamp, data and expands on voltage-clamp principles to formulate a reduced assimilation problem driven by the observed voltage. Concluding thoughts are given in Chapter 8