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A guide to common control principles and how they are used to characterize a variety of physiological mechanisms
The second edition of Physiological Control Systems offers an updated and comprehensive resource that reviews the fundamental concepts of classical control theory and how engineering methodology can be applied to obtain a quantitative understanding of physiological systems. The revised text also contains more advanced topics that feature applications to physiology of nonlinear dynamics, parameter estimation methods, and adaptive estimation and control. The author-a noted expert in the field-includes a wealth of worked examples that illustrate key concepts and methodology and offers in-depth analyses of selected physiological control models that highlight the topics presented.
The author discusses the most noteworthy developments in system identification, optimal control, and nonlinear dynamical analysis and targets recent bioengineering advances. Designed to be a practical resource, the text includes guided experiments with simulation models (using Simulink/Matlab). Physiological Control Systems focuses on common control principles that can be used to characterize a broad variety of physiological mechanisms. This revised resource:
Written for biomedical engineering students and biomedical scientists, Physiological Control Systems, offers an updated edition of this key resource for understanding classical control theory and its application to physiological systems. It also contains contemporary topics and methodologies that shape bioengineering research today.
MICHAEL C.K. KHOO is a Professor of Biomedical Engineering and Pediatrics in the Biomedical Engineering Department at the University of Southern California. He is a fellow of the IEEE, Biomedical Engineering Society, the American Institute of Medical and Biological Engineering, and the International Academy of Medical and Biological Engineering.
Preface xiii
About the Companion Website xvii
1 Introduction 1
1.1 Preliminary Considerations, 1
1.2 Historical Background, 2
1.3 Systems Analysis: Fundamental Concepts, 4
1.4 Physiological Control Systems Analysis: A Simple Example, 6
1.5 Differences Between Engineering and Physiological Control Systems, 8
1.6 The Science (and Art) of Modeling, 11
1.7 "Systems Physiology" Versus "Systems Biology", 12
Problems, 13
Bibliography, 15
2 Mathematical Modeling 17
2.1 Generalized System Properties, 17
2.2 Models with Combinations of System Elements, 21
2.3 Linear Models of Physiological Systems: Two Examples, 24
2.4 Conversions Between Electrical and Mechanical Analogs, 27
2.5 Distributed-Parameter Versus Lumped-Parameter Models, 29
2.6 Linear Systems and the Superposition Principle, 31
2.7 Zero-Input and Zero-State Solutions of ODEs, 33
2.8 Laplace Transforms and Transfer Functions, 34
2.8.1 Solving ODEs with Laplace Transforms, 36
2.9 The Impulse Response and Linear Convolution, 38
2.10 State-Space Analysis, 40
2.11 Computer Analysis and Simulation: MATLAB and SIMULINK, 43
Problems, 49
Bibliography, 53
3 Static Analysis of Physiological Systems 55
3.1 Introduction, 55
3.2 Open-Loop Versus Closed-Loop Systems, 56
3.3 Determination of the Steady-State Operating Point, 59
3.4 Steady-State Analysis Using SIMULINK, 63
3.5 Regulation of Cardiac Output, 66
3.5.1 The Cardiac Output Curve, 67
3.5.2 The Venous Return Curve, 69
3.5.3 Closed-Loop Analysis: Heart and Systemic Circulation Combined, 73
3.6 Regulation of Glucose Insulin, 74
3.7 Chemical Regulation of Ventilation, 78
3.7.1 The Gas Exchanger, 80
3.7.2 The Respiratory Controller, 82
3.7.3 Closed-Loop Analysis: Lungs and Controller Combined, 82
Problems, 86
Bibliography, 91
4 Time-Domain Analysis of Linear Control Systems 93
4.1 Linearized Respiratory Mechanics: Open-Loop Versus Closed-Loop, 93
4.2 Open-Loop Versus Closed-Loop Transient Responses: First-Order Model, 96
4.2.1 Impulse Response, 96
4.2.2 Step Response, 97
4.3 Open-Loop Versus Closed-Loop Transient Responses: Second-Order Model, 98
4.3.1 Impulse Responses, 98
4.3.2 Step Responses, 103
4.4 Descriptors of Impulse and Step Responses, 107
4.4.1 Generalized Second-Order Dynamics, 107
4.4.2 Transient Response Descriptors, 111
4.5 Open-Loop Versus Closed-Loop Dynamics: Other Considerations, 114
4.5.1 Reduction of the Effects of External Disturbances, 114
4.5.2 Reduction of the Effects of Parameter Variations, 115
4.5.3 Integral Control, 116
4.5.4 Derivative Feedback, 118
4.5.5 Minimizing Effect of External Disturbances by Feedforward Gain, 119
4.6 Transient Response Analysis Using MATLAB, 121
4.7 SIMULINK Application 1: Dynamics of Neuromuscular Reflex Motion, 122
4.7.1 A Model of Neuromuscular Reflex Motion, 122
4.7.2 SIMULINK Implementation, 126
4.8 SIMULINK Application 2: Dynamics of Glucose-Insulin Regulation, 127
4.8.1 The Model, 127
4.8.2 Simulations with the Model, 131
Problems, 131
Bibliography, 135
5 Frequency-Domain Analysis of Linear Control Systems 137
5.1 Steady-State Responses to Sinusoidal Inputs, 137
5.1.1 Open-Loop Frequency Response, 137
5.1.2 Closed-Loop Frequency Response, 141
5.1.3 Relationship between Transient and Frequency Responses, 143
5.2 Graphical Representations of Frequency Response, 145
5.2.1 Bode Plot Representation, 145
5.2.2 Nichols Charts, 147
5.2.3 Nyquist Plots, 148
5.3 Frequency-Domain Analysis Using MATLAB and SIMULINK, 152
5.3.1 Using MATLAB, 152
5.3.2 Using SIMULINK, 154
5.4 Estimation of Frequency Response from Input-Output Data, 156
5.4.1 Underlying Principles, 156
5.4.2 Physiological Application: Forced Oscillation Technique in Respiratory Mechanics, 157
5.5 Frequency Response of a Model of Circulatory Control, 159
5.5.1 The Model, 159
5.5.2 Simulations with the Model, 160
5.5.3 Frequency Response of the Model, 162
Problems, 164
Bibliography, 165
6 Stability Analysis: Linear Approaches 167
6.1 Stability and Transient Response, 167
6.2 Root Locus Plots, 170
6.3 Routh-Hurwitz Stability Criterion, 174
6.4 Nyquist Criterion for Stability, 176
6.5 Relative Stability, 181
6.6 Stability Analysis of the Pupillary Light Reflex, 184
6.6.1 Routh-Hurwitz Analysis, 186
6.6.2 Nyquist Analysis, 187
6.7 Model of Cheyne-Stokes Breathing, 190
6.7.1 CO2 Exchange in the Lungs, 190
6.7.2 Transport Delays, 192
6.7.3 Controller Responses, 193
6.7.4 Loop Transfer Functions, 193
6.7.5 Nyquist Stability Analysis Using MATLAB, 194
Problems, 196
Bibliography, 198
7 Digital Simulation of Continuous-Time Systems 199
7.1 Preliminary Considerations: Sampling and the Z-Transform, 199
7.2 Methods for Continuous-Time to Discrete-Time Conversion, 202
7.2.1 Impulse Invariance, 202
7.2.2 Forward Difference, 203
7.2.3 Backward Difference, 204
7.2.4 Bilinear Transformation, 205
7.3 Sampling, 207
7.4 Digital Simulation: Stability and Performance Considerations, 211
7.5 Physiological Application: The Integral Pulse Frequency Modulation Model, 216
Problems, 221
Bibliography, 224
8 Model Identification and Parameter Estimation 225
8.1 Basic Problems in Physiological System Analysis, 225
8.2 Nonparametric and Parametric Identification Methods, 228
8.2.1 Numerical Deconvolution, 228
8.2.2 Least-Squares Estimation, 230
8.2.3 Estimation Using Correlation Functions, 233
8.2.4 Estimation in the Frequency Domain, 235
8.2.5 Optimization Techniques, 237
8.3 Problems in Parameter Estimation: Identifiability and Input Design, 243
8.3.1 Structural Identifiability, 243
8.3.2 Sensitivity Analysis, 244
8.3.3 Input Design, 248
8.4 Identification of Closed-Loop Systems: "Opening the Loop", 252
8.4.1 The Starling Heart-Lung Preparation, 253
8.4.2 Kao's Cross-Circulation Experiments, 253
8.4.3 Artificial Brain Perfusion for Partitioning Central and Peripheral Chemoreflexes, 255
8.4.4 The Voltage Clamp, 256
8.4.5 Opening the Pupillary Reflex Loop, 257
8.4.6 Read Rebreathing Technique, 259
8.5 Identification Under Closed-Loop Conditions: Case Studies, 260
8.5.1 Minimal Model of Blood Glucose Regulation, 262
8.5.2 Closed-Loop Identification of the Respiratory Control System, 267
8.5.3 Closed-Loop Identification of Autonomic Control Using Multivariate ARX Models, 273
8.6 Identification of Physiological Systems Using Basis Functions, 276
8.6.1 Reducing Variance in the Parameter Estimates, 276
8.6.2 Use of Basis Functions, 277
8.6.3 Baroreflex and Respiratory Modulation of Heart Rate Variability, 279
Problems, 283
Bibliography, 285
9 Estimation and Control of Time-Varying Systems 289
9.1 Modeling Time-Varying Systems: Key Concepts, 289
9.2 Estimation of Models with Time-Varying Parameters, 293
9.2.1 Optimal Estimation: The Wiener Filter, 293
9.2.2 Adaptive Estimation: The LMS Algorithm, 294
9.2.3 Adaptive Estimation: The RLS Algorithm, 296
9.3 Estimation of Time-Varying Physiological Models, 300
9.3.1 Extending Adaptive Estimation Algorithms to Other Model Structures, 300
9.3.2 Adaptive Estimation of Pulmonary Gas Exchange, 300
9.3.3 Quantifying Transient Changes in Autonomic Cardiovascular Control, 304
9.4 Adaptive Control of Physiological Systems, 307
9.4.1 General Considerations, 307
9.4.2 Adaptive Buffering of Fluctuations in Arterial PCO2, 308
Problems, 313
Bibliography, 314
10 Nonlinear Analysis of Physiological Control Systems 317
10.1 Nonlinear Versus Linear Closed-Loop Systems, 317
10.2 Phase-Plane Analysis, 320
10.2.1 Local Stability: Singular Points, 322
10.2.2 Method of Isoclines, 325
10.3 Nonlinear Oscillators, 329
10.3.1 Limit Cycles, 329
10.3.2 The van der Pol Oscillator, 329
10.3.3 Modeling Cardiac Dysrhythmias, 336
10.4 The Describing Function Method, 342
10.4.1 Methodology, 342
10.4.2 Application: Periodic Breathing with Apnea, 345
10.5 Models of Neuronal Dynamics, 348
10.5.1 The Hodgkin-Huxley Model, 349
10.5.2 The Bonhoeffer-van der Pol Model, 352
10.6 Nonparametric Identification of Nonlinear Systems, 359
10.6.1 Volterra-Wiener Kernel Approach, 360
10.6.2 Nonlinear Model of Baroreflex and Respiratory Modulated Heart Rate, 364
10.6.3 Interpretations of Kernels, 367
10.6.4 Higher Order Nonlinearities and Block-Structured Models, 369
Problems, 370
Bibliography, 374
11 Complex Dynamics in Physiological Control Systems 377
11.1 Spontaneous Variability, 377
11.2 Nonlinear Control Systems with Delayed Feedback, 380
11.2.1 The Logistic Equation, 380
11.2.2 Regulation of Neutrophil Density, 384
11.2.3 Model of Cardiovascular Variability, 387
11.3 Coupled Nonlinear Oscillators: Model of Circadian Rhythms, 397
11.4 Time-Varying Physiological Closed-Loop Systems: Sleep Apnea Model, 401
11.5 Propagation of System Noise in Feedback Loops, 409
Problems, 415
Bibliography, 416
Appendix A Commonly Used Laplace Transform Pairs 419
Appendix B List of MATLAB and SIMULINK Programs 421
Index 425
A control system may be defined as a collection of interconnected components that can be made to achieve a desired response in the face of external disturbances. The "desired response" could be the tracking of a specified dynamic trajectory, in which case the control system takes the form of a servomechanism. An example of this type of control system is a robot arm that is programmed to grasp some object and to move it to a specified location. There is a second class of control system termed the regulator, for which the "desired response" is to maintain a certain physical quantity within specified limits. A simple example of this kind of control system is the thermostat.
There are two basic ways in which a control system can be made to operate. In open-loop mode, the response of the system is determined only by the controlling input(s). As an example, let us suppose that we wish to control the temperature of a room in winter with the use of a fan-heater that heats up and circulates the air within the room. By setting the temperature control to "medium," for instance, we should be able to get the room temperature to settle down to an agreeable level during the morning hours. However, as the day progresses and the external environment becomes warmer, the room temperature also will rise, because the rate at which heat is added by the fan-heater exceeds the rate at which heat is dissipated from the room. Conversely, when night sets in and the external temperature falls, the temperature in the room will decrease below the desired level unless the heater setting is raised. This is a fundamental limitation of open-loop control systems. They can perform satisfactorily as long as the external conditions do not affect the system much. The simple example we have described may be considered a physical analog of thermoregulatory control in poikilothermic or "cold-blooded" animals. The design of the thermoregulatory processes in these animals do not allow core body temperature to be maintained at a level independent of external conditions; as a consequence, the animal's metabolism also becomes a function of external temperature.
Coming back to the example of the heating system, one way to overcome its limitation might be to anticipate the external changes in temperature and to "preprogram" the temperature setting accordingly. But how would we know what amounts of adjustment are required under the different external temperature conditions? Furthermore, while the external temperature generally varies in a roughly predictable pattern, there will be occasions when this pattern is disrupted. For instance, the appearance of a heavy cloud cover during the day could limit the temperature increase that is generally expected. These problems can be eliminated by making the heater "aware" of changes in the room temperature, thereby allowing it to respond accordingly. One possible scheme might be to measure the room temperature, compare the measured temperature with the desired room temperature, and adjust the heater setting in proportion to the difference between these two temperatures. This arrangement is known as proportional feedback control. There are, of course, other control strategies that make use of the information derived from measurements of the room temperature. Nevertheless, there is a common feature in all these control schemes: They all employ feedback. The great mathematician-engineer, Norbert Wiener (1961), characterized feedback control as "a method of controlling a system by reinserting into it the results of its past performance." In our example, the system output (the measured room temperature) is "fed back" and used to adjust the input (fan speed). As a consequence, what we now have is a control system that operates in closed-loop mode, which also allows the system to be self-regulatory. This strategy of control is ubiquitous throughout Nature: The physiological analog of the simple example we have been considering is the thermoregulatory control system of homeothermic or "warm-blooded" animals. However, as we will demonstrate throughout this book, the exact means through which closed-loop control is achieved in physiological systems invariably turns out to be considerably more complicated than one might expect.
The concept of physiological regulation dates back to ancient Greece (~500 BC), where the human body was considered a small replica of the universe. The four basic elements of the universe - air, water, fire, and earth - were represented in the body by blood, phlegm, yellow bile, and black bile, respectively. The interactions among pairs of these elements produced the four irreducible qualities of wetness, warmth, dryness, and cold. It was the harmonious balance among these elements and qualities that led to the proper functioning of the various organ systems. The Greek physician, Galen (about second century AD), consolidated these traditional theories and promoted a physiological theory that was largely held until the end of the sixteenth century. Similar concepts that developed alongside the Taoist school of thought may be traced back to the third century BC in ancient China. Here, the universe was composed of five agents (Wu Xing): wood, fire, earth, metal, and water. These elements interacted with one another in two ways - one was a productive relationship, in which one agent would enhance the effects of the other; the other was a limiting or destructive relationship whereby one agent would constrain the effects of the other. As in the Graeco-Roman view, health was maintained by the harmonious balancing of these agents with one another (Unschuld, 1985).
The notion of regulatory control clearly persisted in the centuries that followed, as the writings of various notable physiologists such as Boyle, Lavoisier, and Pflüger demonstrate. However, this concept remained somewhat vague until the end of the nineteenth century when French physiologist Claude Bernard thought about self-regulation in more precise terms. He noted that the cells of higher organisms were always bathed in a fluid medium, for example, blood or lymph, and that the conditions of this environment were maintained with great stability in the face of disturbances to the overall physiology of the organism. The maintenance of these relatively constant conditions was achieved by the organism itself. This observation so impressed him that he wrote: "It is the fixity of the 'milieu interieur' which is the condition of free and independent life." He added further that "all the vital mechanisms, however varied they may be, have only one object, that of preserving constant the conditions of life in the internal environment." In the earlier half of this century, Harvard physiologist Walter Cannon (1939) refined Bernard's ideas further and demonstrated systematically these concepts in the workings of various physiological processes, such as the regulation of adequate water and food supply through thirst and hunger sensors, the role of the kidneys in regulating excess water, and the maintenance of blood acid-base balance. He went on to coin the word homeostasis to describe the maintenance of relatively constant physiological conditions. However, he was careful to distinguish the second part of the term, that is, "stasis," from the word "statics," since he was well aware that although the end result was a relatively unchanging condition, the coordinated physiological processes that produce this state are highly dynamic.
Armed with the tools of mathematics, Wiener in the 1940s explored the notion of feedback to a greater level of detail than had been done previously. Mindful that most physiological systems were nonlinear, he laid the foundation for modeling nonlinear dynamics from a Volterra series perspective. He looked into the problem of instability in neurological control systems and examined the connections between instability and physiological oscillations. He coined the word "cybernetics" to describe the application of control theory to physiology, but with the passage of time, this term has come to take on a meaning more closely associated with robotics. The race to develop automatic airplane, radar, and other military control systems during the Second World War provided a tremendous boost to the development of control theory. In the post-war period, an added catalyst for even greater progress was the development of digital computers and the growing availability of facilities for the numerical solution of the complex control problems. Since then, research on physiological control systems has become a field of study on its own, with major contributions coming from a mix of physiologists, mathematicians, and engineers. These pioneers of "modern" physiological control systems analysis include Adolph (1961), Grodins (1963), Clynes and Milsum (1970), Milhorn (1966), Milsum (1966), Bayliss (1966), Stark (1968), Riggs (1970), Guyton et al. (1973), and Jones (1973).
Prior to analyzing or designing a control system, it is useful to define explicitly the major variables and structures involved in the problem. One common way of doing this is to construct a block diagram. The block diagram captures in schematic form the relationships among the variables...
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