* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

# Download Dynamical systems

Survey

Document related concepts

Tensor operator wikipedia , lookup

Wave packet wikipedia , lookup

Bra–ket notation wikipedia , lookup

Dynamic substructuring wikipedia , lookup

Symmetry in quantum mechanics wikipedia , lookup

Relativistic quantum mechanics wikipedia , lookup

Analytical mechanics wikipedia , lookup

Derivations of the Lorentz transformations wikipedia , lookup

Density matrix wikipedia , lookup

Routhian mechanics wikipedia , lookup

Numerical continuation wikipedia , lookup

Equations of motion wikipedia , lookup

Four-vector wikipedia , lookup

Computational electromagnetics wikipedia , lookup

Transcript

Dynamical Systems 1 Introduction Ing. Jaroslav Jíra, CSc. Definition Dynamical system is a system that changes over time according to a set of fixed rules that determine how one state of the system moves to another state. Dynamical system is a state space (phase space) together with a set of functions describing change of the system in time. A dynamical system has two parts a) a State space, which determines possible values of the state vector. State vector consists of a set of variables whose values can be within certain interval. The interval of all possible values form the entire state space. b) a Function, which tells us, given the current state, what the state of the system will be in the next instant of time A state vector can be described by x (t ) [ x1 (t ), x2 (t ),......, xn (t )] A function can be described by a single function or by a set of functions f1 ( x1 , x2 ,...., xn ), f 2 ( x1 , x2 ,...., xn ),...., f n ( x1 , x2 ,...., xn ) Entire system can be then described by a set of differential equations – equations of motion dx1 x1 f1 ( x1 , x2 ,...., xn ) dt dx2 x2 f 2 ( x1 , x2 ,...., xn ) dt . . dxn xn f n ( x1 , x2 ,...., xn ) dt Classification of Dynamical Systems Dynamical system can be either or Linear Nonlinear Autonomous Nonautonomous Conservative Nonconservative Discrete Continous One-dimensional Multidimensional Linear system – a function describing the system behavior must satisfy two basic properties • additivity f ( x y ) f ( x) f ( y ) • homogeneity f ( x) f ( x) For example f(x) = 3x; f(y) = 3y; • additivity f(x+y) = 3(x+y) = 3x + 3y = f(x) + f(y) • homogeneity 5 * f(x) = 5* 3x = 15x = f(5x) Nonlinear system is described by a nonlinear function. It does not satisfy previous basic properties For example f(x) = x2; f(y) = y2; f ( x y) ( x y) 2 x 2 2 xy y 2 f ( x) f ( y) x 2 y 2 f (5x) (5 x) 2 25x 2 5 f ( x) 5x 2 Autonomous system is a system of ordinary differential equations, which do not depend on the independent variable. If the independent variable is time, we call it time-invariant system. Condition: If the input signal x(t) produces an output y(t) then any time shifted input, x(t + δ), results in a time-shifted output y(t + δ) Example: we have two systems System A: System B: y (t ) 10 x(t ) System A: Start with a delay of the input Now we delay the output by δ Clearly , therefore the system is not time-invariant or is nonautonomous. System B: Start with a delay of the input Now delay the output by δ Clearly therefore the system is time-invariant or autonomous. Conservative system - the total mechanical energy remains constant, there are no dissipations present, e.g. simple harmonic oscillator Nonconservative (dissipative) system – the total mechanical energy changes due to dissipations like friction or damping, e.g. damped harmonic oscillator Discrete system – is described be a difference equation or set of equations. In case of a single equation we are also talking about one-dimensional map. We denote time by k, and the system is typically specified by the equations x ( 0) x 0 x(k 1) f ( x(k )) x(k ) f k ( x0 ) The system can be solved by iteration calculation. Typical example is annual progress of a bank account. If the initial deposit is 100000 crowns and annual interest is 3%, then we can describe the system by x(0) 100000 x(k 1) 1.03x(k ) x(k ) 1.03k *100000 Continous system – is described by a differential equation or a set of equations. x ( 0) x 0 x´ f ( x) For example, vertical throw is described by initial conditions h(0), v(0) and equations h(t )´ v (t ) v (t )´ g where h is height and v is velocity of a body. Definition from the Mathematica: When the reals are acting, the system is called a continuous dynamical system, and when the integers are acting, the system is called a discrete dynamical system. One-dimensional system is described by a single function like x(k 1) ax(k ) b x´(t ) ax(t ) b where a,b are constants. Multidimensional system is described by a vector of functions like x (k 1) Ax (k ) B x´(t ) Ax (t ) B where x is a vector with n components, A is n x n matrix and B is a constant vector Repetition From the Matrix Algebra Matrix A a11 a12 A a21 a22 a31 a32 a13 a23 a33 Matrix B Vector C b11 b12 b13 B b21 b22 b23 b31 b32 b33 c 1 C c 2 c3 a11b11 a12b21 a13b31 a11b12 a12b22 a13b32 A.B a21b11 a22b21 a23b31 a21b12 a22b22 a23b32 a31b11 a32b21 a33b31 a31b12 a32b22 a33b32 a c a c a c 11 1 12 2 13 3 A.C a21c1 a22c2 a23c3 a31c1 a32c2 a33c3 a11b13 a12b23 a13b33 a21b13 a22b23 a23b33 a31b13 a32b23 a33b33 Identity matrix (unit matrix), we use symbol E or I Determinant 1 0 0 E 0 1 0 0 0 1 d11 d 21 D d d 22 12 det(D) d11d22 d21d12 a11 a12 A a21 a22 a31 a32 a13 a11 a12 a23 a21 a22 a33 a31 a32 det( A) a11a22a33 a12a23a31 a13a21a32 a11a23a32 a12a21a33 a13a22a31 Inversion matrix – 2x2 a b A c d 1 a b 1 d b 1 d b 1 A c a c d c a det( A ) ad cb Inversion matrix – 3x3 The basic matrix operations in the Mathematica Eigenvalues and Eigenvectors The eigenvectors of a square matrix are the non-zero vectors which, after being multiplied by the matrix, remain proportional to the original vector (i.e. change only in magnitude, not in direction). For each eigenvector, the corresponding eigenvalue λ is the factor by which the eigenvector changes when multiplied by the matrix. Au u For the eigenvalue calculation we use the formula det( A E) 0 Short explanation Two-dimensional example: a11 a12 u1 u1 a 21 a22 u2 u2 This represents a system of two linear equations: a11u1 a12u2 u1 a21u1 a22u2 u2 After small rearrangement: (a11 )u1 a12u2 0 a21u1 (a22 )u2 0 In the matrix form ( A E)u 0 This is a system of linear homogeneous equations. Such system has a nontrivial (nonzero) solution only in case when the matrix (A-λE) is singular. The matrix is singular when its determinant is equal to zero. det( A E) 0 For the eigenvector calculation we use the formula: Two-dimensional example: ( A E)u 0 a12 u1 0 a11 a a22 u2 0 21 After finding eigenvalues we can say, that we found a diagonal matrix, which is similar to the original matrix. The diagonal matrix has the same properties like the original one for the purpose of solving dynamical system stability. We write the diagonal matrix in the form: 1 0 0 0 0 0 0 2 0 0 ... 0 0 0 0 n What are advantages of diagonal matrix? 1. Multidimensional discrete system. The typical formula is: To obtain k-th element: k x (k ) A x (0) x (k 1) Ax (k ) Raising matrices to the power is quite difficult, but in case of diagonal matrix we simply have: 1k 0 k A 0 0 0 2 k 0 0 0 0 0 0 ... 0 k 0 n 2. Multidimensional continous system. The typical differential equation: Solution of the equation: dx Ax (t ) dt x (t ) x0 exp( At ) Using matrices as an argument for the exponential function is much more difficult than raising them to the power, but in case of diagonal matrix we can write: e 1 t 0 exp( At ) 0 0 0 e 2 0 0 t 0 0 0 0 ... 0 nt 0 e Example for eigenvalue and eigenvector calculation Initial matrix Characteristic equation Eigenvalues 4 2 A 1 5 2 4 det( A E) det 0 5 1 (4 )(5 ) 2 2 9 18 0 1 6; 2 3 Eigenvector calculation 2 u1 2 2 u1 0 4 1 1 u 1 1 u 0 5 2 1 2 2u1 2u2 0 u1 u2 0 1 u1 1 2 u1 1 2 u1 0 4 2 1 u 1 2 u 0 5 2 2 2 u1 2u2 0 u1 2u2 0 2 u2 1 Any vector that satisfies condition u1=u2 is an eigenvector for the λ=6 Any vector that satisfies condition u1=-2u2 is an eigenvector for the λ=3 Automatic eigenvalue and eigenvector calculation in the Mathematica Trace of a matrix is a sum of the elements on the main diagonal 3 1 0 Tr 7 5 4 6 8 4 2 Tr (A) a11 a22 .... ann Jacobian matrix is the matrix of all first-order partial derivatives of a vectoror scalar-valued function with respect to another vector. This matrix is frequently being marked as J, Df or A. dx1 x1 f1 ( x1 , x2 ,...., xn ) dt dx2 x2 f 2 ( x1 , x2 ,...., xn ) dt . . dxn xn f n ( x1 , x2 ,...., xn ) dt f1 x 1 f 2 J x1 ... f n x1 f1 x2 f 2 x2 ... f n x2 f1 xn f 2 ... xn ... f n ... xn ... Phase Portraits A phase space is a space in which all possible states of a system are represented, with each possible state of the system corresponding to one unique point in the phase space. The phase space of a two-dimensional system is called a phase plane, which occurs in classical mechanics for a single particle moving in one dimension, and where the two variables are position and velocity. A phase curve is a plot of the solution of equations of motion in a phase plane (generally in a phase space). A phase portrait is a plot of single phase curve or multiple phase curves corresponding to different initial conditions in the same phase plane. A phase portrait of a simple harmonic oscillator x 2 x 0 Differential equation where x is displacement, v is velocity, A is amplitude and ω is angular frequency. x A cos(t ) v A sin( t ) Solution of the equation Now we separate sine and cosine functions, raise both equations to the power of two and finally we add them. x cos(t ) A v sin( t ) A 2 x 2 cos (t ) A 2 v 2 sin (t ) A 2 2 x v 2 2 cos (t ) sin (t ) 1 A A 2 Final equation describes an ellipse 2 x v 1 A A The following figure shows a phase portrait of a simple harmonic oscillator with ω=10 s-1 and initial conditions x(0)=1 m; v(0)=0 m/s 2 2 x v 1 A A 2 2 x v 1 1 10 Oscillator with critical damping ω= 10s-1; δ= 10s-1 x(0)=1m; v(0)=0 m/s Overdamped oscillator ω= 10s-1; δ= 20s-1 x(0)=1m; v(0)=0 m/s Underdamped oscillator ω= 10s-1; δ= 1s-1 x(0)=1m; v(0)=0 m/s Simple harmonic oscillator initial amplitudes are 1,2, …,10 m Creating a phase portrait of an oscillator in Mathematica Stability and Fixed Points A fixed point is a special point of the dynamical system which does not change in time. It is also called an equilibrium, steady-state, or singular point of the system. If a system is defined by an equation dx/dt = f(x), then the fixed point x~ can be found by examining of condition f(x~)=0. We need not know analytic solution of x(t). For discrete time systems we examine condition x~ = f(x~) A stable fixed point: for all starting values x0 near the x~ the system converges to the x~ as t→∞. A marginally stable (neutrally stable) fixed point: for all starting values x0 near x~, the system stays near x~ but does not converge to x~ . An unstable fixed point: for starting values x0 very near x~ the system moves far away from x~ An attractor is a set towards which a dynamical system evolves over time. It can be a point, a curve or more complicated structure A perturbation is a small change in a physical system, most often in a physical system at equilibrium that is disturbed from the outside. Phase portraits of basic three types of fixed points STABLE MARGINALLY STABLE UNSTABLE Example 1– bacteria in a jar A jar is filled with a nutritive solution and some bacteria. Let b (for birth) be relative rate at which the bacteria reproduce and p (for perish) be relative rate at which they die. Then the population is growing at the rate r = b−p. If there are x bacteria in the jar, then the rate at which the number of bacteria is increasing is (b − p)x, that is, dx/dt = rx. Solution of this equation fox x(0)=x0 is x(t ) x0 e rt This model is not realistic, because bacteria population goes to the ∞ for r>0. Actually, as the number of bacteria rises, they crowd each other, produce more toxic waste products etc. Instead of constant relative perish rate p we will assume relative perish rate dependent on their number px. Now the number of bacteria increases by bx and their number decreases by px2. New differential equation will be dx 2 bx px dt Differential equation, initial number of bacteria x(0)=x0 dx bx px 2 dt Analytic solution provided by Mathematica To be able to find a fixed point, we have to set the right-hand side of the differential equation to zero. There are two possible solutions, i.e. we have two fixed points: b~ x p~ x2 0 ~ x (b p~ x) 0 ~ x1 0 b ~ x2 p First fixed point x~1= 0; There are no bacteria, so none can be born, none can die, but after small contamination of the jar (perturbation), but smaller than b/p, we can see, that the number of bacteria will increase by dx/dt = bx-px2>0 and will never return to the zero state. Conclusion: this fixed point is unstable. Second point x~2= b/p; At this population level, bacteria are being born at a rate bx~=b(b/p) = b2/p and are dying at a rate px~2 = p(b/p)2 = b2/p, so birth and death rates are exactly in balance. If the number of bacteria would be slightly increased, then dx/dt = bx-px2<0 and would return to equilibrium. If the number of bacteria would be slightly decreased, then dx/dt = bx-px2>0 and would return to equilibrium. Small perturbations away from x~ = b/p will self-correct back to b/p. Conclusion: this fixed point is stable and is also an attractor of this system. Graphical solution from Mathematica Input parameters: b=0.2, p=0.5 Initial conditions: x0= 0.9 for blue curves x0= 0.01 for red curves Phase portrait Number of bacteria in time dx dt 0.10 x 1.0 0.8 0.05 b/p 0.6 0.4 0.00 0.2 0.0 0 10 20 30 40 t 0.05 0.10 0.2 0.4 0.6 0.8 1.0 x Example 2 – predator and prey Now we have a biological system containing two species – predators (wolves) and prey (rabbits). Population of rabbits in time is r(t) and population of wolves is w(t). Rabbits, left on their own, will reproduce with velocity dr/dt= ar, where a>0 Wolves, without rabbits, will starve and their population will decline with velocity dw/dt= -bw, where b>0 When brought into the same environment, wolves will catch and eat rabbits. Loss to the rabbit population will be proportional to number of wolves w and number of rabbits r by a constant g (aggressivity of predators). Gain to the wolf population will be also proportional to r and w, this time by a constant h (effectivity of transformation of prey meat into the predator biomass). Here are differential equations describing the closed system dr ar grw dt dw bw hrw dt Here is time dependence and phase diagram for both populations a=0.3; b=0.1; g=0.002; h=0.001, initial number of rabbits r0=100, wolves w0=25 Number of populations over time Phase portrait An attractor of this system drawn on the phase diagram is a limit cycle. With higher rabbit natality and higher wolf mortality together with higher wolf aggressivity the changes are quicker a=0.75; b=0.2; g=0.03; h=0.01 Number of populations over time Phase portrait With extremely low rabbit natality and low wolf mortality togehter with high wolf aggressivity both populations will vanish a=0.01; b=0.05; g=0.05; h=0.05 Number of populations over time Phase portrait Calculation of the predator-prey system in the Mathematica The phase portrait of the predator-prey system in the Mathematica