Dynamic Behavior

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Chapter 5 - Dynamic Behavior

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In this chapter we give a broad discussion of the behavior of dynamical systems, focused on systems modeled by nonlinear differential equations. This allows us to discuss equilibrium points, stability, limit cycles and other key concepts of dynamical systems. We also introduce some methods for analyzing global behavior of solutions.

Contents

1. Solving Differential Equations
2. Qualitative Analysis
3. Stability
4. Lyapunov Stability Analysis
5. Parametric and Nonlocal Behavior
6. Further Reading

Exercises

Chapter Summary

1. We say that $x(t)$ is a solution of a differential equation on the time interval $t_{0}$ to $t_{\text{f}}$ with initial value $x_{0}$ if it satisfies

   $x(t_{0})=x_{0}\quad {\text{and}}\quad {\dot {x}}(t)=F(x(t))\quad {\text{for all}}\quad t_{0}\leq t\leq t_{\text{f}}.$ We will usually assume $t_{0}=0$. For most differential equations we will encounter, there is a unique solution for a given initial condition. Numerical tools such as MATLAB and Mathematica can be used to obtain numerical solutions for $x(t)$ given the function $F(x)$.

2. An equilibrium point for a dynamical system represents a point $x_{e}$ such that if $x(0)=x_{\text{e}}$ then $x(t)=x_{\text{e}}$ for all $t$. Equilibrium points represent stationary conditions for the dynamics of a system. A limit cycle for a dynamical system is a solution $x(t)$ which is periodic with some period $T$, so that $x(t+T)=x(t)$ for all $t$.

3. An equilibrium point is (locally) stable if initial conditions that start near an equilibrium point stay near that equilibrium point. A equilibrium point is (locally) asymptotically stable if it is stable and, in addition, the state of the system converges to the equilibrium point as time increases. An equilibrium point is unstable if it is not stable. Similar definitions can be used to define the stability of a limit cycle.

4. Phase portraits provide a convenient way to understand the behavior of 2-dimensional dynamical systems. A phase portrait is a graphical representation of the dynamics obtained by plotting the state $x(t)=(x_{1}(t),x_{2}(t))$ in the plane. This portrait is often augmented by plotting an arrow in the plane corresponding to $F(x)$, which shows the rate of change of the state. The following diagrams illustrate the basic features of a dynamical systems:

   An asymptotically stable equilibrium point at $x=(0,0)$.	A limit cycle of radius one, with an unstable equilibrium point at $x=(0,0)$.	A stable equlibirum point at $x=(0,0)$ (nearby initial conditions stay nearby).

5. A linear system

   ${\dfrac {dx}{dt}}=Ax$

   is asymptotically stable if and only if all eigenvalues of $A$ all have strictly negative real part and is unstable if any eigenvalue of $A$ has strictly positive real part. A nonlinear system can be approximated by a linear system around an equilibrium point by using the relationship

   ${\dot {x}}=F(x_{\text{e}})+\left.{\frac {\partial F}{\partial x}}\right|_{x_{\text{e}}}(x-x_{\text{e}})+{\text{higher order terms in}}\,(x-x_{\text{e}}).$

   Since $F(x_{e})=0$, we can approximate the system by choosing a new state variable $z=x-x_{\text{e}}$ and writing the dynamics as ${\dot {z}}=Az$. The stability of the nonlinear system can be determined in a local neighborhood of the equilibrium point through its linearization.

6. A Lyapunov function is an energy-like function $V:\mathbb {R} ^{n}\to \mathbb {R}$ that can be used to reason about the stability of an equilibrium point. We define the derivative of $V$ along the trajectory of the system as

   ${\dot {V}}(x)={\dfrac {\partial V}{\partial x}}{\dot {x}}={\dfrac {\partial V}{\partial x}}F(x)$

   Assuming $x_{\text{e}}=0$ and $V(0)=0$, the following conditions hold:

   Condition on $V$	Condition on ${\dot {V}}$	Stability
   $V(x)>0,x\neq 0$	${\dot {V}}(x)\leq 0$ for all $x$	$x_{e}$ stable
   $V(x)>0,x\neq 0$	${\dot {V}}(x)<0,x\neq 0$	$x_{e}$ asymptotically stable

   Stability of limit cycles can also be studied using Lyapunov functions.

7. The Krasovskii-LaSalle Principle allows one to reason about asymptotic stability even if the time derivative of $V$ is only negative semi-definite ($\leq 0$ rather than $<0$). Let $V:\mathbb {R} ^{n}\to \mathbb {R}$ be a positive definite function, $V(x)>0$ for all $x\neq 0$ and $V(0)=0$, such that

   ${\dot {V}}(x)\leq 0$ on the compact set $\Omega _{c}=\{x\in \mathbb {R} ^{n}:V(x)\leq c\}$.

   Then as $t\to \infty$, the trajectory of the system will converge to the largest invariant set inside

   $S=\{x\in \Omega _{c}:{\dot {V}}(x)=0\}$.

   In particular, if $S$ contains no invariant sets other than $x=0$, then 0 is asymptotically stable.

8. The global behavior of a nonlinear system refers to dynamics of the system far away from equilibrium points. The region of attraction of an asymptotically stable equilirium point refers to the set of all initial conditions that converge to that equilibrium point. An equilibrium point is said to be globally asymptotically stable if all initial conditions converge to that equilibrium point. Global stability can be checked by finding a Lyapunov function that is globally positive definition with time derivative globally negative definite.

Teaching Materials None available Additional Exercises None available Frequently Asked Questions None available Errata Errata: sign errors in Example 5.18 (noise cancellation)

Python Code The following Python scripts are available for producing figures that appear in this chapter. Figure 5.1: Response of a damped oscillator Figure 5.3: Phase portraits Figure 5.4: Equilibrium points for an inverted pendulum Figure 5.5: Phase portrait and time domain simulation for a system with a limit cycle Figure 5.6: Illustration of Lyapunov’s concept of a stable solution Figure 5.7: Phase portrait and time domain simulation for a system with a single stable equilibrium point Figure 5.8: Phase portrait and time domain simulation for a system with a single asymptotically stable equilibrium point Figure 5.9: Phase portrait and time domain simulation for a system with a single unstable equilibrium point Figure 5.10: Phase portraits for a congestion control protocol running with N = 60 identical source computers Figure 5.11: Comparison between phase portraits for a nonlinear system and its linearization Figure 5.12: Stability analysis for a tanker Figure 5.13: Solution curves for a stable limit cycle Figure 5.15: Stability of a genetic switch Figure 5.16: Dynamics of a genetic switch Figure 5.17: Stabilized inverted pendulum Figure 5.18: Bifurcation analysis of the predator–prey system Figure 5.19: Stability plots for a bicycle moving at constant velocity Figure 5.21: Simulation of noise cancellation See the software page for more information on how to run these scripts. Additional Information First Edition Supplemental Information