1.2 Differential equations

Differential equations are the backbone of control theory, describing how systems change over time. They model everything from simple pendulums to complex spacecraft, allowing engineers to predict and manipulate system behavior.

In control theory, we use differential equations to design controllers that stabilize systems and achieve desired performance. Understanding these equations is crucial for analyzing system dynamics and creating effective control strategies.

Definition of differential equations

• Differential equations are mathematical equations that involve derivatives or rates of change of one or more variables with respect to another variable, typically time or space
• They describe the relationship between a function and its derivatives, allowing us to model and analyze dynamic systems in various fields, including control theory
• Differential equations can be used to represent physical laws, such as Newton's laws of motion, and to study the behavior of systems over time or space

Classification of differential equations

Ordinary vs partial differential equations

Top images from around the web for Ordinary vs partial differential equations

• 

  pde - what is separation of variables - Mathematics Stack Exchange View original

  Is this image relevant?

• 

  control theory - Derivation for state equation linearization - Mathematics Stack Exchange View original

  Is this image relevant?

• 

  Maxwell equations (thermodynamics) - Knowino View original

  Is this image relevant?

• 

  pde - what is separation of variables - Mathematics Stack Exchange View original

  Is this image relevant?

• 

  control theory - Derivation for state equation linearization - Mathematics Stack Exchange View original

  Is this image relevant?

1 of 3

Top images from around the web for Ordinary vs partial differential equations

• 

  pde - what is separation of variables - Mathematics Stack Exchange View original

  Is this image relevant?

• 

  control theory - Derivation for state equation linearization - Mathematics Stack Exchange View original

  Is this image relevant?

• 

  Maxwell equations (thermodynamics) - Knowino View original

  Is this image relevant?

• 

  pde - what is separation of variables - Mathematics Stack Exchange View original

  Is this image relevant?

• 

  control theory - Derivation for state equation linearization - Mathematics Stack Exchange View original

  Is this image relevant?

1 of 3

• Ordinary differential equations (ODEs) involve derivatives with respect to a single independent variable, usually time
  • Example: $\frac{dy}{dt} = f(t, y)$, where $y$ is a function of $t$
• Partial differential equations (PDEs) involve derivatives with respect to multiple independent variables, such as time and space
  • Example: $\frac{\partial u}{\partial t} = c^2 \frac{\partial^2 u}{\partial x^2}$, where $u$ is a function of $t$ and $x$

Linear vs nonlinear differential equations

• Linear differential equations have the dependent variable and its derivatives appearing linearly, with coefficients that can be functions of the independent variable
  • Example: $\frac{dy}{dt} + p(t)y = q(t)$
• Nonlinear differential equations have the dependent variable or its derivatives appearing in a nonlinear manner, such as squared or multiplied with each other
  • Example: $\frac{dy}{dt} = y^2 + \sin(t)$

Homogeneous vs non-homogeneous equations

• Homogeneous differential equations have all terms containing the dependent variable and its derivatives, with no standalone terms
  • Example: $\frac{d^2y}{dt^2} + 4\frac{dy}{dt} + 4y = 0$
• Non-homogeneous differential equations have at least one term that does not contain the dependent variable or its derivatives
  • Example: $\frac{d^2y}{dt^2} + 4\frac{dy}{dt} + 4y = \cos(t)$

Order of differential equations

• The order of a differential equation is the highest derivative that appears in the equation
  • First-order equations contain only first derivatives
  • Second-order equations contain second derivatives, and so on
• The order of the equation determines the number of initial or boundary conditions needed to solve the equation uniquely

Solution methods for first-order equations

Separation of variables

• Separation of variables is a method for solving first-order ODEs in which the variables can be separated on opposite sides of the equation
  • The equation is rearranged to have all terms involving $y$ on one side and all terms involving $t$ on the other side
  • Both sides are then integrated to find the solution
• Example: $\frac{dy}{dt} = ty^2$ can be solved by separating variables and integrating: $\int \frac{1}{y^2}dy = \int tdt$

Integrating factors

• Integrating factors are used to solve first-order linear ODEs by multiplying both sides of the equation by a carefully chosen function
  • The function, called the integrating factor, is chosen to make the left-hand side of the equation a perfect derivative
  • The equation can then be integrated to find the solution
• Example: To solve $\frac{dy}{dt} + P(t)y = Q(t)$, multiply both sides by the integrating factor $e^{\int P(t)dt}$

Exact equations

• An exact first-order ODE is one that can be written in the form $M(x, y)dx + N(x, y)dy = 0$, where $\frac{\partial M}{\partial y} = \frac{\partial N}{\partial x}$
  • The solution to an exact equation is a function $F(x, y) = C$, where $C$ is an arbitrary constant
  • The function $F(x, y)$ can be found by integrating $M(x, y)$ with respect to $x$ or $N(x, y)$ with respect to $y$
• Example: $2xy^3dx + (3x^2y^2 - 1)dy = 0$ is an exact equation with solution $F(x, y) = x^2y^3 - y = C$

Bernoulli equations

• A Bernoulli equation is a first-order nonlinear ODE of the form $\frac{dy}{dt} + P(t)y = Q(t)y^n$, where $n \neq 0, 1$
  • Bernoulli equations can be transformed into linear equations by substituting $v = y^{1-n}$
  • The resulting linear equation can be solved using integrating factors or other methods
• Example: $\frac{dy}{dt} + 2ty = t^2y^3$ can be transformed into a linear equation by substituting $v = y^{-2}$

Solution methods for higher-order equations

Reduction of order

• Reduction of order is a method for solving second-order linear ODEs when one solution, $y_1(t)$, is already known
  • The method seeks a second solution of the form $y_2(t) = v(t)y_1(t)$, where $v(t)$ is a function to be determined
  • Substituting $y_2(t)$ into the original equation leads to a first-order linear ODE for $v(t)$, which can be solved using integrating factors
• Example: If $y_1(t) = t$ is a solution to $t^2\frac{d^2y}{dt^2} + 2t\frac{dy}{dt} - 2y = 0$, then $y_2(t) = v(t)t$ can be found by solving for $v(t)$

Method of undetermined coefficients

• The method of undetermined coefficients is used to find particular solutions to non-homogeneous linear ODEs with specific types of forcing functions (right-hand side)
  • The particular solution is assumed to have a form similar to the forcing function, with unknown coefficients
  • The assumed solution is substituted into the ODE, and the coefficients are determined by equating like terms
• Example: For $\frac{d^2y}{dt^2} + 4y = 3\cos(2t)$, assume a particular solution of the form $y_p(t) = A\cos(2t) + B\sin(2t)$ and solve for $A$ and $B$

Variation of parameters

• Variation of parameters is a general method for finding particular solutions to non-homogeneous linear ODEs
  • The particular solution is assumed to be a linear combination of the fundamental solutions of the corresponding homogeneous equation, with coefficients that are functions of the independent variable
  • The assumed solution is substituted into the ODE, and the coefficients are determined by solving a system of linear equations
• Example: For $\frac{d^2y}{dt^2} + y = \sec(t)$, the particular solution is $y_p(t) = u_1(t)\cos(t) + u_2(t)\sin(t)$, where $u_1(t)$ and $u_2(t)$ are found using the method

Laplace transforms in differential equations

Definition of Laplace transform

• The Laplace transform is an integral transform that converts a function of time, $f(t)$, into a function of a complex variable, $F(s)$
  • The Laplace transform is defined as $\mathcal{L}\{f(t)\} = F(s) = \int_0^{\infty} e^{-st}f(t)dt$
  • The Laplace transform is a powerful tool for solving linear ODEs with initial conditions
• Example: The Laplace transform of $f(t) = e^{at}$ is $F(s) = \frac{1}{s-a}$

Properties of Laplace transform

• The Laplace transform has several important properties that make it useful for solving ODEs:
  • Linearity: $\mathcal{L}\{af(t) + bg(t)\} = a\mathcal{L}\{f(t)\} + b\mathcal{L}\{g(t)\}$
  • Differentiation: $\mathcal{L}\{f'(t)\} = s\mathcal{L}\{f(t)\} - f(0)$
  • Integration: $\mathcal{L}\{\int_0^t f(\tau)d\tau\} = \frac{1}{s}\mathcal{L}\{f(t)\}$
  • Shifting: $\mathcal{L}\{e^{at}f(t)\} = F(s-a)$
• These properties allow the Laplace transform to convert differential equations into algebraic equations in the complex variable $s$

Inverse Laplace transform

• The inverse Laplace transform converts a function of the complex variable $s$ back into a function of time, $t$
  • The inverse Laplace transform is denoted as $\mathcal{L}^{-1}\{F(s)\} = f(t)$
  • The inverse Laplace transform can be found using tables, partial fraction decomposition, or the Bromwich integral
• Example: The inverse Laplace transform of $F(s) = \frac{1}{s-a}$ is $f(t) = e^{at}$

Solving differential equations with Laplace transforms

• To solve a linear ODE with initial conditions using Laplace transforms:
  1. Take the Laplace transform of both sides of the equation, using the properties of the Laplace transform to handle derivatives and initial conditions
  2. Solve the resulting algebraic equation for the Laplace transform of the solution, $Y(s)$
  3. Find the inverse Laplace transform of $Y(s)$ to obtain the solution $y(t)$
• Example: To solve $y'' + 4y = 0$ with $y(0) = 1$ and $y'(0) = 0$, take the Laplace transform, solve for $Y(s)$, and find the inverse Laplace transform

Systems of differential equations

Coupled equations

• A system of differential equations consists of two or more ODEs that are coupled, meaning the equations involve multiple dependent variables and their derivatives
  • Coupled equations arise when modeling systems with multiple interacting components, such as predator-prey populations or electrical circuits
  • To solve a system of ODEs, the equations must be solved simultaneously, either analytically or numerically
• Example: The Lotka-Volterra equations, $\frac{dx}{dt} = ax - bxy$ and $\frac{dy}{dt} = cxy - dy$, model the interactions between predator and prey populations

Eigenvalues and eigenvectors

• Eigenvalues and eigenvectors are important concepts in the analysis of linear systems of ODEs
  • An eigenvalue $\lambda$ and its corresponding eigenvector $\vec{v}$ satisfy the equation $A\vec{v} = \lambda\vec{v}$, where $A$ is the coefficient matrix of the linear system
  • The eigenvalues determine the stability and behavior of the solutions to the system of ODEs
• Example: For the system $\frac{dx}{dt} = 2x + 3y$ and $\frac{dy}{dt} = x + 2y$, the eigenvalues and eigenvectors can be found by solving $\det(A - \lambda I) = 0$

Phase plane analysis

• Phase plane analysis is a graphical method for studying the qualitative behavior of solutions to systems of two first-order ODEs
  • The phase plane is a 2D plot with the dependent variables on the axes, showing the trajectories of solutions as curves in the plane
  • Equilibrium points, where the derivatives of both variables are zero, are classified as stable, unstable, or saddle points based on the eigenvalues of the linearized system
• Example: For the system $\frac{dx}{dt} = y$ and $\frac{dy}{dt} = -x$, the phase plane shows circular trajectories around the origin, which is a stable equilibrium point

Stability analysis of solutions

Equilibrium points

• Equilibrium points, also known as fixed points or steady-state solutions, are constant solutions to a system of ODEs where the derivatives of all variables are zero
  • To find equilibrium points, set the right-hand side of each equation in the system to zero and solve for the dependent variables
  • The stability of an equilibrium point determines whether nearby solutions converge to or diverge from the point over time
• Example: For the system $\frac{dx}{dt} = x(1-x)$, the equilibrium points are $x = 0$ and $x = 1$

Linearization of nonlinear systems

• Linearization is a technique for approximating a nonlinear system of ODEs by a linear system near an equilibrium point
  • The Jacobian matrix, containing the partial derivatives of the right-hand side of each equation with respect to each variable, is evaluated at the equilibrium point
  • The eigenvalues of the Jacobian matrix determine the local stability of the equilibrium point
• Example: To linearize the system $\frac{dx}{dt} = xy$ and $\frac{dy}{dt} = -y + x^2$ at the origin, find the Jacobian matrix and evaluate it at $(0, 0)$

Lyapunov stability theory

• Lyapunov stability theory provides a general framework for analyzing the stability of equilibrium points in nonlinear systems
  • A Lyapunov function, $V(x)$, is a scalar function that is positive definite and has a negative semidefinite time derivative along the system trajectories
  • If a Lyapunov function exists for an equilibrium point, the point is stable; if the time derivative is negative definite, the point is asymptotically stable
• Example: For the system $\frac{dx}{dt} = -x^3$, the Lyapunov function $V(x) = \frac{1}{2}x^2$ proves the origin is asymptotically stable

Numerical methods for differential equations

Euler's method

• Euler's method is a simple numerical method for solving first-order ODEs by approximating the solution using a finite difference approximation of the derivative
  • The method starts from an initial condition and iteratively updates the solution using the equation $y_{n+1} = y_n + hf(t_n, y_n)$, where $h$ is the step size
  • Euler's method is first-order accurate, meaning the error is proportional to the step size $h$
• Example: To solve $\frac{dy}{dt} = t^2 + y^2$ with $y(0) = 1$ using Euler's method, iterate $y_{n+1} = y_n + h(t_n^2 + y_n^2)$ with a chosen step size

Runge-Kutta methods

• Runge-Kutta methods are a family of numerical methods for solving first-order ODEs that achieve higher accuracy than Euler's method by using multiple function evaluations per step
  • The most common Runge-Kutta method is the fourth-order RK4 method, which uses four function evaluations to update the solution: $y_{n+1} = y_n + \frac{h}{6}(k_1 + 2k_2 + 2k_3 + k_4)$
  • Runge-Kutta methods have higher-order accuracy, with the error proportional to higher powers of the step size $h$
• Example: To solve $\frac{dy}{dt} = t^2 + y^2$ with $y(0) = 1$ using RK4, calculate $k_1$, $k_2$, $k_3$, and $k_4$ at each step and update the solution accordingly

Finite difference methods

• Finite difference methods are numerical methods for solving PDEs by approximating the derivatives using finite differences on a discretized grid
  • The domain is divided into a grid of points, and the PDE is replaced by a system of algebraic equations involving the values of the solution at the grid points
  • Finite difference methods can be explicit, updating the solution at each time step based on the previous time step, or implicit, solving a system of equations at each time step
• Example: To solve the heat equation $\frac{\partial u}{\partial t} = \alpha\frac{\partial^2 u}{\partial x^2}$ using the explicit finite difference method, approximate the derivatives using central differences in space and forward differences in time

Applications of differential equations in control theory

Modeling of dynamic systems

• Differential equations are used to model the behavior of dynamic systems in control theory, such as mechanical, electrical, and thermal systems
  • The equations describe the relationship between the system's inputs, outputs, and state variables, which can