Chapter 7: Problem 17
Consider the initial value problem $$ x^{\prime}=A x+g(t), \quad x(0)=x^{0} $$ (a) By referring to Problem \(15(c)\) in Section \(7.7,\) show that $$ x=\Phi(t) x^{0}+\int_{0}^{t} \Phi(t-s) g(s) d s $$ (b) Show also that $$ x=\exp (A t) x^{0}+\int_{0}^{t} \exp [\mathbf{A}(t-s)] \mathbf{g}(s) d s $$ Compare these results with those of Problem 27 in Section \(3.7 .\)
Short Answer
Step by step solution
Part (a): Prove the solution formula in terms of the state transition matrix
Part (b): Prove the solution formula in terms of the matrix exponential
Comparison with Problem 27 in Section 3.7
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Key Concepts
These are the key concepts you need to understand to accurately answer the question.
State Transition Matrix
For a system given by \( x'(t) = Ax(t) + g(t) \), where \( A \) is a constant matrix and \( g(t) \) is a known vector function, the solution using the state transition matrix is:
- \( x(t) = \Phi(t)x^0 + \int_{0}^{t} \Phi(t-s)g(s)\,ds \)
The matrix \( \Phi(t) \) essentially encapsulates how the homogeneous part of the system evolves over time, which in turn helps to solve the nonhomogeneous problem.
Matrix Exponential
- \( e^{At} = I + At + \frac{(At)^2}{2!} + \frac{(At)^3}{3!} + \cdots \)
In the context of a nonhomogeneous system, the solution can be expressed using the matrix exponential as:
- \( x(t) = e^{At} x^0 + \int_{0}^{t} e^{A(t-s)}g(s)\,ds \)
Nonhomogeneous Linear System
The general format of the solution to a nonhomogeneous linear system involves handling both:
- The homogeneous part, dealt with using either the state transition matrix \( \Phi(t) \) or the matrix exponential \( e^{At} \).
- The nonhomogeneous part, integrated over time with the aid of \( g(t) \).
- \( x(t) = \Phi(t)x^0 + \int_{0}^{t} \Phi(t-s)g(s)\,ds \)
- Or alternatively, \( x(t) = e^{At} x^0 + \int_{0}^{t} e^{A(t-s)}g(s)\,ds \)
Initial Value Problem
- Equation: \( x'(t) = Ax(t) + g(t) \)
- Initial condition: \( x(0) = x^0 \)
The solution to an IVP often involves the techniques discussed, such as the state transition matrix \( \Phi(t) \) or the matrix exponential \( e^{At} \), particularly in the case of linear systems. These methods together with the initial condition facilitate a clear path to finding \( x(t) \) efficiently, illustrating how the initial state evolves over time.