Linearization of non linear systems

In summary, in a dynamics modelling class, the professor discussed linearizing non-linear state equations to approximate behavior. The operating point can be solved for by setting x' = 0, even when an operating point is not given. This may result in finding the maximum and minimum values of x, such as displacement. However, further research may be needed to fully understand this concept. The equation x' = f(x,u) can also be applied in this scenario.
  • #1
theBEAST
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Homework Statement


In my dynamics modelling class, the professor went over an example where we linearize non linear state equations to approximate the behavior. In this case, we are not given an operating point. However, the professor said you can solve for the operating point by setting x' = 0. See the picture below for the notes.

I don't really understand why we can do this, if we set x' = 0, doesn't this just solve for the max and min values of x. For example if x = displacement, then we get the max and min displacements at a certain point. I tried to do some research but I am still confused.

Homework Equations


x' = f(x,u)

The Attempt at a Solution


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  • #2
A dynamic system can have equilibrium points, where the velocities (##\dot x_i##-s ) are zero, and investigate the motion of the system around these points: If moving out the system slightly from equilibrium, it returns back or goes away, and there are some other possibilities.

ehild
 

1. What is linearization of non linear systems?

Linearization of non linear systems is the process of approximating a non linear system with a linear system in order to simplify its analysis and solution. This involves finding a linear function that closely matches the behavior of the non linear system around a particular point, known as the operating point.

2. Why is linearization important in science?

Linearization is important in science because many real-world systems, such as mechanical, electrical, and biological systems, exhibit non linear behavior. By linearizing these systems, we can use well-established techniques from linear systems theory to analyze and solve complex problems.

3. What types of systems can be linearized?

Any system that can be described by a set of mathematical equations can be linearized. This includes both continuous and discrete systems, as well as time-invariant and time-varying systems.

4. What are the benefits of linearization?

The main benefit of linearization is that it allows us to simplify the analysis and solution of non linear systems. This can lead to a better understanding of the system's behavior and can also help us design more efficient and effective control strategies for the system.

5. What are the limitations of linearization?

Linearization is only an approximation of the non linear system, so it is important to choose an appropriate operating point to minimize errors. Additionally, linearization may not accurately capture the behavior of the system in extreme or nonlinear operating conditions.

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