Genral state space model - runge kutte

In summary, the conversation is about solving a general n order state space model using numerical methods such as Runge-Kutta and MATLAB. The Butcher tableau is not necessary for this problem. The conversation also mentions using functional representations and first-order ODE techniques. The original poster is seeking help with their specific problem, but it is not clear what they have already tried.
  • #1
ash4sigh
5
0
Hi,

Really glad to have found this site... I was hoping someone could help me - have been trying to decipher how to find a way to solve general n order state space model - I started off with RK4 and now I want to expand that but I can't get my head wrapped around the butcher tableau to generate code for n order state space model..

any help would be appreciated..

sorry *general
 
Last edited:
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  • #2
hell i don't even know if i need butcher tableau - lost!
 
  • #3
You've lost me too! I thought I knew differential equations but I don't recognize "n order state space" nor "Butcher tableau"!
 
  • #4
sniff
 
  • #5
What he means is just a matrix ODE given as in a compact fashion
[tex]
\dot{x} = Ax+Bu, y = Cx + Du
[/tex]
where A,B,C,D are matrices. This is usually referred as a State-Space model of a system.


You can just use MATLAB/simulink for your system. For that you don't need anything else, it does it for you. Or you can just pick up a sample time and update your states at each sample time e.g. Newton's method. If you insist on RK4 it is similar to Runge Kutta 4 for scalar ODEs. just make sure that what you are doing is the same with why you are doing.

Of course if you have the input function known in time domain, you can solve the convolution integral

[tex]
y=Ce^{At}x_0 + C\int^t_0{e^{A(t-\tau)}Bu(\tau)d\tau}
[/tex]

or the Laplace equivalent of this (in terms of a transfer function), (if you know your input function's Laplace domain representation!)
[tex]
y(s) = (C(sI-A)^{-1}B + D)u(s)
[/tex]

Butcher Tableau is not useful here. Just forget about it.
 
  • #6
ash4sigh said:
[ I have ] been trying to decipher how to find a way to solve general n order state space model - I started off with RK4 ...
What do you mean by "general n order state space model"? I assume you mean that you have a functional representation for the nth derivative of the state with respect to time,

[tex]\frac {d^n}{dt^n} x(t) = x^{(n)}(t) = g(t,x(t),x^{(1)}(t),\cdots,x^{(n-1)}(t))[/tex]

Such systems can be converted to a first-order ODE by making the derivatives up to order n-1 a part of the state. You then have a plethora of first-order ODE techniques from which to choose, Runge-Kutta methods being just one class.

RK4 is the gold standard, not so much because it is particularly good but more because it is often adequate and particular easy to implement. Lower order methods such as Heun's method (aka velocity verlet) can yield good results at a lower cost. What problems are you confronting, and what have you tried?

You probably don't need to use the Butcher tableau. Most of the work on Runge-Kutta methods was done a long time ago.
 
  • #7
help on state space HW

Edited by HallsofIvy
I have removed this because

1) It introduced a totally new problem, not that of the original poster.

2) It has also been posted under "homework".
 
Last edited by a moderator:

1. What is a general state space model?

A general state space model is a mathematical representation of a dynamic system in which the state of the system at any given time is described by a set of variables or parameters. These variables can change over time, and the model allows for the prediction of future states based on past and current states.

2. What is the Runge-Kutta method?

The Runge-Kutta method is a numerical algorithm used to solve ordinary differential equations, which are equations that describe the relationship between a function and its derivatives. It is particularly useful for solving complex systems that cannot be solved analytically.

3. How is the Runge-Kutta method used in state space models?

In state space models, the Runge-Kutta method is used to numerically solve the differential equations that describe the relationship between the system's state variables. It is used to update the state variables at each time step, allowing for the prediction of future states.

4. What are the advantages of using a general state space model with Runge-Kutta?

Using a general state space model with the Runge-Kutta method allows for the modeling of complex systems with non-linear relationships between variables. It also allows for the inclusion of external factors or inputs that may affect the system's behavior. Additionally, the numerical solution provided by the Runge-Kutta method is more accurate than analytical solutions in many cases.

5. What are some practical applications of general state space models with Runge-Kutta?

General state space models with Runge-Kutta are commonly used in various fields such as physics, engineering, economics, and biology. They are used to model and predict the behavior of systems such as weather patterns, population dynamics, and financial markets. They are also used in control systems to optimize performance and make predictions for future states.

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