What is the relationship between matrix dimensions in state space control?

In summary, the conversation discusses the dimensions of the state equation x_dot=Ax+Bu and how they relate to the number of states and inputs. It is noted that A will always be a square matrix and that if it is not full rank, there may be sections of the matrix with multiple or infinite possibilities. It is also mentioned that dim(A)+dim(B)=dim(x) and that B and A will have different dimensions.
  • #1
kidsasd987
143
4
Hello, I want to verify this question.

In short,
"Where did input matrix Bu arise from?"

I was wondering why the state equation has to be in the form of x_dot=Ax+Bu. I got to the point that the highest order terms can be expressed in the form of linear superposition of lower degree terms.

If that is the case, we can find dim(x)=n. (state vector in R^n)
Also because d/dt is a linear operator, dim(x_dot)=dim(x) (because we need n terms to uniquely determine x_dot).

And this gives a conclusion that dim(A)+dim(B)=dim(x). which means, Bu is compensating dimension for the 2nd order differential terms (since x_dot's 2nd order terms are linear superposition of lower differential terms)

Professor told me that, x_dot doesn't need to be in the same dimension in the case that if A has a nullity greater than 1 (A doesn't have full rank)

but considering the canonical solution, wouldn't they have to be in the same dimension?
 

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  • #2
https://en.wikibooks.org/wiki/Control_Systems/State-Space_Equations

go down the section heading
Matrix Dimensions

Per my understanding A will
always be a square matrix. This is due to the fact that every state in the X vector must be multiplies by every other state. Now if A is not full rank, there will be a section of the matrix that will have multiple or infinite possibilities. Now this might be something you don't care about for certain states, or something that doesn't matter. I suppose when representing your state equations you can show only part of the Xdot values, as many of the others simply do not matter. However in that case they would still 'exist.' they simply would not be written down on paper.

kidsasd987 said:
dim(A)+dim(B)=dim(x)
follow the link. B and A will have different dimensions. X and Xdot are and will always be vectors of the states and their derivatives. A and B are simply sized based on the number of states and the number of inputs. They are the mathematical representations of how the states relate to one another, and how they are affected by the inputs.
 

1. What is the difference between classical control and modern control using state space?

Classical control deals with the analysis and design of control systems using transfer functions and frequency domain techniques, while modern control uses state space representation and time domain methods. State space methods allow for the analysis of systems with multiple inputs and outputs, and can handle nonlinear and time-varying systems more easily than classical control methods.

2. How is a system represented in state space?

A system in state space is represented by a set of differential equations that describe the relationship between the system's inputs, outputs, and internal state variables. The state variables represent the current state of the system and are used to predict future behavior.

3. What are the advantages of using state space representation?

State space representation allows for a more comprehensive analysis of a system, as it can handle complex systems with multiple inputs and outputs, nonlinearities, and time-varying behavior. It also allows for the use of modern control techniques such as optimal control and robust control.

4. How is control achieved in state space?

In state space control, the system's state variables are measured and used to calculate the control input needed to achieve a desired output. This is done using control algorithms such as state feedback and observer-based control. The control input is then applied to the system to achieve the desired behavior.

5. What are some applications of state space control?

State space control is widely used in the aerospace and automotive industries for the control of complex systems such as aircraft and vehicles. It is also used in industrial processes, robotics, and power systems. State space control is also commonly used in the design of modern control systems for various applications.

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