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The state space or phase space is the geometric space in which the axes are the state variables. The system state can be represented as a vector , the state vector . If the dynamical system is linear, time-invariant, and finite-dimensional, then the differential and algebraic equations may be written in matrix form.
If the size of the state space is finite, calculating the size of the state space is a combinatorial problem. [4] For example, in the Eight queens puzzle, the state space can be calculated by counting all possible ways to place 8 pieces on an 8x8 chessboard. This is the same as choosing 8 positions without replacement from a set of 64, or
A model class that is general enough to capture this situation is the class of stochastic nonlinear state-space models. A state-space model is usually obtained using first principle laws, [ 16 ] such as mechanical, electrical, or thermodynamic physical laws, and the parameters to be identified usually have some physical meaning or significance.
In mathematics, specifically in control theory, subspace identification (SID) aims at identifying linear time invariant (LTI) state space models from input-output data. SID does not require that the user parametrizes the system matrices before solving a parametric optimization problem and, as a consequence, SID methods do not suffer from problems related to local minima that often lead to ...
The state-transition matrix is used to find the solution to a general state-space representation of a linear system in the following form ˙ = () + (), =, where () are the states of the system, () is the input signal, () and () are matrix functions, and is the initial condition at .
This state-space realization is called controllable canonical form (also known as phase variable canonical form) because the resulting model is guaranteed to be controllable (i.e., because the control enters a chain of integrators, it has the ability to move every state).
Discretization of linear state space models [ edit ] Discretization is also concerned with the transformation of continuous differential equations into discrete difference equations , suitable for numerical computing .
The state space of the dynamical system is a ν-dimensional manifold M. The dynamics is given by a smooth map:. Assume that the dynamics f has a strange attractor with box counting dimension d A. Using ideas from Whitney's embedding theorem, A can be embedded in k-dimensional Euclidean space with