Search results
Results from the WOW.Com Content Network
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.
The behavioral approach to systems theory and control theory was initiated in the late-1970s by J. C. Willems as a result of resolving inconsistencies present in classical approaches based on state-space, transfer function, and convolution representations.
In discrete-time the transfer function is given in terms of the state-space parameters by + = and it is holomorphic in a disc centered at the origin. [4] In case 1/ z belongs to the resolvent set of A (which is the case on a possibly smaller disc centered at the origin) the transfer function equals D + C z ( I − z A ) − 1 B {\displaystyle D ...
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
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
Any given transfer function which is strictly proper can easily be transferred into state-space by the following approach (this example is for a 4-dimensional, single-input, single-output system)): Given a transfer function, expand it to reveal all coefficients in both the numerator and denominator.
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 ...
Hankel matrices are formed when, given a sequence of output data, a realization of an underlying state-space or hidden Markov model is desired. [3] The singular value decomposition of the Hankel matrix provides a means of computing the A , B , and C matrices which define the state-space realization. [ 4 ]