pypose.module.LTI

class pypose.module.LTI(A, B, C, D, c1=None, c2=None)

Discrete-time Linear Time-Invariant (LTI) system.

Parameters

• A (Tensor) – The state matrix of LTI system.

• B (Tensor) – The input matrix of LTI system.

• C (Tensor) – The output matrix of LTI system.

• D (Tensor) – The observation matrix of LTI system,

• c1 (Tensor) – The constant input of LTI system,

• c2 (Tensor) – The constant output of LTI system.

A linear time-invariant lumped system can be described by state-space equation of the form:

\[\begin{align*} \mathbf{z} = \mathbf{A}\mathbf{x} + \mathbf{B}\mathbf{u} + \mathbf{c}_1 \\ \mathbf{y} = \mathbf{C}\mathbf{x} + \mathbf{D}\mathbf{u} + \mathbf{c}_2 \\ \end{align*} \]

where \(\mathbf{x}\) and \(\mathbf{u}\) are state and input of the current timestamp of LTI system.

Note

The variables including state and input are row vectors, which is the last dimension of a Tensor. A, B, C, D, x, u could be a single matrix or batched matrices. In the batch case, their dimensions must be consistent so that they can be multiplied for each channel.

Example

>>> # Batch, State, Input, Observe Dimension
>>> Bd, Sd, Id, Od = 2, 3, 2, 2
>>> # Linear System Matrices
>>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
>>> A = torch.randn(Bd, Sd, Sd)
>>> B = torch.randn(Bd, Sd, Id)
>>> C = torch.randn(Bd, Od, Sd)
>>> D = torch.randn(Bd, Od, Id)
>>> c1 = torch.randn(Bd, Sd)
>>> c2 = torch.randn(Bd, Od)
...
>>> lti = pp.module.LTI(A, B, C, D, c1, c2).to(device)
...
>>> state = torch.randn(Bd, Sd, device=device)
>>> input = torch.randn(Bd, Id, device=device)
>>> lti(state, input)
tensor([[[-8.5639,  0.0523, -0.2576]],
        [[ 4.1013, -1.5452, -0.0233]]]),
tensor([[[-3.5780, -2.2970, -2.9314]],
        [[-0.4358,  1.7306,  2.7514]]]))

Note

In this general example, all variables are in a batch. User definable as appropriate.

Note

More practical examples can be found at examples/module/dynamics.

property A

System state matrix A

property B

System input matrix B

property C

System output matrix C

property D

System observation matrix D

property c1

Constant input c1

property c2

Constant output c2

forward(state, input)

Perform one step advance for the LTI system.

observation(state, input, t=None)

Return the observation of LTI system at current time step.

\[\mathbf{y} = \mathbf{C}\mathbf{x} + \mathbf{D}\mathbf{u} + \mathbf{c}_2 \]

state_transition(state, input, t=None)

Perform one step of LTI state transition.

\[\mathbf{z} = \mathbf{A}\mathbf{x} + \mathbf{B}\mathbf{u} + \mathbf{c}_1 \]