pySimBlocks.blocks.systems.polytopic_state_space

class pySimBlocks.blocks.systems.polytopic_state_space.PolytopicStateSpace(name: str, A: ArrayLike, B: ArrayLike, C: ArrayLike, x0: TypeAliasForwardRef('ArrayLike') | None = None, sample_time: float | None = None)

Bases: Block

Discrete-time polytopic state-space block.

Implements a convex combination of linear state-space models:

x[k+1] = sum_{i=1}^r w_i[k] (A_i x[k] + B_i u[k])

y[k] = C x[k]

The weight vector w must be non-negative and sum to 1 at each step. Matrices A and B are provided as horizontal concatenations of the per-vertex matrices: A = [A_1, …, A_r] of shape (nx, r*nx) and B = [B_1, …, B_r] of shape (nx, r*nu).

A

Concatenated vertex state matrices of shape (nx, r*nx).

B

Concatenated vertex input matrices of shape (nx, r*nu).

C

Output matrix of shape (ny, nx).

direct_feedthrough = False

True if outputs depend directly on inputs.

initialize(t0: float) → None

Compute initial outputs from the initial state.

Parameters:

t0 – Initial simulation time in seconds.

output_update(t: float, dt: float) → None

Compute y and x outputs from the committed state.

Parameters:

• t – Current simulation time in seconds.

• dt – Current time step in seconds.

state_update(t: float, dt: float) → None

Compute the next state as a weighted sum of vertex dynamics.

Parameters:

• t – Current simulation time in seconds.

• dt – Current time step in seconds.

Raises:

• RuntimeError – If inputs w or u are not connected.

• ValueError – If w does not sum to 1, has negative entries, or if input shapes are wrong.