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Architecture

Synapsys is built in six layers. Each layer has a single responsibility and depends only on the layers below. This means you can use the mathematical core alone without touching the agent infrastructure, or swap the transport without changing any control logic.

Layer diagram​

Design decisions​

API / math engine separation​

The synapsys.api layer is a convenience wrapper only. All mathematics lives in synapsys.core. This allows the API to evolve without touching the numerical core.

LTI class operator overloading​

G1 * G2 (series), G1 + G2 (parallel), and G.feedback() allow composing systems with natural block algebra:

T = (C * G).feedback()   # closed loop: C in series with G

Strategy pattern in transport​

PlantAgent and ControllerAgent do not know how data is sent. They call transport.write() and transport.read(). The concrete implementation is injected at construction time — no control logic changes needed.

Transport lifecycle​

The transport is owned by the caller, not the agent. The agent never calls transport.close(). This prevents double-free when multiple agents share views of the same memory block.

Continuous vs discrete​

StateSpace(A, B, C, D, dt=0) is continuous. dt > 0 is discrete. The same class supports both:

  • is_stable() uses Re(poles) < 0 for continuous and |poles| < 1 for discrete
  • step() delegates to scipy.signal.step or scipy.signal.dstep automatically
  • evolve(x, u) executes x(k+1) = Ax + Bu step by step for real-time simulation