Control System functions
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Control System functions
Algorithms for designing, analyzing and tuning linear control systems.
- Verifies the dimensional compatibility of matrices A, B, C, and D.
- Pole placement gain selection using Ackermann's formula.
- Appends the inputs and outputs of the two models.
- Append state vector to output vector.
- Gramian-based balancing of state-space realizations.
- Block-diagonal Schur factorization.
- Bode plot of frequency response, magnitude and phase data.
- Convert model from continuous to discrete time.
- Continuous-time algebraic Riccati equation solution.
- Feedback connection of multiple models.
- Companion realization of transfer functions.
- Controllability of state-space model.
- Compute controllability staircase form.
- Convert model from discrete to continuous time.
- Natural frequency and damping ratio.
- Discret-time algebraic Riccati equation solution.
- Low-frequency (DC) gain of LTI system.
- Linear-quadratic (LQ) state-feedback regulator for discrete-time state-space system.
- Discrete-time Lyapunov equations.
- Sort discrete-time poles by magnitude.
- Sort continuous-time poles by real part.
- Evaluate frequency response at given frequency.
- Feedback connection of multiple models.
- Evaluate system response over a grid of frequencies.
- Create periodic signals for simulating system response.
- Controllability and observability Gramians.
- Hankel singular values of dynamic system.
- Impulse response plot of dynamic system.
- System response to initial states of state-space model.
- Checks if dynamic system model is in continuous time.
- Checks if dynamic system model is in discret time.
- Checks if variable is an linear model tf, ss or zpk.
- Checks if dynamic system model is single input and single output.
- Checks if model is static or dynamic.
- Design Kalman filter for state estimation.
- Kalman estimator design for continuous-time systems.
- Calculates the discrete Kalman estimator configuration based on a continuous cost function.
- Linear-Quadratic Regulator (LQR) design.
- Form linear-quadratic (LQ) state-feedback regulator with output weighting.
- Plot simulated time response of dynamic system to arbitrary inputs.
- Continuous Lyapunov equation solution.
- Minimal realization or pole-zero cancellation.
- Nyquist plot of frequency response.
- Observability of state-space model.
- Compute observability staircase form.
- Generate continuous second-order systems.
- Computes the Pade approximation of time delays.
- Parallel connection of two models.
- Poles of dynamic system.
- Series connection of two models.
- State-space model.
- Convert state-space representation to transfer function.
- Access state-space model data.
- Remove inputs, outputs and states from state-space system.
- Extract subsystem from larger system.
- Step response plot of dynamic system.
- Constructs a transfer function model.
- Convert transfer function filter parameters to state-space form.
- Access transfer function model data.
- Invariant zeros of linear system.
- Zeros and gain of SISO dynamic system.