Function reference

harold is a Python package that provides tools for analyzing feedback control systems and designing controllers.

System creation

State(a[, b, c, d, dt])	A class for creating State space models.
Transfer(num[, den, dt])	A class for creating Transfer functions.
random_state_model(n[, p, m, dt, prob_dist, …])	Generates a continuous or discrete State model with random data.
transfer_to_state(G[, output])	Converts a Transfer to a State
state_to_transfer(state_or_abcd[, output])	Converts a State to a Transfer

Discretization

discretize(G, dt[, method, prewarp_at, q])	Continuous- to discrete-time model conversion.
undiscretize(G[, method, prewarp_at, q])	Discrete- to continuous-time model conversion.

Controller Design

lqr(G, Q[, R, S, weight_on])	A full-state static feedback control design solver for which the following quadratic cost function is integrated (summed) over all positive time axis .
ackermann(G, loc)	Pole placement using Ackermann’s polynomial method.
place_poles(*args, **kwargs)	An error only function for recommending SciPy’s algorithm.

Model Functions

transmission_zeros(A, B, C, D)	Computes the transmission zeros of State data arrays A, B, C, D
system_norm(G[, p, hinf_tol, eig_tol])	Computes the system p-norm.
cancellation_distance(F, G)	Computes the upper and lower bounds of the perturbation needed to render the pencil \([F-pI | G]\) rank deficient.
controllability_indices(A, B[, tol])	Computes the controllability indices for a controllable pair (A, B)

Kalman tests

controllability_matrix(G[, compress])	Computes the Kalman controllability and the transformation matrix.
observability_matrix(G[, compress])	Computes the Kalman controllability and the transformation matrix.
is_kalman_controllable(G)	Tests the rank of the Kalman controllability matrix and compares it with the A matrix size, returns a boolean depending on the outcome.
is_kalman_observable(G)	Tests the rank of the Kalman observability matrix and compares it with the A matrix size, returns a boolean depending on the outcome.

Time domain simulation

simulate_linear_system(sys, u[, t, x0, …])	Compute the linear model response to an input array sampled at given time instances.
simulate_step_response(sys[, t])	Compute the linear model response to an Heaviside function (or all-ones array) sampled at given time instances.
simulate_impulse_response(sys[, t])	Compute the linear model response to an Dirac delta pulse (or all-zeros array except the first sample being 1/dt at each channel) sampled at given time instances.
impulse_response_plot(sys[, t, style])	Plots the impulse response of a model.
step_response_plot(sys[, t, style])	Plots the step response of model(s).

Frequency domain simulation

frequency_response(G[, w, samples, w_unit, …])	Compute the frequency response of a State() or Transfer() representation.
bode_plot(sys[, w, use_db, use_hz, …])	Draw the Bode plot of State, Transfer model(s).
nyquist_plot(sys[, w, use_hz, …])	Draw the Nyquist plot of State, Transfer model(s).

Model simplification tools

feedback(G, H[, negative])	Feedback interconnection of two models in the following configuration .
minimal_realization(G[, tol])	Given system realization G, this computes minimal realization such that if a State representation is given then the returned representation is controllable and observable within the given tolerance tol.
hessenberg_realization(G[, compute_T, form, …])	A state transformation is applied in order to get the following form where A is a Hessenberg matrix and B (or C if ‘form’ is set to ‘o’ ) is row/col compressed .
staircase(A, B, C[, compute_T, form, …])	Given a state model data A, B, C, Returns the so-called staircase form State realization to assess the controllability/observability properties.
kalman_decomposition(G[, compute_T, output, …])	By performing a sequence of similarity transformations the State representation is transformed into a special structure such that if the system has uncontrollable/unobservable modes, the corresponding rows/columns of the B/C matrices have zero blocks and the modes are isolated in the A matrix.

Polynomial Operations

haroldgcd(*args)	Takes 1D numpy arrays and computes the numerical greatest common divisor polynomial.
haroldlcm(*args[, compute_multipliers, …])	Takes n-many 1D numpy arrays and computes the numerical least common multiple polynomial.
haroldpolyadd(*args[, trim_zeros])	A wrapper around NumPy’s numpy.polyadd() but allows for multiple args and offers a trimming option.
haroldpolymul(*args[, trim_zeros])	Simple wrapper around the numpy.convolve() function for polynomial multiplication with multiple args.
haroldpolydiv(dividend, divisor)	Polynomial division wrapped around scipy.signal.deconvolve() function.
haroldcompanion(somearray)	Takes a 1D numpy array or list and returns the companion matrix of the monic polynomial of somearray.
haroldker(N[, side])	This function is a straightforward basis computation for the right/left nullspace for rank deficient or fat/tall matrices.

Auxillary Functions

matrix_slice(M, corner_shape[, corner])	Takes a two dimensional array M and slices into four parts dictated by the corner_shape and the corner string corner.
e_i(width[, nth, output])	Returns the nth column(s) of the identity matrix with shape (width,width).
concatenate_state_matrices(G)	Takes a State() model as input and returns the A, B, C, D matrices combined into a full matrix.
haroldsvd(A[, also_rank, rank_tol])	This is a wrapper/container function of both the SVD decomposition and the rank computation.