irlc.ex04.continuous_time_model.ContiniousTimeSymbolicModel

class irlc.ex04.continuous_time_model.ContiniousTimeSymbolicModel(cost=None, simple_bounds=None)

Continious time symbolic model. See (Her21, Section 11.3) for a top-level description.

This model represents the top-level description of the physical system as a differential equation

> dx/dt = f(x, u, t)

and a cost-function defined as an integral:

> Cost = g(x(t0), x(tf), t0, tf) + int_t0^tf g(x, u, t) dt

and bounds on x, u and t.

In this description both x and u are vectors.

The overall idea is that you write a new model by editing the def sym_f function. Add a symbolic expression here, and the class will automatically convert it into a numpy function, and allow e.g. the discrete model to compute derivatives.

__init__(cost=None, simple_bounds=None)

Methods

__init__([cost, simple_bounds])
animate_rollout(x0, u_fun, t0, tF[, ...])
close()
guess()
render(x[, mode])
reset()
set_simple_bounds(bounds)
simple_bounds()	Simple inequality constraints (i.e.
simulate(x0, u_fun, t0, tF[, N_steps, method])	Defaults to RK4 simulation of the trajectory from x0, u0, t0 to tf, see (Her21, Algorithm 18) Method can be either 'rk4' or 'euler'
sym_c(x, u, t)	Compute Lagrange term in cost function
sym_cf(t0, tF, x0, xF)	Compute Mayer term in cost function
sym_f(x, u[, t])
sym_g(t0, tF, x0, xF)	Boundary constraints
sym_h(x, u, t)	Dynamical path constraint of the form: (See (Kel17, Eq.(1.3)))

Attributes

action_labels
action_size
action_space
observation_space
state_labels
state_size