rcognita.systems.SysInvertedPendulumPD

class rcognita.systems.SysInvertedPendulumPD(*args, **kwargs)

__init__(*args, **kwargs)

Parameters

• sys_type (: string) –

  Type of system by description:

  diff_eqn : differential equation \(\mathcal D state = f(state, action, disturb)\)

  discr_fnc : difference equation \(state^+ = f(state, action, disturb)\)

  discr_prob : by probability distribution \(X^+ \sim P_X(state^+| state, action, disturb)\)

• where –

  \(state\) : state

  \(action\) : input

  \(disturb\) : disturbance

• time variable time is commonly used by ODE solvers (The) –

• you shouldn't have it explicitly referenced in the definition (and) –

• your system is non-autonomous. (unless) –

• the latter case (For) –

• however –

• already have the input and disturbance at your disposal. (you) –

• of the system are contained in pars attribute. (Parameters) –

• dim_state (: integer) – System dimensions

• dim_input (: integer) – System dimensions

• dim_output (: integer) – System dimensions

• dim_disturb (: integer) – System dimensions

• pars (: list) – List of fixed parameters of the system

• action_bounds (: array of shape [dim_input, 2]) – Box control constraints. First element in each row is the lower bound, the second - the upper bound. If empty, control is unconstrained (default)

• is_dynamic_controller (: 0 or 1) – If 1, the controller (a.k.a. agent) is considered as a part of the full state vector

• is_disturb (: 0 or 1) – If 0, no disturbance is fed into the system

• pars_disturb (: list) – Parameters of the disturbance model

Methods

__init__(*args, **kwargs)	param sys_type Type of system by description:
compute_closed_loop_rhs(time, state_full)	Right-hand side of the closed-loop system description.
compute_dynamics(time, state, action[, disturb])	Description of the system internal dynamics.
out(state[, time, action])	System output.
receive_action(action)	Receive exogeneous control action to be fed into the system.
reset()