classic_controllers_dc_motor_example.py

from classic_controllers import Controller
from externally_referenced_state_plot import ExternallyReferencedStatePlot

import gym_electric_motor as gem
from gym_electric_motor.envs.motors import ActionType, ControlType, Motor, MotorType
from gym_electric_motor.visualization import MotorDashboard
from gym_electric_motor.visualization.render_modes import RenderMode

if __name__ == "__main__":
    """
    motor type:     'PermExDc'  Permanently Excited DC Motor
                    'ExtExDc'   Externally Excited MC Motor
                    'SeriesDc'  DC Series Motor
                    'ShuntDc'   DC Shunt Motor
                    
    control type:   'SC'         Speed Control
                    'TC'         Torque Control
                    'CC'         Current Control
                    
    action_type:    'Cont'      Continuous Action Space
                    'Finite'    Discrete Action Space
    """

    """
    motor_type = "PermExDc"
    control_type = "TC"
    action_type = "Cont"
    """


    motor = Motor(
        MotorType.PermanentlyExcitedDcMotor,
        ControlType.TorqueControl,
        ActionType.Continuous,
    )


    # definition of the plotted variables
    external_ref_plots = [ExternallyReferencedStatePlot(state) for state in motor.states()]

    motor_dashboard = MotorDashboard(additional_plots=external_ref_plots, render_mode=RenderMode.Figure)
    # initialize the gym-electric-motor environment
    env = gem.make(
        motor.env_id(),
        visualization=motor_dashboard,
    )
    """
        initialize the controller

        Args:
            environment                     gym-electric-motor environment
            external_ref_plots (optional)   plots of the environment, to plot all reference values
            stages (optional)               structure of the controller
            automated_gain (optional)       if True (default), the controller will be tuned automatically
            a (optional)                    tuning parameter of the symmetrical optimum (default: 4)
    
    """

    controller = Controller.make(env, external_ref_plots=external_ref_plots)

    (state, reference), _ = env.reset(seed=None)
    # simulate the environment
    for i in range(10001):
        action = controller.control(state, reference)
        (state, reference), reward, terminated, truncated, _ = env.step(action)
        if terminated:
            env.reset()
            controller.reset()

    motor_dashboard.show()
    env.close()