optimo/examples/vdp.py at main · wedoco/optimo · GitHub

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import os
import numpy as np
import matplotlib.pyplot as plt
from wedoco_optimo.model import OptimoModel


def run_vdp(force_recompile=True, plot=True):
    # Compile and transfer the Modelica model
    mo = OptimoModel()
    model_file = os.path.join(os.path.dirname(__file__), "vdp.mo")
    mo.transfer_model(model="vdp", modelica_files=[model_file], force_recompile=force_recompile)

    # Simulate
    res_sim_df = mo.simulate()

    # Plot
    if plot:
        _, axs = plt.subplots(3, 1)
        res_sim_df[["x1","x2"]].plot(ax=axs[0], title="States", legend=True)
        res_sim_df["objectiveIntegrand"].plot(ax=axs[1], title="Objective", legend=True)
        res_sim_df["u"].plot(ax=axs[2], title="Input", legend=True)
        plt.show()

    # Optimize
    mo.define_optimization(constraints={"u":(-1, 0.75)},
                           objective_terms=["objectiveIntegrand"])
    res_ocp_df = mo.optimize(calculate_objective_terms_integrals=False)

    # Plot
    if plot:
        _, axs = plt.subplots(3, 1)
        res_ocp_df[["x1","x2"]].plot(ax=axs[0], title="States", legend=True)
        res_ocp_df["objectiveIntegrand"].plot(ax=axs[1], title="Objective", legend=True)
        res_ocp_df["u"].plot(ax=axs[2], title="Input", legend=True)
        plt.show()

    return res_sim_df, res_ocp_df

def run_vdp_simulate_with_input(force_recompile=False, plot=True):
    # Compile and transfer the Modelica model
    mo = OptimoModel()
    model_file = os.path.join(os.path.dirname(__file__), "vdp.mo")
    mo.transfer_model(model="vdp", modelica_files=[model_file], force_recompile=force_recompile)

    # Simulate with an input trajectory
    u_traj = np.sin(mo.tgrid)
    res_sim_df = mo.simulate(u_sim=u_traj)

    # Plot
    if plot:
        _, axs = plt.subplots(3, 1)
        res_sim_df[["x1","x2"]].plot(ax=axs[0], title="States", legend=True)
        res_sim_df["objectiveIntegrand"].plot(ax=axs[1], title="Objective", legend=True)
        res_sim_df["u"].plot(ax=axs[2], title="Input", legend=True)
        plt.show()

    return res_sim_df


def run_vdp_optimize_with_input(force_recompile=False, plot=True):
    # Compile and transfer the Modelica model
    mo = OptimoModel()
    model_file = os.path.join(os.path.dirname(__file__), "vdp_prescribed_input.mo")
    mo.transfer_model(model="vdpPrescribedInput", modelica_files=[model_file], force_recompile=force_recompile)

    # Optimize with an input trajectory
    u_traj = np.sin(mo.tgrid)
    mo.define_optimization(constraints={"u":(-1, 0.75)},
                           objective_terms=["objectiveIntegrand"],
                           prescribed_inputs={"u_prescribed": u_traj})
    res_ocp_df = mo.optimize(calculate_objective_terms_integrals=False)

    # Plot
    if plot:
        _, axs = plt.subplots(3, 1)
        res_ocp_df[["x1","x2"]].plot(ax=axs[0], title="States", legend=True)
        res_ocp_df["objectiveIntegrand"].plot(ax=axs[1], title="Objective", legend=True)
        res_ocp_df["u"].plot(ax=axs[2], title="Inputs", legend=True)
        res_ocp_df["u_prescribed"].plot(ax=axs[2], legend=True)
        plt.show()

    return res_ocp_df

if __name__ == "__main__":
    run_vdp()