diff --git a/aerosandbox/optimization/test_optimization/test_racecar_adaptive_ode.py b/aerosandbox/optimization/test_optimization/test_racecar_adaptive_ode.py
new file mode 100644
index 00000000..7bed9c25
--- /dev/null
+++ b/aerosandbox/optimization/test_optimization/test_racecar_adaptive_ode.py
@@ -0,0 +1,112 @@
+import aerosandbox as asb
+import aerosandbox.numpy as np
+from aerosandbox.numpy.integrate import solve_ivp
+import pytest
+
+
+def test_racecar_adaptive_ode(plot=False):
+
+    N = 100  # number of control intervals
+
+    opti = asb.Opti()  # Optimization problem
+
+    t_final = opti.variable(init_guess=1, lower_bound=1e-2)
+    u_amp = opti.variable(init_guess=np.ones(10), lower_bound=0, upper_bound=1)
+
+    # u_amp = opti.variable(init_guess=np.ones(20) * 0.5, lower_bound=-0.1, upper_bound=0.1)
+
+    # u_amp = opti.variable(init_guess=np.eye(10)[0],)
+
+    def u(t):
+        u = 0
+        # # Fourier
+        # for i in range(np.length(u_amp)):
+        #     u += u_amp[i] * np.cos(np.pi * (i) * t / t_final)
+
+        # Bernstein
+        from scipy.special import comb
+        tn = t / t_final
+        for i in range(np.length(u_amp)):
+            u += (
+                    u_amp[i]
+                    * comb(np.length(u_amp) - 1, i)
+                    * tn ** i
+                    * (1 - tn) ** (np.length(u_amp) - 1 - i)
+            )
+
+        # # # Step
+        # index = np.floor(t / t_final * np.length(u_amp))
+        # for i in range(np.length(u_amp)):
+        #     if i == 0:
+        #         weight = np.where(index <= 0, 1, 0)
+        #     elif i == np.length(u_amp) - 1:
+        #         weight = np.where(index >= i, 1, 0)
+        #     else:
+        #         weight = np.where(index == i, 1, 0)
+        #
+        #     u += u_amp[i] * weight
+
+        return u
+
+    def func(t, y):
+        return np.array([
+            y[1],
+            u(t) - y[1],
+        ])
+
+    res = solve_ivp(
+        fun=func,
+        t_span=(0, t_final),
+        y0=[0, 0],
+    )
+    t = res.t
+    x = res.y[0, :]
+    v = res.y[1, :]
+
+    opti.minimize(t_final)
+
+    opti.subject_to([
+        v <= 1 - np.sin(2 * np.pi * x) / 2,
+        x[-1] >= 1,
+    ])
+
+    def callback(i):
+        soli = asb.OptiSol(opti, opti.debug)
+
+        print(f"t_final: {soli(t_final)}")
+
+        if i % 5 == 0:
+            import matplotlib.pyplot as plt
+            import aerosandbox.tools.pretty_plots as p
+            fig, ax = plt.subplots()
+            ax.plot(soli(t), soli(v), label="speed")
+            ax.plot(soli(t), soli(x), label="position")
+            ax.plot(soli(t), soli(1 - np.sin(2 * np.pi * x) / 2), "r--", label="speed limit")
+            t_plot = np.linspace(0, soli(t_final), 1000)
+            ax.plot(t_plot, soli(u(t_plot)), "k", label="throttle")
+            plt.ylim(-0.1, 1.6)
+            p.show_plot(f"i = {i}")
+
+    sol = opti.solve(
+        # callback=callback,
+        # verbose=False,
+        # behavior_on_failure="return_last"
+    )
+
+    assert sol(t_final) == pytest.approx(1.9683594, abs=1e-3)
+
+    if plot:
+        import matplotlib.pyplot as plt
+        import aerosandbox.tools.pretty_plots as p
+
+        fig, ax = plt.subplots()
+        ax.plot(sol(t), sol(v), label="speed")
+        ax.plot(sol(t), sol(x), label="position")
+        ax.plot(sol(t), sol(1 - np.sin(2 * np.pi * x) / 2), "r--", label="speed limit")
+        t_plot = np.linspace(0, sol(t_final), 1000)
+        ax.plot(t_plot, sol(u(t_plot)), "k", label="throttle")
+        plt.xlabel("Time [s]")
+        plt.ylabel("Position [m] / Speed [m/s] / Throttle [-]")
+        p.show_plot(
+            rotate_axis_labels=False
+        )