Betts 10 113

examples-gallery/plot_betts_10_113.py

@@ -0,0 +1,141 @@
+"""
+Mixed State-Control Constraints
+===============================
+
+This is example 10.113 from Betts' book "Practical Methods for Optimal Control
+Using NonlinearProgramming", 3rd edition, Chapter 10: Test Problems.
+
+This simulation shows how to handle inequality constraints, here of the form:
+:math:`0 \\geq u + \\dfrac{y_1}{6}`.
+
+I  set:
+:math:`J = u + \\dfrac{y_1}{6}`, and bound :math:`J \\leq 0`.
+
+
+**States**
+
+- :math:`y_1, y_2` : state variables
+- :math:`J` : additional state variable to handle the constraint.
+
+**Controls**
+
+- :math:`u` : control variable
+
+Note: I simply copied the equations of motion, the bounds and the constants
+from the book. I do not know their meaning.
+
+"""
+
+import numpy as np
+import sympy as sm
+import sympy.physics.mechanics as me
+from opty.direct_collocation import Problem
+from opty.utils import create_objective_function
+
+# %%
+# Equations of Motion
+# -------------------
+t = me.dynamicsymbols._t
+
+# Parameter
+p = 0.14
+
+y1, y2, J = me.dynamicsymbols(f'y1, y2, J')
+u = me.dynamicsymbols('u')
+
+eom = sm.Matrix([
+    -y1.diff(t) + y2,
+    -y2.diff(t) -y1  + y2*(1.4 - p*y2**2) + 4*u,
+    -J + u + y1/6
+])
+
+sm.pprint(eom)
+
+# %%
+# Define and Solve the Optimization Problem
+# -----------------------------------------
+num_nodes = 1001
+t0, tf = 0.0, 4.5
+interval_value = (tf - t0) / (num_nodes - 1)
+
+state_symbols = (y1, y2, J)
+unkonwn_input_trajectories = (u,)
+
+# %%
+# Specify the objective function and form the gradient.
+objective = sm.Integral(u**2 + y1**2, t)
+
+obj, obj_grad = create_objective_function(
+    objective,
+    state_symbols,
+    unkonwn_input_trajectories,
+    tuple(),
+    num_nodes,
+    interval_value
+)
+
+# %%
+
+instance_constraints = (
+    y1.func(t0) + 5,
+    y2.func(t0) + 5,
+)
+
+# %%
+# Here I bound J <= 0.0
+limit_value = np.inf
+bounds = {
+   J: (-limit_value, 0.0),
+}
+
+# %%
+# Iterate
+# -------
+
+prob = Problem(obj,
+        obj_grad,
+        eom,
+        state_symbols,
+        num_nodes,
+        interval_value,
+        instance_constraints= instance_constraints,
+        bounds=bounds,
+)
+
+prob.add_option('max_iter', 1000)
+
+initial_guess = np.ones(prob.num_free) * 0.1
+# Find the optimal solution.
+for i in range(1):
+    solution, info = prob.solve(initial_guess)
+    initial_guess = solution
+    print(info['status_msg'])
+    print(f'Objective value achieved: {info['obj_val']:.4f}, as per the book '+
+        f'it is {44.8044433}, so the improvement of the value in the book is is: '
+        f'{(-info['obj_val'] + 44.8044433)/44.8044433*100:.3f} % ')
+    print('\n')
+
+
+# %%
+# Plot the optimal state and input trajectories.
+prob.plot_trajectories(solution)
+
+# %%
+# Plot the constraint violations.
+prob.plot_constraint_violations(solution)
+
+# %%
+# Plot the objective function.
+prob.plot_objective_value()
+
+
+# %%
+# Is the inequality constraint satisfied at all points in time?
+max_J = np.max(solution[2*num_nodes: 3*num_nodes-1])
+if max_J <= 0.0:
+    print(f"Minimal value of the J\u1D62 is: {max_J:.3e} <= 0.0, so satisfied")
+else:
+    print(f"Minimal value of the J\u1D62 is: {max_J:.3e} > 0.0, so not satisfied")
+
+# %%
+# sphinx_gallery_thumbnail_number = 2