use obc as standard prefix for control.optimal

murrayrm · murrayrm · commit 981104bf9b80 · 2023-03-31T13:16:38.000-07:00
diff --git a/examples/kincar-flatsys.py b/examples/kincar-flatsys.py
@@ -10,7 +10,7 @@
 import matplotlib.pyplot as plt
 import control as ct
 import control.flatsys as fs
-import control.optimal as opt
+import control.optimal as obc
 
 #
 # System model and utility functions
@@ -147,7 +147,7 @@ def plot_results(t, x, ud, rescale=True):
 basis = fs.PolyFamily(8)
 
 # Define the cost function (penalize lateral error and steering)
-traj_cost = opt.quadratic_cost(
+traj_cost = obc.quadratic_cost(
     vehicle_flat, np.diag([0, 0.1, 0]), np.diag([0.1, 1]), x0=xf, u0=uf)
 
 # Solve for an optimal solution
@@ -168,7 +168,7 @@ def plot_results(t, x, ud, rescale=True):
 
 # Constraint the input values
 constraints = [
-    opt.input_range_constraint(vehicle_flat, [8, -0.1], [12, 0.1]) ]
+    obc.input_range_constraint(vehicle_flat, [8, -0.1], [12, 0.1]) ]
 
 # TEST: Change the basis to use B-splines
 basis = fs.BSplineFamily([0, Tf/2, Tf], 6)
@@ -198,11 +198,11 @@ def plot_results(t, x, ud, rescale=True):
 #
 
 # Define the cost function (mainly penalize steering angle)
-traj_cost = opt.quadratic_cost(
+traj_cost = obc.quadratic_cost(
     vehicle_flat, None, np.diag([0.1, 10]), x0=xf, u0=uf)
 
 # Set terminal cost to bring us close to xf
-terminal_cost = opt.quadratic_cost(vehicle_flat, 1e3 * np.eye(3), None, x0=xf)
+terminal_cost = obc.quadratic_cost(vehicle_flat, 1e3 * np.eye(3), None, x0=xf)
 
 # Change the basis to use B-splines
 basis = fs.BSplineFamily([0, Tf/2, Tf], [4, 6], vars=2)
diff --git a/examples/mpc_aircraft.ipynb b/examples/mpc_aircraft.ipynb
@@ -19,7 +19,7 @@
    "source": [
     "import control as ct\n",
     "import numpy as np\n",
-    "import control.optimal as opt\n",
+    "import control.optimal as obc\n",
     "import matplotlib.pyplot as plt"
    ]
   },
@@ -70,15 +70,15 @@
     "# model.y.reference = ys;\n",
     "\n",
     "# provide constraints on the system signals\n",
-    "constraints = [opt.input_range_constraint(sys, [-5, -6], [5, 6])]\n",
+    "constraints = [obc.input_range_constraint(sys, [-5, -6], [5, 6])]\n",
     "\n",
     "# provide penalties on the system signals\n",
     "Q = model.C.transpose() @ np.diag([10, 10, 10, 10]) @ model.C\n",
     "R = np.diag([3, 2])\n",
-    "cost = opt.quadratic_cost(model, Q, R, x0=xd, u0=ud)\n",
+    "cost = obc.quadratic_cost(model, Q, R, x0=xd, u0=ud)\n",
     "\n",
     "# online MPC controller object is constructed with a horizon 6\n",
-    "ctrl = opt.create_mpc_iosystem(model, np.arange(0, 6) * 0.2, cost, constraints)"
+    "ctrl = obc.create_mpc_iosystem(model, np.arange(0, 6) * 0.2, cost, constraints)"
    ]
   },
   {
diff --git a/examples/steering-optimal.py b/examples/steering-optimal.py
@@ -8,7 +8,7 @@
 import numpy as np
 import math
 import control as ct
-import control.optimal as opt
+import control.optimal as obc
 import matplotlib.pyplot as plt
 import logging
 import time
@@ -106,7 +106,7 @@ def plot_lanechange(t, y, u, yf=None, figure=None):
 # Set up the cost functions
 Q = np.diag([.1, 10, .1])       # keep lateral error low
 R = np.diag([.1, 1])            # minimize applied inputs
-quad_cost = opt.quadratic_cost(vehicle, Q, R, x0=xf, u0=uf)
+quad_cost = obc.quadratic_cost(vehicle, Q, R, x0=xf, u0=uf)
 
 # Define the time horizon (and spacing) for the optimization
 timepts = np.linspace(0, Tf, 20, endpoint=True)
@@ -124,7 +124,7 @@ def plot_lanechange(t, y, u, yf=None, figure=None):
 
 # Compute the optimal control, setting step size for gradient calculation (eps)
 start_time = time.process_time()
-result1 = opt.solve_ocp(
+result1 = obc.solve_ocp(
     vehicle, timepts, x0, quad_cost, initial_guess=straight_line, log=True,
     # minimize_method='trust-constr',
     # minimize_options={'finite_diff_rel_step': 0.01},
@@ -158,9 +158,9 @@ def plot_lanechange(t, y, u, yf=None, figure=None):
 print("\nApproach 2: input cost and constraints plus terminal cost")
 
 # Add input constraint, input cost, terminal cost
-constraints = [ opt.input_range_constraint(vehicle, [8, -0.1], [12, 0.1]) ]
-traj_cost = opt.quadratic_cost(vehicle, None, np.diag([0.1, 1]), u0=uf)
-term_cost = opt.quadratic_cost(vehicle, np.diag([1, 10, 10]), None, x0=xf)
+constraints = [ obc.input_range_constraint(vehicle, [8, -0.1], [12, 0.1]) ]
+traj_cost = obc.quadratic_cost(vehicle, None, np.diag([0.1, 1]), u0=uf)
+term_cost = obc.quadratic_cost(vehicle, np.diag([1, 10, 10]), None, x0=xf)
 
 # Change logging to keep less information
 logging.basicConfig(
@@ -175,7 +175,7 @@ def plot_lanechange(t, y, u, yf=None, figure=None):
 
 # Compute the optimal control
 start_time = time.process_time()
-result2 = opt.solve_ocp(
+result2 = obc.solve_ocp(
     vehicle, timepts, x0, traj_cost, constraints, terminal_cost=term_cost,
     initial_guess=straight_line, log=True,
     # minimize_method='SLSQP', minimize_options={'eps': 0.01}
@@ -207,10 +207,10 @@ def plot_lanechange(t, y, u, yf=None, figure=None):
 
 # Input cost and terminal constraints
 R = np.diag([1, 1])                 # minimize applied inputs
-cost3 = opt.quadratic_cost(vehicle, np.zeros((3,3)), R, u0=uf)
+cost3 = obc.quadratic_cost(vehicle, np.zeros((3,3)), R, u0=uf)
 constraints = [
-    opt.input_range_constraint(vehicle, [8, -0.1], [12, 0.1]) ]
-terminal = [ opt.state_range_constraint(vehicle, xf, xf) ]
+    obc.input_range_constraint(vehicle, [8, -0.1], [12, 0.1]) ]
+terminal = [ obc.state_range_constraint(vehicle, xf, xf) ]
 
 # Reset logging to its default values
 logging.basicConfig(
@@ -219,7 +219,7 @@ def plot_lanechange(t, y, u, yf=None, figure=None):
 
 # Compute the optimal control
 start_time = time.process_time()
-result3 = opt.solve_ocp(
+result3 = obc.solve_ocp(
     vehicle, timepts, x0, cost3, constraints,
     terminal_constraints=terminal, initial_guess=straight_line, log=False,
     # solve_ivp_kwargs={'atol': 1e-3, 'rtol': 1e-2},
@@ -254,7 +254,7 @@ def plot_lanechange(t, y, u, yf=None, figure=None):
 
 # Compute the optimal control
 start_time = time.process_time()
-result4 = opt.solve_ocp(
+result4 = obc.solve_ocp(
     vehicle, timepts, x0, quad_cost,
     constraints,
     terminal_constraints=terminal,
