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Commit4d89991

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Merge pull request#800 from murrayrm/benchmarks-24Aug2022
Update benchmarks to help with optimal control tuning
2 parentsed4ff84 +54de2a3 commit4d89991

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9 files changed

+285
-138
lines changed

9 files changed

+285
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lines changed

‎benchmarks/README

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Original file line numberDiff line numberDiff line change
@@ -11,7 +11,7 @@ you can use the following command from the root directory of the repository:
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PYTHONPATH=`pwd` asv run --python=python
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You can also run benchmarks against specific commitsusuing
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You can also run benchmarks against specific commitsusing
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asv run <range>
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‎benchmarks/flatsys_bench.py

Lines changed: 71 additions & 13 deletions
Original file line numberDiff line numberDiff line change
@@ -11,6 +11,10 @@
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importcontrol.flatsysasflat
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importcontrol.optimalasopt
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#
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# System setup: vehicle steering (bicycle model)
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#
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# Vehicle steering dynamics
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defvehicle_update(t,x,u,params):
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# Get the parameters for the model
@@ -67,11 +71,28 @@ def vehicle_reverse(zflag, params={}):
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# Define the time points where the cost/constraints will be evaluated
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timepts=np.linspace(0,Tf,10,endpoint=True)
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deftime_steering_point_to_point(basis_name,basis_size):
71-
ifbasis_name=='poly':
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basis=flat.PolyFamily(basis_size)
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elifbasis_name=='bezier':
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basis=flat.BezierFamily(basis_size)
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#
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# Benchmark test parameters
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#
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basis_params= (['poly','bezier','bspline'], [8,10,12])
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basis_param_names= ["basis","size"]
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defget_basis(name,size):
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ifname=='poly':
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basis=flat.PolyFamily(size,T=Tf)
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elifname=='bezier':
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basis=flat.BezierFamily(size,T=Tf)
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elifname=='bspline':
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basis=flat.BSplineFamily([0,Tf/2,Tf],size)
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returnbasis
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#
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# Benchmarks
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#
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deftime_point_to_point(basis_name,basis_size):
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basis=get_basis(basis_name,basis_size)
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# Find trajectory between initial and final conditions
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traj=flat.point_to_point(vehicle,Tf,x0,u0,xf,uf,basis=basis)
@@ -80,13 +101,16 @@ def time_steering_point_to_point(basis_name, basis_size):
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x,u=traj.eval([0,Tf])
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np.testing.assert_array_almost_equal(x0,x[:,0])
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np.testing.assert_array_almost_equal(u0,u[:,0])
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np.testing.assert_array_almost_equal(xf,x[:,1])
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np.testing.assert_array_almost_equal(uf,u[:,1])
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np.testing.assert_array_almost_equal(xf,x[:,-1])
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np.testing.assert_array_almost_equal(uf,u[:,-1])
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time_point_to_point.params=basis_params
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time_point_to_point.param_names=basis_param_names
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time_steering_point_to_point.params= (['poly','bezier'], [6,8])
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time_steering_point_to_point.param_names= ["basis","size"]
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deftime_steering_cost():
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deftime_point_to_point_with_cost(basis_name,basis_size):
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basis=get_basis(basis_name,basis_size)
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# Define cost and constraints
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traj_cost=opt.quadratic_cost(
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vehicle,None,np.diag([0.1,1]),u0=uf)
@@ -95,13 +119,47 @@ def time_steering_cost():
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traj=flat.point_to_point(
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vehicle,timepts,x0,u0,xf,uf,
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cost=traj_cost,constraints=constraints,basis=flat.PolyFamily(8)
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cost=traj_cost,constraints=constraints,basis=basis,
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)
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# Verify that the trajectory computation is correct
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x,u=traj.eval([0,Tf])
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np.testing.assert_array_almost_equal(x0,x[:,0])
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np.testing.assert_array_almost_equal(u0,u[:,0])
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np.testing.assert_array_almost_equal(xf,x[:,1])
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np.testing.assert_array_almost_equal(uf,u[:,1])
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np.testing.assert_array_almost_equal(xf,x[:,-1])
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np.testing.assert_array_almost_equal(uf,u[:,-1])
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time_point_to_point_with_cost.params=basis_params
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time_point_to_point_with_cost.param_names=basis_param_names
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deftime_solve_flat_ocp_terminal_cost(method,basis_name,basis_size):
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basis=get_basis(basis_name,basis_size)
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# Define cost and constraints
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traj_cost=opt.quadratic_cost(
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vehicle,None,np.diag([0.1,1]),u0=uf)
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term_cost=opt.quadratic_cost(
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vehicle,np.diag([1e3,1e3,1e3]),None,x0=xf)
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constraints= [
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opt.input_range_constraint(vehicle, [8,-0.1], [12,0.1]) ]
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# Initial guess = straight line
148+
initial_guess=np.array(
149+
[x0[i]+ (xf[i]-x0[i])*timepts/Tfforiin (0,1)])
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151+
traj=flat.solve_flat_ocp(
152+
vehicle,timepts,x0,u0,basis=basis,initial_guess=initial_guess,
153+
trajectory_cost=traj_cost,constraints=constraints,
154+
terminal_cost=term_cost,minimize_method=method,
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)
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# Verify that the trajectory computation is correct
158+
x,u=traj.eval([0,Tf])
159+
np.testing.assert_array_almost_equal(x0,x[:,0])
160+
np.testing.assert_array_almost_equal(xf,x[:,-1],decimal=2)
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time_solve_flat_ocp_terminal_cost.params=tuple(
163+
[['slsqp','trust-constr']]+list(basis_params))
164+
time_solve_flat_ocp_terminal_cost.param_names=tuple(
165+
['method']+basis_param_names)

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