print('started continuous transition')
m = u_seq[0, :].size
for i in range(N):
u = u_seq[i, :].reshape(m, 1)
x = A.dot(x) + B.dot(u) + K
print('Discrete time: k = ' +str(i) )
print('\t u[' +str(i) +"]' = " +str(u.T) )
print('\t x[' +str(i) +"]' = " +str(x.T) +'\n')
print('completed continuous transition iteration')
return x
x0 = np.array([0.5, 0.6])
start = find_discrete_state(x0, disc_dynamics.ppp)
end = 14
start_poly = disc_dynamics.ppp.regions[start]
end_poly = disc_dynamics.ppp.regions[end]
if not is_inside(start_poly, x0):
raise Exception('x0 \\notin start_poly')
start_state = start
end_state = end
post = disc_dynamics.ts.states.post(start_state)
print(post)
if not end_state in post:
raise Exception('end \\notin post(start)')
print('started continuous transition')
m = u_seq[0, :].size
for i in xrange(N):
u = u_seq[i, :].reshape(m, 1)
x = A.dot(x) + B.dot(u) + K
print('Discrete time: k = ' + str(i))
print('\t u[' + str(i) + "]' = " + str(u.T))
print('\t x[' + str(i) + "]' = " + str(x.T) + '\n')
print('completed continuous transition iteration')
return x
x0 = np.array([0.5, 0.6])
start = find_discrete_state(x0, disc_dynamics.ppp)
end = 14
start_poly = disc_dynamics.ppp.regions[start]
end_poly = disc_dynamics.ppp.regions[end]
if not is_inside(start_poly, x0):
raise Exception('x0 \\notin start_poly')
start_state = 's' + str(start)
end_state = 's' + str(end)
post = disc_dynamics.ts.states.post(start_state)
print(post)
if not end_state in post:
raise Exception('end \\notin post(start)')
work with only_mode_controlled.py - make a manual AOFTS from there. Send it to synth. Change env_augmented_open_fts2spec to
call env_open_fts2spec and then append equil pts. Allow self_trans and see what controller happens
Put !OUTSIDE inside env_spec.
"""
#jt_fts = synth.synthesize('jtlv', specs, env=abstMOS.ts, ignore_env_init=True, rm_deadends=False)
gr_fts = synth.synthesize('gr1c', specs, env=abstMOS.ts,rm_deadends=True, ignore_env_init=True)
#print (gr_fts)
if not gr_fts.save('gr_fts.eps'):
print(gr_fts)
disc_dynamics=abstMOS
ctrl=gr_fts
Tc = [18.0]
Th = [16.0]
s0_part = find_discrete_state([Tc[0],Th[0]],disc_dynamics.ppp)
#mach = synth.determinize_machine_init(ctrl,{'sys_actions':'on'}) # - to be used if we want a certain mode only
sim_hor = 130
N=1 # N = number of steps between each sampled transition
(s1, dum) = mach.reaction('Sinit', {'eloc': 's0'})
#(s1, dum) = mach.reaction(s1, {'on': 1})
for sim_time in range(sim_hor):
sysnow=('regular',dum['sys_actions'])
#sysnow = #find your mode (read from mach)
for ind in range(N):
x = np.dot(
ssd.dynamics[sysnow].list_subsys[0].A, [Tc[-1],Th[-1]]
) + ssd.dynamics[sysnow].list_subsys[0].K.flatten()
Tc.append(x[0])
#jt_fts = synth.synthesize('jtlv', specs, env=abstMOS.ts, ignore_env_init=True, rm_deadends=False)
gr_fts = synth.synthesize('gr1c',
specs,
env=abstMOS.ts,
rm_deadends=True,
ignore_env_init=True)
#print (gr_fts)
if not gr_fts.save('gr_fts.eps'):
print(gr_fts)
disc_dynamics = abstMOS
ctrl = gr_fts
Tc = [18.0]
Th = [16.0]
s0_part = find_discrete_state([Tc[0], Th[0]], disc_dynamics.ppp)
#mach = synth.determinize_machine_init(ctrl,{'sys_actions':'on'}) # - to be used if we want a certain mode only
sim_hor = 130
N = 1 # N = number of steps between each sampled transition
(s1, dum) = mach.reaction('Sinit', {'eloc': 's0'})
#(s1, dum) = mach.reaction(s1, {'on': 1})
for sim_time in range(sim_hor):
sysnow = ('regular', dum['sys_actions'])
#sysnow = #find your mode (read from mach)
for ind in range(N):
x = np.dot(ssd.dynamics[sysnow].list_subsys[0].A,
[Tc[-1], Th[-1]
]) + ssd.dynamics[sysnow].list_subsys[0].K.flatten()
Tc.append(x[0])
def test_find_controller_non_convex():
"""Test that it is enough that one polytope in the target region is
reachable"""
# Continuous state space
cont_state_space = pc.box2poly([[0., 1.], [0., 2.]])
# System dynamics (continuous state, discrete time): simple double integrator
# but no reversing.
h = 1.0
A = np.array([[1.0, h], [0., 1.0]])
B = np.array([[h**2 / 2.0], [h]])
E = None
# Available control, no disturbances. Can brake or accelerate.
U = pc.box2poly(np.array([[-1., 1.]]))
W = None
# Construct the LTI system describing the dynamics
sys_dyn = hybrid.LtiSysDyn(A, B, E, None, U, W, cont_state_space)
# Define atomic propositions for relevant regions of state space
cont_props = dict()
cont_props['target'] = pc.box2poly([[0., 1.], [0., 2.]])
# Compute the proposition preserving partition of the continuous state space
# noinspection PyTypeChecker
cont_partition = abstract.prop2part(cont_state_space, cont_props)
# Abstraction
disc_dynamics = abstract.discretize(cont_partition,
sys_dyn,
closed_loop=False,
conservative=True,
N=1,
min_cell_volume=0.01,
plotit=False,
simu_type='bi',
trans_length=1)
# Setup a start point and a target point in a region that are problematic
c_start_state = np.array([0.5, 0.0])
c_end_desired = np.array([1.0, 2.0])
# Find the discrete states of the start state and the target region
d_start_state = abstract.find_discrete_state(c_start_state,
disc_dynamics.ppp)
d_end_state = abstract.find_discrete_state(c_end_desired,
disc_dynamics.ppp)
# Try to find a control policy
u = abstract.find_controller.get_input(x0=c_start_state,
ssys=sys_dyn,
abstraction=disc_dynamics,
start=d_start_state,
end=d_end_state,
ord=1,
mid_weight=5)
assert 0.5 <= u <= 1.0, "u was " + str(u)
# Try to find a control policy in the other direction and ascertain
# an error is thrown
assert_raises(Exception,
abstract.find_controller.get_input,
x0=c_end_desired,
ssys=sys_dyn,
abstraction=disc_dynamics,
start=d_end_state,
end=d_start_state,
ord=1,
mid_weight=5)
