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MPC_testing.py
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MPC_testing.py
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#%%
import numpy as np
import do_mpc
from casadi import *
from SI import data_collection as Pendulum
import sys
from time import sleep, perf_counter
from multiprocessing import Process, Array, Value
#%%
def get_mpc():
model = do_mpc.model.Model('continuous')
y0 = model.set_variable('_x', 'y_0')
y1 = model.set_variable('_x', 'y_1')
dy = model.set_variable('_x', 'dy', shape=(2, 1))
f = model.set_variable('_u', 'f')
l = model.set_variable('_p', 'l')
ma = model.set_variable('_p', 'ma')
mb = model.set_variable('_p', 'mb')
ke = model.set_variable('_p', 'ke')
kf = model.set_variable('_p', 'kf')
# sp = model.set_variable('_tvp', 'sp')
sp = 0
# -ke*dy[0]+12*f)-kf*dy[0]
expr = vertcat(
(296.296296296296*pi*ke*(-ke*dy[0]+12*f)-kf*dy[0]-((l*mb*(-9.8*l*mb*sin(y1)-(l*mb*(296.296296296296*pi*ke*(-ke*dy[0]+12*f)-kf*dy[0]+l*mb*(dy[1]**2)*sin(y1)))*cos(y1)))/((-((l**2)*(mb**2)*(cos(y1)**2))/(ma+mb))+(l**2)*mb)) + l*mb*(dy[1]**2)*sin(y1))/(ma+mb),
((-9.8*l*mb*sin(y1))-((l*mb*(296.296296296296*pi*ke*(-ke*dy[0]+12*f)-kf*dy[0]+l*mb*(dy[1]**2)*sin(y1)))/(ma+mb)))/((-((l**2)*(mb**2)*(cos(y1)**2))/(ma+mb))+(l**2)*mb)
)
expr = vertcat(
(-296.296296296296*pi*ke*(-ke*dy[0] + 12*f) - kf*dy[0] - l*mb*(-9.8*l*mb*sin(y1) - l*mb*(-296.296296296296*pi*ke*(-ke*dy[0] + 12*f) - kf*dy[0] + l*mb*dy[1]**2*sin(y1))*cos(y1)/(ma + mb))*cos(y1)/(-l**2*mb**2*cos(y1)**2/(ma + mb) + l**2*mb) + l*mb*dy[1]**2*sin(y1))/(ma + mb),
(-9.8*l*mb*sin(y1) - l*mb*(-296.296296296296*pi*ke*(-ke*dy[0] + 12*f) - kf*dy[0] + l*mb*dy[1]**2*sin(y1))*cos(y1)/(ma + mb))/(-l**2*mb**2*cos(y1)**2/(ma + mb) + l**2*mb),
)
PE = 100*9.8*l*mb*(1-cos(y1))
KE = 0.5*ma*dy[0]**2 + 0.5*mb*((sin(y1)*dy[1])**2 + (dy[0]+cos(y1)*dy[1])**2)
model.set_expression('E_kin', KE)
model.set_expression('E_pot', PE)
#%%
model.set_rhs('y_0', dy[0])
model.set_rhs('y_1', dy[1])
model.set_rhs('dy', expr)
# model.set_expression('tvp', sp)
model.setup()
mpc = do_mpc.controller.MPC(model)
tstep = 0.1
thorizon = 1
nhorizon = int(thorizon/tstep)
setup_mpc = {
'n_horizon': nhorizon,
't_step': tstep,
'open_loop': 0,
'n_robust': 0,
# 'store_full_solution': True,
# 'state_discretization': 'collocation',
# 'collocation_type': 'radau',
# 'collocation_deg': 3,
# 'collocation_ni': 1,
'store_full_solution': True,
}
mpc.set_param(**setup_mpc)
mpc.set_param(nlpsol_opts = {'ipopt.linear_solver': 'MA27'})
# suppress printing
mpc.nlpsol_opts['ipopt.print_level'] = 0
mpc.nlpsol_opts['ipopt.sb'] = 'yes'
mpc.nlpsol_opts['print_time'] = 0
mpc.nlpsol_opts['ipopt.linear_solver'] = 'MA27'
# l_term = 10*cos(y1) + 0.1*y0**2 + 0.1*dy[0]**2 + 0.1*dy[1]**2 + 0.3*f**2 # step cost
# m_term = 10*cos(y1) + 0.1*y0**2 + 0.02*dy[0]**2 + 0.5*dy[1]**2 # terminal state cost
# # m_term = 0*y1
l_term = model.aux['E_kin'] - model.aux['E_pot'] + 500*(model.x['y_0'])**2
m_term = -model.aux['E_pot']+1000*(model.x['y_0'])**2 # stage cost
mpc.set_objective(lterm=l_term, mterm = m_term)
mpc.set_rterm(f=100)
mpc.set_nl_cons('y_0', y0**2, 0.3**2, soft_constraint=True)
# mpc.set_nl_cons('f', f**2, 0.5**2, soft_constraint=False)
# bounds on state:
mpc.bounds['lower','_x', 'y_0'] = -0.4
mpc.bounds['upper','_x', 'y_0'] = 0.4
# bounds on input:
mpc.bounds['lower','_u', 'f'] = -0.3
mpc.bounds['upper','_u', 'f'] = 0.3
# scaling
mpc.scaling['_x', 'y_0'] = 10
consts = [0.17, 0.8, 0.1, 0.00299, 25]
# 0.16043 0.7101 0.08698 0.00230 19.14743
mpc.set_uncertainty_values(
# l = np.array([0.17, 0.15, 0.2]),
# ma = np.array([1]),
# mb = np.array([0.1, 0.13]),
# ke = np.array([0.00299, 0.004, 0.002]),
# kf = np.array([20, 15, 25])
# l = np.array([0.16043]),
# ma = np.array([0.7101]),
# mb = np.array([0.08698]),
# ke = np.array([0.0023]),
# kf = np.array([19.14743])
l = np.array([consts[0]]),
ma = np.array([consts[1]]),
mb = np.array([consts[2]]),
ke = np.array([consts[3]]),
kf = np.array([consts[4]])
)
# 0.18319 0.77088 0.11271 0.00093 32.95541
# 0.18319 0.77088 0.11271 0.00093 32.95541
# template = mpc.get_tvp_template()
# template['_tvp',:, 'sp'] = 0
# def tvp_fun(t):
# return template
# mpc.set_tvp_fun(tvp_fun)
mpc.setup()
simulator = do_mpc.simulator.Simulator(model)
simulator.set_param(t_step = setup_mpc['t_step'])
p_template = simulator.get_p_template()
p_template['l'] = consts[0]
p_template['ma'] = consts[1]
p_template['mb'] = consts[2]
p_template['ke'] = consts[3]
p_template['kf'] = consts[4]
def p_fun(t_now):
return p_template
# return p_template
simulator.set_p_fun(p_fun)
tvp_template = simulator.get_tvp_template()
def tvp_fun_2(t):
# tvp_template['_tvp',:, 'sp'] = 0
return tvp_template
simulator.set_tvp_fun(tvp_fun_2)
simulator.setup()
# x0 = np.zeros((4, 1))
# simulator.x0 = x0
# mpc.x0 = x0
mpc.set_initial_guess()
mpc.compile_nlp(overwrite = False) #set overwrite to true if things changed
return mpc, simulator, model
null = open('/dev/null', 'w')
def get_power(t, y, mpc):
# start = perf_counter()
mpc.reset_history()
sys.stdout = null
print(y, mpc.make_step(y)[0, 0])
sys.stdout = sys.__stdout__
def mpc_loop(power, state, mpc):
# sleep(0.5)
while True:
# sleep(0.5)
start = perf_counter()
get_power(0, np.array(state), mpc)
time = int((perf_counter() - start)/mpc.t_step)
while perf_counter()-start < mpc.t_step: pass
ptr.value = 0
for i in range(time, mpc.n_horizon):
power[i-time] = mpc.data.prediction(('_u', 'f'))[0][-i]
print('recalculated!')
def write_to_pend_loop(power, state, ptr, t_step):
with Pendulum.Pendulum(file = '/dev/null') as p:
try:
p.set_mode('usb')
sleep(0.05)
p.set(0)
sleep(0.05)
except AttributeError:
pass
try:
while True:
state[0] = p.y[0]
state[1] = p.y[1]
state[2] = p.y[3]
state[3] = p.y[4]
# power = -get_power(0, np.array([p.y[0],p.y[1],p.y[3],p.y[4]]), mpc)
try:
u = float(power[ptr.value])
p.set(-u)
ptr.value += 1
print(u, ptr.value, *state, sep = '\t', file=sys.__stdout__)
except IndexError:
pass
sleep(0.95*t_step)
except KeyboardInterrupt:
p.set(0)
print('Stopping.')
sys.stdout = null
exit()
if __name__ == '__main__':
mpc, _, _ = get_mpc()
power = Array('d', mpc.n_horizon)
state = Array('d', 4)
ptr = Value('I', 0)
input('press enter to start balancing')
write_thread = Process(target = write_to_pend_loop, args = [power, state, ptr, mpc.t_step])
write_thread.start()
mpc.x0 = np.array(state[:])
mpc.set_initial_guess()
sleep(2)
mpc_loop(power, state, mpc)