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RoboRally
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general refactoring

simple_control
Simon Pirkelmann 2020-10-24 21:11:22 +02:00
parent 7ded3bee79
commit a0f701fe83
1 changed files with 30 additions and 30 deletions
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22
remote_control/mpc_controller.py
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@ -3,6 +3,7 @@ import time
from casadi_opt import OpenLoopSolver
class MPCController:
def __init__(self, estimator):
self.t = time.time()
@ -37,7 +38,7 @@ class MPCController:
self.controlling = False
robot.send_cmd()
elif key == 48: # 0
target_pos = np.array([0.0,0.0,0.0])
target_pos = np.array([0.0, 0.0, 0.0])
elif key == 43: # +
self.control_scaling += 0.1
self.control_scaling = min(self.control_scaling, 1.0)
@ -63,25 +64,24 @@ class MPCController:
error_pos = np.linalg.norm(x_pred[0:2] - target_pos[0:2])
angles_unwrapped = np.unwrap([x_pred[2], target_pos[2]]) # unwrap angle to avoid jump in data
error_ang = np.abs(angles_unwrapped[0] - angles_unwrapped[1])
#print("error pos = ", error_pos)
#print("error_pos = {}, error_ang = {}".format(error_pos, error_ang))
# print("error pos = ", error_pos)
# print("error_pos = {}, error_ang = {}".format(error_pos, error_ang))
#if error_pos > 0.075 or error_ang > 0.35:
# if error_pos > 0.075 or error_ang > 0.35:
if error_pos > 0.05 or error_ang > 0.1:
# solve mpc open loop problem
res = self.ols.solve(x_pred, target_pos)
#us1 = res[0]
#us2 = res[1]
# us1 = res[0]
# us2 = res[1]
us1 = res[0] * self.control_scaling
us2 = res[1] * self.control_scaling
#print("u = {}", (us1, us2))
# print("u = {}", (us1, us2))
tmpc_end = time.time()
#print("---------------- mpc solution took {} seconds".format(tmpc_end - tmpc_start))
# print("---------------- mpc solution took {} seconds".format(tmpc_end - tmpc_start))
dt_mpc = time.time() - self.t
if dt_mpc < self.dt: # wait until next control can be applied
#print("sleeping for {} seconds...".format(self.dt - dt_mpc))
# print("sleeping for {} seconds...".format(self.dt - dt_mpc))
time.sleep(self.dt - dt_mpc)
else:
us1 = [0] * self.mstep
@ -150,7 +150,7 @@ class MPCController:
# measure state
x_pred = self.get_measurement(robot.id)
#print(np.linalg.norm(x_pred-target_pos))
# print(np.linalg.norm(x_pred-target_pos))
# solve mpc open loop problem
res = self.ols.solve(x_pred, target_pos)