x_start = np.array([-3.0, 0.0]) x_goal = np.array([0.0, 0.0]) ########################### # Create objects ########################### Robot = Manipulator.OneLinkManipulator() PD = linear.PD(kp=5, kd=2) PID = linear.PID(kp=5, kd=2, ki=4) CTC = ComputedTorque.ComputedTorqueController(Robot) SLD = ComputedTorque.SlidingModeController(Robot) RRT = RandomTree.RRT(Robot, x_start) VI = DPO.ValueIteration1DOF(Robot, 'quadratic') ############################ # Params ############################ tmax = 8 # max motor torque Robot.u_ub = np.array([tmax]) # Control Upper Bounds Robot.u_lb = np.array([-tmax]) # Control Lower Bounds RRT.x_start = x_start RRT.discretizeactions(3) RRT.dt = 0.1 RRT.goal_radius = 0.3 RRT.max_nodes = 5000 RRT.max_solution_time = 5 RRT.dyna_plot = True
@author: alex """ from AlexRobotics.dynamic import Manipulator as M from AlexRobotics.control import DPO as DPO import matplotlib.pyplot as plt """ Define system """ # Define dynamic system R = M.OneLinkManipulator() # Define controller cost_function = 'quadratic' ValueIterationAlgo = DPO.ValueIteration1DOF( R , cost_function ) path = 'data/' ValueIterationAlgo.first_step() ValueIterationAlgo.load_data( path + 'R1' + cost_function ) #ValueIterationAlgo.compute_steps(200) ValueIterationAlgo.compute_steps(1) ValueIterationAlgo.save_data( path + 'R1' + cost_function ) # Plot Value Iteration Results ValueIterationAlgo.plot_raw() ValueIterationAlgo.plot_J_nice( 2 ) # Assign Controller ValueIterationAlgo.assign_interpol_controller()
from AlexRobotics.dynamic import simple1DOF as RD1 from AlexRobotics.control import DPO as DPO import matplotlib.pyplot as plt """ Define system """ # Define dynamic system pendulum = RD1.Pendulum() m = 0.5 l = 1 g = 9.8 pendulum.setparams(m, g, l) # Define controller cost_function = 'quadratic' ValueIterationAlgo = DPO.ValueIteration1DOF(pendulum, cost_function) path = 'data/' ValueIterationAlgo.first_step() ValueIterationAlgo.load_data(path + cost_function + 'pendulum') ValueIterationAlgo.compute_steps(1) ValueIterationAlgo.save_data(path + cost_function + 'pendulum') # Plot Value Iteration Results ValueIterationAlgo.plot_raw() # Assign Controller ValueIterationAlgo.assign_interpol_controller() """ Simulation and plotting """
@author: alex """ from AlexRobotics.dynamic import Manipulator as M from AlexRobotics.control import DPO as DPO import numpy as np import matplotlib.pyplot as plt # Define dynamic system R = M.OneLinkManipulator() # Define controller cost_function = 'quadratic' QLearningAlgo = DPO.QLearning1DOF(R, cost_function) path = 'data/' name = 'R1_Qlearning' + cost_function #n_steps = 10000 n_steps = 1 QLearningAlgo.first_step() QLearningAlgo.load_data(name) x0 = np.array([3, 0]) QLearningAlgo.x0 = x0 QLearningAlgo.training(n_steps, True, True)
CTC_controller = RminCTC.RminComputedTorqueController(R) CTC_controller.load_trajectory(RRT.solution) CTC_controller.w0 = 1.0 CTC_controller.zeta = 0.7 CTC_controller.n_gears = 2 CTC_controller.traj_ref_pts = 'interpol' CTC_controller.hysteresis = True CTC_controller.hys_level = 1.0 CTC_controller.min_delay = 0.1 """ Value Iteration """ # Define controller cost_function = 'quadratic' ValueIterationAlgo = DPO.ValueIteration_hybrid_1DOF(R, cost_function) ValueIterationAlgo.first_step() ValueIterationAlgo.load_data(path + 'R1H' + cost_function) #ValueIterationAlgo.compute_steps(50) ValueIterationAlgo.compute_steps(1) ValueIterationAlgo.save_data(path + 'R1H' + cost_function) #ValueIterationAlgo.plot_raw_nice( 2 ) """ RRT Simulation """ R.ctl = CTC_controller.ctl tf = RRT.time_to_goal + 5 n = int(10 / 0.001) + 1 R.computeSim(x_start, tf, n, solver='euler')
