T_max_Single = 7607000/9g = 9.81 m = 114239.00k = 18.74I = 16071705.59import numpy as npimport mathimport control as ctimport control.optimal as optimport matplotlib.pyplot as pltimport loggingimport timeimport osplt.style.use("default")from IPython.display import displayfrom colorama import *import sympy as sfrom scipy.integrate import odeint#Definition of the statesdef rocket_update(t, x, u, params):            xpos = x[0]     U = x[1]     z = x[2]     W = x[3]     θ = x[4]     q = x[5]         T = u[0]    a = u[1]    # Return the derivative of the state    dydt = np.array([U,            T*np.cos(θ-a)/m,             W,            T*np.sin(θ-a)/m-g,            q,            T*k*np.sin(a)/I])        return(dydt)def rocket_output(t, x, u, params):    return x                            # return (full state)Tf = 8# Define the rocket movement dynamics as an input/output systemrocket = ct.NonlinearIOSystem(    rocket_update, rocket_output, states=6, name='rocket',    inputs=("T", "α"), outputs=("x", "U", "z", "W", "θ", "q")) # Define the time horizon (and spacing) for the optimizationhorizon = np.linspace(0, Tf, Tf, endpoint=True)                                        #############IMPORTANT# Provide an intial guess (will be extended to entire horizon)bend_left = [T_max_Single, 0.01]          # slight left veer                     #############IMPORTANT# Optimal Control ProblemInitial_x = 0 #mFinal_x =  5 #mInitial_z =  0 #mFinal_z = 200 #mInitial_θ = np.pi/2 #mFinal_θ = np.pi/3 #mFinal_Thrust = T_max_Singlex0 = [Initial_x, 0, Initial_z, 0, Initial_θ, 0]; u0 = [bend_left[0], bend_left[1]]xf = [Final_x, 0, Final_z, 0, Final_θ, 0]; uf = [Final_Thrust, 0]#T_max_SingleMin_T = 1 * T_max_SingleMax_T = 9 * T_max_Singlea_min = -20* np.pi/180a_max = 20* np.pi/180# Approach 3: terminal constraints## We can also remove the cost function on the state and replace it# with a terminal *constraint* on the state.  If a solution is found,# it guarantees we get to exactly the final state.#print("Initialisation of optimisation")    # print("Checkpoint_1")# Input cost and terminal constraintsR = np.diag([0, 0])                 # minimize applied inputscost3 = opt.quadratic_cost(rocket, np.zeros((6,6)), R, u0=uf)con1 = [ opt.input_range_constraint(rocket, [Min_T, a_min], [Max_T, a_max]) ]terminal = [ opt.state_range_constraint(rocket, [Final_x, -np.inf, Final_z, -np.inf, -np.inf, -np.inf],                                                [Final_x, np.inf, Final_z, np.inf, np.inf, np.inf]) ]# Reset logging to its default valueslogging.basicConfig(    level=logging.DEBUG, filename="./steering-terminal_constraint.log",    filemode='w', force=True)# Compute the optimal controlstart_time = time.process_time()result3 = opt.solve_ocp(    rocket, horizon, x0, cost3, con1,              terminal_constraints=terminal, initial_guess=bend_left, log=False, method="trust-constr",    options={'eps': 0.01})print("* Total time = %5g seconds\n" % (time.process_time() - start_time))

/opt/anaconda3/lib/python3.8/site-packages/scipy/optimize/_constraints.py:386: OptimizeWarning: At least one constraint is unbounded above and below. Such constraints are ignored.  warn("At least one constraint is unbounded above and below. Such "/opt/anaconda3/lib/python3.8/site-packages/scipy/optimize/_constraints.py:432: OptimizeWarning: Equality and inequality constraints are specified in the same element of the constraint list. For efficient use with this method, equality and inequality constraints should be specified in separate elements of the constraint list.   warn("Equality and inequality constraints are specified in the same "