Adaptive scheme issue with 2 phase schwartz problem

[sandeep026]

With different segments and polynomial orders, the adaptive method is struggling. How to avoid this issue?

from mpopt import mp

ocp = mp.OCP(n_states=2, n_controls=1, n_phases=2)

# Step-1 : Define dynamics
def dynamics0(x, u, t):
    return [x[1], u[0] - 0.1 * (1.0 + 2.0 * x[0] * x[0]) * x[1]]


ocp.dynamics = [dynamics0, dynamics0]

# Step-2: Add path constraints
def path_constraints0(x, u, t):
    return [
        1.0 - 9.0 * (x[0] - 1) * (x[0] - 1) - (x[1] - 0.4) * (x[1] - 0.4) / (0.3 * 0.3)
    ]


ocp.path_constraints[0] = path_constraints0

# Step-3: Add terminal cost
def terminal_cost1(xf, tf, x0, t0):
    return 5 * (xf[0] * xf[0] + xf[1] * xf[1])


ocp.terminal_costs[1] = terminal_cost1

ocp.x00[0] = [1, 1]
ocp.x00[1] = [1, 1]
ocp.xf0[0] = [1, 1]
ocp.xf0[1] = [0, 0]

ocp.lbx[0][1] = -0.8
ocp.lbu[0], ocp.ubu[0] = -1, 1
ocp.lbt0[0], ocp.ubt0[0] = 0, 0
ocp.lbtf[0], ocp.ubtf[0] = 1, 1
ocp.lbtf[1], ocp.ubtf[1] = 2.9, 2.9
ocp.du_continuity[0] = 1
ocp.validate()
#mpo = mp.mpopt_h_adaptive(ocp, 20, 10,scheme='CGL')
#max_iter = 50
#solution = mpo.solve(max_iter=max_iter, mpopt_options={'method':'control_slope'})
#post = mpo.process_results(solution, plot=True)
#mpo = mp.mpopt_h_adaptive(ocp, 10, 15,scheme='LGL') # Use ctrl + tab for help, inside ()
#mpo.plot_residual_evolution = True
#mpo.tol_residual[0] = 1e-4
#max_iter = 10
#solution = mpo.solve(max_iter=max_iter, mpopt_options={'method':'residual', 'sub_method':'merge_split'})
#post = mpo.process_results(solution, plot=True)
mpo = mp.mpopt_adaptive(ocp, 10, [5]*10,scheme="LGR") # Use ctrl + tab for help, inside ()
mpo.mid_residuals=True
solution = mpo.solve()
post = mpo.process_results(solution, plot=True)
#mpo = mp.mpopt_h_adaptive(ocp, 10, 4) # Use ctrl + tab for help, inside ()
#mpo.plot_residual_evolution = True
#max_iter = 2
#solution = mpo.solve(max_iter=max_iter, mpopt_options={'method':'residual', 'sub_method':'equal_area'})
#post = mpo.process_results(solution, plot=True)