how to solve vector-based variable/constraints in python pyomo - optimization

Question

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I am solving this problem, i write code. but getting error at last line ' results'
please anyone know how to deal vector based dynamic optimization please let me know.
model = ConcreteModel()
model.num_itter = Param(initialize = 0)
global p_
p_ = model.num_itter
# print(value(p_))
torq = np.zeros((14,6))
# global p_
def M_func(model,i):
return np.array(Mqn( *value(model.q[i]) ))
def sai_func(model, i):
return np.array(sain( *value(model.q[i]) ))
def sai_func_T(model, i):
return np.array(sain( *value(model.q[i]) )).T
def c_func(model, i):
return np.array(cqn(*np.append(value(model.q[0]),value(model.u[0])) ))
def dsai_dt_func(model, i):
return np.array(saidn(*np.append(value(model.q[0]),value(model.u[0])) ))
def first_eq(model,i):
return value(model.M[i])#value(model.q[i])-\
value(model.sai[i]).T#value(model.lm[i])+\
torq#value(model.tu[i])
def second_eq(model,i):
return value(model.sai[i])#value(model.a[i])
def third_eq(model,i):
return (-value(model.dsai_dt[i])#value(model.u[i])).reshape(10,1)
def solu_f(model,i):
global p_
s_ = solu10[int(p_/2),:]
# time.sleep(2)
p_= p_+1
print('p',p_)
return s_
def init_q(model,i):
return solu10[i,:]
def init_u(model,i):
return np.zeros((14,))
def init_tu(model,i):
return np.zeros((6,))
def init_lm(model,i):
return np.zeros((10,))
def init_a(model,i):
return np.zeros((14,))
# Define the time horizon
t0 =0 ;tf =40
model.t = ContinuousSet(bounds=(t0,tf))
# measurements = {_: solu10[_,:] for _ in range(len(solu10))}
# Define the state variables, control inputs, and other variables
model.time_points = Set(initialize=range(200))
model.q = Var(model.t,initialize=init_q)
model.u = Var(model.t,initialize=init_u)
model.a = Var(model.t,initialize=init_a)
model.tu =Var(model.t,initialize=init_tu)
model.lm = Var(model.t,initialize=init_lm)
model.dqdt = DerivativeVar(model.q,wrt=model.t)
model.dudt = DerivativeVar(model.u,wrt=model.t)
# Define the parameters
model.M = Param(model.t,mutable=True, initialize=M_func,within=Any)
model.sai = Param(model.t,mutable=True, initialize=sai_func,within=Any)
model.c = Param(model.t,mutable=True, initialize=c_func,within=Any)
model.dsai_dt = Param(model.t,mutable=True, initialize=dsai_dt_func,within=Any)
model.first_meq = Param(model.t,mutable =True, initialize = first_eq,within = Any)
model.solu = Param(model.t,mutable = True,initialize = solu_f, within =Any)
model.second_meq = Param(model.t,mutable =True, initialize = second_eq,within = Any)
model.third_meq = Param(model.t,mutable =True, initialize = third_eq,within = Any)
# print(value(p_))
# Define the Constraint equations
def dyn_eq_1(model,i):
return model.first_meq[i]==-model.c[i]
def dyn_eq_2(model,i):
return model.second_meq[i]==model.third_meq[i]
def vel_constraint(model,i):
return model.dqdt[i]==model.u[i]
def acc_constraint(model,i):
return model.dudt[i]==model.a[i]
def path_constraint(model, i):
return model.q[i] == model.solu[i]
model.dyn_eq_1s= Constraint(model.t, rule=dyn_eq_1)
model.dyn_eq_2s= Constraint(model.t, rule=dyn_eq_2)
model.vel_constraints = Constraint(model.t, rule= vel_constraint)
model.acc_constraints = Constraint(model.t, rule= acc_constraint)
model.path_constraints = Constraint(model.t, rule=path_constraint)
# Define the bounds
u_lb = -np.array([2,2,0.5,0.34,float('inf'),float('inf'),0.20,28,float('inf'),float('inf'),0.20,28,0.25,0.25])
u_ub = [2,2,0.5,0.34,None,None,0.20,28,None,None,0.20,28,0.25,0.25]
a_lb = -np.array([1.5,1.5,0.2,0.10,float('inf'),float('inf'),0.10,5,float('inf'),float('inf'),0.10,5,0.10,0.10])
a_ub = [1.5,1.5,0.2,0.10,None,None,0.10,5,None,None,0.10,5,0.10,0.10]
tu_lb = -np.array([4,0.5,4,0.5,4,4])
tu_ub = [4,4,4,4,4,4]
model.u.setlb(u_lb)
model.u.setub(u_ub)
model.a.setlb(a_lb)
model.a.setub(a_ub)
model.tu.setlb(tu_lb)
model.tu.setub(tu_ub)
def u_nes_fn(model,i):
u_nes = np.array([])
for _ in [6,7,10,11,12,13]:
u_nes = np.append(u_nes,value(model.u[i])[_])
return u_nes.astype('float64')
model.u_nes = Param(model.t,initialize= u_nes_fn,within = Any)
# Define the objective function
def tu_u_(model,i):
return np.linalg.norm(value( model.u_nes[i]*model.tu[i]*model.u_nes[i]*model.tu[i]))
model.tu_u = Integral(model.t,wrt=model.t,rule = tu_u_)
def objfun(model):
return model.tu_u
model.obj = Objective(rule = objfun)
# Define the solver and solve the problem
solver = SolverFactory('ipopt')
results = solver.solve(model)
ValueError Traceback (most recent call last)
c:\Users\pyomo_opti.ipynb Cell 16 in <cell line: 11>()
9 # Define the solver and solve the problem
10 solver = SolverFactory('ipopt')
---> 11 results = solver.solve(model)
File c:\Users\AppData\Local\Programs\Python\Python310\lib\site-packages\pyomo\opt\base\solvers.py:570, in OptSolver.solve(self, *args, **kwds)
565 try:
566
...
File pyomo\repn\plugins\ampl\ampl_.pyx:410, in pyomo.repn.plugins.ampl.ampl_.ProblemWriter_nl.call()
File pyomo\repn\plugins\ampl\ampl_.pyx:1072, in pyomo.repn.plugins.ampl.ampl_.ProblemWriter_nl._print_model_NL()
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()