New attributes lb_opt_x and ub_opt_x are now considering scaling vari…

…ables

do_mpc/controller.py

@@ -1081,32 +1081,32 @@ def _update_bounds(self):
         """
         if self.cons_check_colloc_points:   # Constraints for all collocation points.
             # Dont bound the initial state
-            self.lb_opt_x['_x', 1:self.n_horizon] = self._x_lb.cat/self._x_scaling
-            self.ub_opt_x['_x', 1:self.n_horizon] = self._x_ub.cat/self._x_scaling
+            self.lb_opt_x['_x', 1:self.n_horizon] = self._x_lb.cat
+            self.ub_opt_x['_x', 1:self.n_horizon] = self._x_ub.cat
 
             # Bounds for the algebraic variables:
-            self.lb_opt_x['_z'] = self._z_lb.cat/self._z_scaling
-            self.ub_opt_x['_z'] = self._z_ub.cat/self._z_scaling
+            self.lb_opt_x['_z'] = self._z_lb.cat
+            self.ub_opt_x['_z'] = self._z_ub.cat
 
             # Terminal bounds
-            self.lb_opt_x['_x', self.n_horizon, :, -1] = self._x_terminal_lb.cat/self._x_scaling
-            self.ub_opt_x['_x', self.n_horizon, :, -1] = self._x_terminal_ub.cat/self._x_scaling
+            self.lb_opt_x['_x', self.n_horizon, :, -1] = self._x_terminal_lb.cat
+            self.ub_opt_x['_x', self.n_horizon, :, -1] = self._x_terminal_ub.cat
         else:   # Constraints only at the beginning of the finite Element
             # Dont bound the initial state
-            self.lb_opt_x['_x', 1:self.n_horizon, :, -1] = self._x_lb.cat/self._x_scaling
-            self.ub_opt_x['_x', 1:self.n_horizon, :, -1] = self._x_ub.cat/self._x_scaling
+            self.lb_opt_x['_x', 1:self.n_horizon, :, -1] = self._x_lb.cat
+            self.ub_opt_x['_x', 1:self.n_horizon, :, -1] = self._x_ub.cat
 
             # Bounds for the algebraic variables:
-            self.lb_opt_x['_z', :, :, 0] = self._z_lb.cat/self._z_scaling
-            self.ub_opt_x['_z', :, : ,0] = self._z_ub.cat/self._z_scaling
+            self.lb_opt_x['_z', :, :, 0] = self._z_lb.cat
+            self.ub_opt_x['_z', :, : ,0] = self._z_ub.cat
 
             # Terminal bounds
-            self.lb_opt_x['_x', self.n_horizon, :, -1] = self._x_terminal_lb.cat/self._x_scaling
-            self.ub_opt_x['_x', self.n_horizon, :, -1] = self._x_terminal_ub.cat/self._x_scaling
+            self.lb_opt_x['_x', self.n_horizon, :, -1] = self._x_terminal_lb.cat
+            self.ub_opt_x['_x', self.n_horizon, :, -1] = self._x_terminal_ub.cat
 
         # Bounds for the inputs along the horizon
-        self.lb_opt_x['_u'] = self._u_lb.cat/self._u_scaling
-        self.ub_opt_x['_u'] = self._u_ub.cat/self._u_scaling
+        self.lb_opt_x['_u'] = self._u_lb.cat
+        self.ub_opt_x['_u'] = self._u_ub.cat
 
         # Bounds for the slack variables:
         self.lb_opt_x['_eps'] = self._eps_lb.cat

do_mpc/estimator.py

@@ -1149,35 +1149,40 @@ def make_step(self, y0):
 
     def _update_bounds(self):
         """Private method to update the bounds of the optimization variables based on the current values defined with :py:attr:`scaling`.
+
+        .. note::
+
+            Bounds are automatically scaled as they invoke the :py:attr:lb_opt_x` and :py:attr:`ub_opt_x` methods. Scaling is done automatically in these methods.
         """
+
         if self.cons_check_colloc_points:   # Constraints for all collocation points.
             # Bounds for the states on all discretize values along the horizon
-            self.lb_opt_x['_x'] = self._x_lb.cat/self._x_scaling
-            self.ub_opt_x['_x'] = self._x_ub.cat/self._x_scaling
+            self.lb_opt_x['_x'] = self._x_lb.cat
+            self.ub_opt_x['_x'] = self._x_ub.cat
 
             # Bounds for the algebraic states along the horizon
-            self.lb_opt_x['_z'] = self._z_lb.cat/self._z_scaling
-            self.ub_opt_x['_z'] = self._z_ub.cat/self._z_scaling
+            self.lb_opt_x['_z'] = self._z_lb.cat
+            self.ub_opt_x['_z'] = self._z_ub.cat
         else:   # Constraints only at the beginning of the finite Element
             # Bounds for the states on all discretize values along the horizon
-            self.lb_opt_x['_x', 1:self.n_horizon, -1] = self._x_lb.cat/self._x_scaling
-            self.ub_opt_x['_x', 1:self.n_horizon, -1] = self._x_ub.cat/self._x_scaling
+            self.lb_opt_x['_x', 1:self.n_horizon, -1] = self._x_lb.cat
+            self.ub_opt_x['_x', 1:self.n_horizon, -1] = self._x_ub.cat
 
             # Bounds for the algebraic states along the horizon
-            self.lb_opt_x['_z', :, 0] = self._z_lb.cat/self._z_scaling
-            self.ub_opt_x['_z', :, 0] = self._z_ub.cat/self._z_scaling
+            self.lb_opt_x['_z', :, 0] = self._z_lb.cat
+            self.ub_opt_x['_z', :, 0] = self._z_ub.cat
 
         # Bounds for the inputs along the horizon
-        self.lb_opt_x['_u'] = self._u_lb.cat/self._u_scaling
-        self.ub_opt_x['_u'] = self._u_ub.cat/self._u_scaling
+        self.lb_opt_x['_u'] = self._u_lb.cat
+        self.ub_opt_x['_u'] = self._u_ub.cat
 
         # Bounds for the slack variables along the horizon:
         self.lb_opt_x['_eps'] = self._eps_lb.cat
         self.ub_opt_x['_eps'] = self._eps_ub.cat
 
         # Bounds for the inputs along the horizon
-        self.lb_opt_x['_p_est'] = self._p_est_lb.cat/self._p_est_scaling
-        self.ub_opt_x['_p_est'] = self._p_est_ub.cat/self._p_est_scaling
+        self.lb_opt_x['_p_est'] = self._p_est_lb.cat
+        self.ub_opt_x['_p_est'] = self._p_est_ub.cat
 
     def _prepare_nlp(self):
         """Internal method. See detailed documentation in optimizer.prepare_nlp
@@ -1248,8 +1253,8 @@ def _prepare_nlp(self):
 
         self.n_opt_aux = opt_aux.shape[0]
 
-        self.lb_opt_x = opt_x(-np.inf)
-        self.ub_opt_x = opt_x(np.inf)
+        self._lb_opt_x = opt_x(-np.inf)
+        self._ub_opt_x = opt_x(np.inf)
 
         # Initialize objective function and constraints
         obj = DM(0)

do_mpc/optimizer.py

@@ -229,55 +229,61 @@ def nlp_cons_ub(self, val):
         assert self.flags['prepare_nlp'], 'Cannot query attribute prior to calling prepare_nlp or setup'
         self._nlp_cons_ub = val
 
-    @property
-    def lb_opt_x(self):
+    @IndexedProperty
+    def lb_opt_x(self, ind):
         """Query and modify the lower bounds of all optimization variables :py:attr:`opt_x`.
         This is a more advanced method of setting bounds on optimization variables of the MPC/MHE problem.
         Users with less experience are advised to use :py:attr:`bounds` instead.
 
         The attribute returns a nested structure that can be indexed using powerindexing. Please refer to :py:attr:`opt_x` for more details. 
 
-        .. warning::
+        .. note::
 
-            This is a VERY low level feature and should be used with extreme caution.
-            It is easy to break the code.
+            The attribute automatically considers the scaling variables when setting the bounds. See :py:attr:`scaling` for more details.
 
         .. note::
 
             Modifications must be done after calling :py:meth:`prepare_nlp` or :py:meth:`setup` respectively.
 
         """
-        return self._lb_opt_x
+        return self._lb_opt_x[ind] 
 
     @lb_opt_x.setter
-    def lb_opt_x(self, val):
-        # Cannot overwrite this property
-        raise Exception('Cannot overwrite lb_opt_x')
+    def lb_opt_x(self, ind, val):
+        self._lb_opt_x[ind] = val
+        # Get canonical index 
+        cind = self._lb_opt_x.f[ind]
+        # Modify the newly set values by considering the scaling variables. This requires the canonical index.
+        self._lb_opt_x.master[cind] = self._lb_opt_x.master[cind]/self.opt_x_scaling.master[cind]
+
 
-    @property
-    def ub_opt_x(self):
-        """Query and modify the uppper bounds of all optimization variables :py:attr:`opt_x`.
+
+    @IndexedProperty
+    def ub_opt_x(self, ind):
+        """Query and modify the lower bounds of all optimization variables :py:attr:`opt_x`.
         This is a more advanced method of setting bounds on optimization variables of the MPC/MHE problem.
         Users with less experience are advised to use :py:attr:`bounds` instead.
 
         The attribute returns a nested structure that can be indexed using powerindexing. Please refer to :py:attr:`opt_x` for more details. 
 
-        .. warning::
+        .. note::
 
-            This is a VERY low level feature and should be used with extreme caution.
-            It is easy to break the code.
+            The attribute automatically considers the scaling variables when setting the bounds. See :py:attr:`scaling` for more details.
 
         .. note::
 
             Modifications must be done after calling :py:meth:`prepare_nlp` or :py:meth:`setup` respectively.
 
         """
-        return self._ub_opt_x
+        return self._ub_opt_x[ind]
 
     @ub_opt_x.setter
-    def ub_opt_x(self, val):
-        # Cannot overwrite this property
-        raise Exception('Cannot overwrite ub_opt_x')
+    def ub_opt_x(self, ind, val):
+        self._ub_opt_x[ind] = val
+        # Get canonical index
+        cind = self._ub_opt_x.f[ind]
+        # Modify the newly set values by considering the scaling variables. This requires the canonical index.
+        self._ub_opt_x.master[cind] = self._ub_opt_x.master[cind]/self.opt_x_scaling.master[cind]
 
 
     @IndexedProperty
@@ -712,8 +718,8 @@ def solve(self):
 
         solver_call_kwargs = {
             'x0': self.opt_x_num,
-            'lbx': self.lb_opt_x,
-            'ubx': self.ub_opt_x,
+            'lbx': self._lb_opt_x,
+            'ubx': self._ub_opt_x,
             'lbg': self.nlp_cons_lb,
             'ubg': self.nlp_cons_ub,
             'p': self.opt_p_num,