Nov 26, 2022 · Nov 24, 2022
diff --git a/control/iosys.py b/control/iosys.py
            dx = sys._rhs(t, x, u) - dx0
            if dtime:
                dx -= x           # TODO: check
            dy = sys._out(t, x, u) - y0

            # Map the results into the constrained variables
            return np.concatenate((dx[deriv_vars], dy[output_vars]), axis=0)
            # If no y0 is given, don't evaluate the output function
            if y0 is None:
                return dx[deriv_vars]
            else:
                dy = sys._out(t, x, u) - y0

                # Map the results into the constrained variables
                return np.concatenate((dx[deriv_vars], dy[output_vars]), axis=0)

        # Set the initial condition for the root finding algorithm
        z0 = np.concatenate((x[state_vars], u[input_vars]), axis=0)
diff --git a/control/tests/iosys_test.py b/control/tests/iosys_test.py
        np.testing.assert_array_almost_equal(
            nlsys_full._rhs(0, xeq, ueq)[-4:], np.zeros((4,)), decimal=5)

        # The same test as previous, but now all constraints are in the state vector
        nlsys_full = ios.NonlinearIOSystem(pvtol_full, None)
        xeq, ueq, result = ios.find_eqpt(
            nlsys_full, [0, 0, 0.1, 0.1, 0, 0], [0.01, 4*9.8],
            idx=[2, 3, 4, 5], ix=[0, 1, 2, 3], return_result=True)
        assert result.success
        np.testing.assert_array_almost_equal(
            nlsys_full._out(0, xeq, ueq)[[2, 3]], [0.1, 0.1], decimal=5)
        np.testing.assert_array_almost_equal(
            nlsys_full._rhs(0, xeq, ueq)[-4:], np.zeros((4,)), decimal=5)

        # Fix one input and vary the other
        nlsys_full = ios.NonlinearIOSystem(pvtol_full, None)
        xeq, ueq, result = ios.find_eqpt(
Original file line number	Diff line number	Diff line change
Expand Up		@@ -2138,10 +2138,15 @@ def rootfun(z):
		dx = sys._rhs(t, x, u) - dx0
		if dtime:
		dx -= x # TODO: check
		dy = sys._out(t, x, u) - y0

		# Map the results into the constrained variables
		return np.concatenate((dx[deriv_vars], dy[output_vars]), axis=0)
		# If no y0 is given, don't evaluate the output function
		if y0 is None:
		return dx[deriv_vars]
		else:
		dy = sys._out(t, x, u) - y0

		# Map the results into the constrained variables
		return np.concatenate((dx[deriv_vars], dy[output_vars]), axis=0)

		# Set the initial condition for the root finding algorithm
		z0 = np.concatenate((x[state_vars], u[input_vars]), axis=0)
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Original file line number	Diff line number	Diff line change
Expand Up		@@ -822,6 +822,17 @@ def test_find_eqpts(self, tsys):
		np.testing.assert_array_almost_equal(
		nlsys_full._rhs(0, xeq, ueq)[-4:], np.zeros((4,)), decimal=5)

		# The same test as previous, but now all constraints are in the state vector
		nlsys_full = ios.NonlinearIOSystem(pvtol_full, None)
		xeq, ueq, result = ios.find_eqpt(
		nlsys_full, [0, 0, 0.1, 0.1, 0, 0], [0.01, 4*9.8],
		idx=[2, 3, 4, 5], ix=[0, 1, 2, 3], return_result=True)
		assert result.success
		np.testing.assert_array_almost_equal(
		nlsys_full._out(0, xeq, ueq)[[2, 3]], [0.1, 0.1], decimal=5)
		np.testing.assert_array_almost_equal(
		nlsys_full._rhs(0, xeq, ueq)[-4:], np.zeros((4,)), decimal=5)

		# Fix one input and vary the other
		nlsys_full = ios.NonlinearIOSystem(pvtol_full, None)
		xeq, ueq, result = ios.find_eqpt(
Expand Down