diff --git a/ct_optcon/examples/NLOC.cpp b/ct_optcon/examples/NLOC.cpp
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+/*!
+ * \example NLOC.cpp
+ *
+ * This example shows how to use the nonlinear optimal control solvers iLQR, unconstrained Gauss-Newton-Multiple-Shooting (GNMS),
+ * as well as the hybrid methods iLQR-GNMS(M), where M denotes the number of multiple-shooting intervals. This example applies
+ * them to a simple second-order oscillator.
+ *
+ */
+#include <ct/optcon/optcon.h>
+#include "exampleDir.h"
+#include "plotResultsOscillator.h"
+
+using namespace ct::core;
+using namespace ct::optcon;
+
+
+int main(int argc, char **argv)
+{
+    /*get the state and control input dimension of the oscillator. Since we're dealing with a simple oscillator,
+	 the state and control dimensions will be state_dim = 2, and control_dim = 1. */
+    const size_t state_dim = ct::core::SecondOrderSystem::STATE_DIM;
+    const size_t control_dim = ct::core::SecondOrderSystem::CONTROL_DIM;
+
+
+    /* STEP 1: set up the Nonlinear Optimal Control Problem
+	 * First of all, we need to create instances of the system dynamics, the linearized system and the cost function. */
+
+    /* STEP 1-A: create a instance of the oscillator dynamics for the optimal control problem.
+	 * Please also compare the documentation of SecondOrderSystem.h */
+    double w_n = 0.1;
+    double zeta = 5.0;
+    std::shared_ptr<ct::core::ControlledSystem<state_dim, control_dim>> oscillatorDynamics(
+        new ct::core::SecondOrderSystem(w_n, zeta));
+
+
+    /* STEP 1-B: Although the first order derivatives of the oscillator are easy to derive, let's illustrate the use of the System Linearizer,
+	 * which performs numerical differentiation by the finite-difference method. The system linearizer simply takes the
+	 * the system dynamics as argument. Alternatively, you could implement your own first-order derivatives by overloading the class LinearSystem.h */
+    std::shared_ptr<ct::core::SystemLinearizer<state_dim, control_dim>> adLinearizer(
+        new ct::core::SystemLinearizer<state_dim, control_dim>(oscillatorDynamics));
+
+
+    /* STEP 1-C: create a cost function. We have pre-specified the cost-function weights for this problem in "nlocCost.info".
+	 * Here, we show how to create terms for intermediate and final cost and how to automatically load them from the configuration file.
+	 * The verbose option allows to print information about the loaded terms on the terminal. */
+    std::shared_ptr<ct::optcon::TermQuadratic<state_dim, control_dim>> intermediateCost(
+        new ct::optcon::TermQuadratic<state_dim, control_dim>());
+    std::shared_ptr<ct::optcon::TermQuadratic<state_dim, control_dim>> finalCost(
+        new ct::optcon::TermQuadratic<state_dim, control_dim>());
+    bool verbose = true;
+    intermediateCost->loadConfigFile(ct::optcon::exampleDir + "/nlocCost.info", "intermediateCost", verbose);
+    finalCost->loadConfigFile(ct::optcon::exampleDir + "/nlocCost.info", "finalCost", verbose);
+
+    // Since we are using quadratic cost function terms in this example, the first and second order derivatives are immediately known and we
+    // define the cost function to be an "Analytical Cost Function". Let's create the corresponding object and add the previously loaded
+    // intermediate and final term.
+    std::shared_ptr<CostFunctionQuadratic<state_dim, control_dim>> costFunction(
+        new CostFunctionAnalytical<state_dim, control_dim>());
+    costFunction->addIntermediateTerm(intermediateCost);
+    costFunction->addFinalTerm(finalCost);
+
+
+    /* STEP 1-D: initialization with initial state and desired time horizon */
+
+    StateVector<state_dim> x0;
+    x0.setRandom();  // in this example, we choose a random initial state x0
+
+    ct::core::Time timeHorizon = 3.0;  // and a final time horizon in [sec]
+
+
+    // STEP 1-E: create and initialize an "optimal control problem"
+    OptConProblem<state_dim, control_dim> optConProblem(
+        timeHorizon, x0, oscillatorDynamics, costFunction, adLinearizer);
+
+
+    /* STEP 2: set up a nonlinear optimal control solver. */
+
+    /* STEP 2-A: Create the settings.
+	 * the type of solver, and most parameters, like number of shooting intervals, etc.,
+	 * can be chosen using the following settings struct. Let's use, the iterative
+	 * linear quadratic regulator, iLQR, for this example. In the following, we
+	 * modify only a few settings, for more detail, check out the NLOptConSettings class. */
+    NLOptConSettings ilqr_settings;
+    ilqr_settings.dt = 0.01;  // the control discretization in [sec]
+    ilqr_settings.integrator = ct::core::IntegrationType::EULERCT;
+    ilqr_settings.discretization = NLOptConSettings::APPROXIMATION::FORWARD_EULER;
+    ilqr_settings.max_iterations = 10;
+    ilqr_settings.nThreads = 4;  // use multi-threading
+    ilqr_settings.nlocp_algorithm = NLOptConSettings::NLOCP_ALGORITHM::ILQR;
+    ilqr_settings.lqocp_solver =
+        NLOptConSettings::LQOCP_SOLVER::GNRICCATI_SOLVER;  // solve LQ-problems using custom Riccati solver
+    ilqr_settings.printSummary = true;
+    ilqr_settings.debugPrint = true;
+
+
+    /* STEP 2-B: provide an initial guess */
+    // calculate the number of time steps K
+    size_t K = ilqr_settings.computeK(timeHorizon);
+
+    /* design trivial initial controller for iLQR. Note that in this simple example,
+	 * we can simply use zero feedforward with zero feedback gains around the initial position.
+	 * In more complex examples, a more elaborate initial guess may be required.*/
+    FeedbackArray<state_dim, control_dim> u0_fb(K, FeedbackMatrix<state_dim, control_dim>::Zero());
+    ControlVectorArray<control_dim> u0_ff(K, ControlVector<control_dim>::Zero());
+    StateVectorArray<state_dim> x_ref_init(K + 1, x0);
+    NLOptConSolver<state_dim, control_dim>::Policy_t initController(x_ref_init, u0_ff, u0_fb, ilqr_settings.dt);
+
+
+    // STEP 2-C: create an NLOptConSolver instance
+    NLOptConSolver<state_dim, control_dim> iLQR(optConProblem, ilqr_settings);
+
+    // set the initial guess
+    iLQR.setInitialGuess(initController);
+
+
+    // STEP 3: solve the optimal control problem
+    iLQR.solve();
+
+
+    // STEP 4: retrieve the solution
+    ct::core::StateFeedbackController<state_dim, control_dim> solution = iLQR.getSolution();
+
+    // let's plot the output
+    plotResultsOscillator<state_dim, control_dim>(solution.x_ref(), solution.uff(), solution.time());
+}