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\page optcon_tut_mpc NLOC-MPC of a Damped Oscillator
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\page optcon_tut_mpc Model Predictive Control for a Damped Oscillator
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In this example we show how to use a Nonlinear Optimal Control solver (here iLQG) in combination with the MPC-class for out-of-the box Model Predictive Control.
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\endcode
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\page optcon_tut_nlp Simple Examples for Nonlinear Programming
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\note these examples show how to set up a very simple nonlinear program and solve it using IPOPT.
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\include nlp/Nlp2D.cpp
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You can run this example with the following command
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\code{.sh}
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rosrun ct_optcon ex_Nlp_2D
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\endcode
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\include nlp/Nlp3D.cpp
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You can run this example with the following command
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\code{.sh}
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rosrun ct_optcon ex_Nlp_3D
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\endcode
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\page optcon_tut_ekf Extended Kalman Filtering
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This example demonstrates how to set up an Extended Kalman Filter. We simulate a system, add noise to the observations and then
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compute state estimates using the EKF class.
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\include KalmanFiltering.cpp
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You can run this example with the following command
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\code{.sh}
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rosrun ct_optcon ex_KalmanFiltering
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\endcode
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\page optcon_tut_disturbance_ekf Extended Kalman Filter as Disturbance Observer
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Here, we use an Extended Kalman Filter to observer an input-disturbance to a simple example system.
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We simulate a system, subjet to noise and a time-varying disturbance, add noise to the observations and then
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compute state and disturbance estimates using an EKF.
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\include KalmanDisturbanceFiltering.cpp
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You can run this example with the following command
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