C++基于事件的在线自适应快速非线性模型预测控制框架+项目源码+文档说明+代码注释

#DENMPC @file README.md @Author Jan Dentler (jan.dentler@uni.lu) University of Luxembourg @date 26.October, 2017 @time 16:14h @license GPLv3 @brief README ## Outline DENMPC is providing an object-oriented real-time nonlinear model predictive control (NMPC) framework which has been developed at the Automation & Robotics Research Group http://wwwde.uni.lu/snt/research/automation_robotics_research_group at the University of Luxembourg. The basic idea of DENMPC is to provide a fast nonlinear MPC that can adjust at runtime to different systems. This refers to: * Multi-agent systems that can change tasks, objectives and topology * Fault-tolerant control, where the controller has to adapt to different system conditions * Control prototyping, where you want to explore different scenarios without creating the underlying Optimal Control Problem (OCP) from scratch In order to do so, DENMPC features an object-oriented modularization approach. This allows structuring the control scenario into agents, constraints and couplings. Out of these single components, DENMPC is dynamically creating the OCP at runtime. As a result, agents, constraints and couplings can be added, removed, and parameters can be changed at runtime. This addition, respectively subtraction is triggered by events which can be for example timer events, ROS-messages events, etc. For very complex tasks, this can further be used to combine step chains with DENMPC, to specialize the MPC for each task stage individually. ##Literature and Publication DENMPC is open-source software, available under available under [https://github.com/snt-robotics/denmpc](https://github.com/snt-robotics/denmpc) and [https://github.com/DentOpt/denmpc](https://github.com/DentOpt/denmpc). The usage of DENMPC use regulated under the terms of the GPL3 license (Proprietary licences are available under request.). If you are using the software in your research work, you are supposed to cite one or more of the following references: J. Dentler, "Real-time Model Predictive Control of Cooperative Aerial Manipulation", [http://orbilu.uni.lu/handle/10993/36965](http://orbilu.uni.lu/handle/10993/36965), PhD Thesis, University of Luxembourg, July 2018 Jan Dentler, Somasundar Kannan, Souad Bezzaoucha, Miguel Angel Olivares-Mendez, and Holger Voos, Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints. Autonomous Robots, March 2018, pages 1–26. doi: 10.1007/s10514-018-9711-z, url: https://doi.org/10.1007/s10514-018-9711-z J. Dentler, S. Kannan, M. A. O. Mendez and H. Voos, "A modularization approach for nonlinear model predictive control of distributed fast systems", 24th Mediterranean Conference on Control and Automation (MED), Athens, Greece, 2016, pp. 292-297. doi: 10.1109/MED.2016.7535973 Jan Dentler and Somasundar Kannan and Miguel Angel Olivares Mendez and Holger Voos, "A real-time model predictive position control with collision avoidance for commercial low-cost quadrotors", Proceedings of 2016 IEEE Multi-Conference on Systems and Control (MSC 2016), Argentina, Buenos Aires, 2016 If you are using the "Condensed Multiple Shooting Generalized Minimal Residuum Method (CMSCGMRES)" kernel contributed by the team of Prof. Dr. Toshiyuki OHTSUKA, please refer to: Ohtsuka, T., “A Continuation/GMRES Method for Fast Computation of Nonlinear Receding Horizon Control,” Automatica, Vol. 40, No. 4, Apr. 2004, pp. 563-574. Seguchi, H., and Ohtsuka, T., “Nonlinear Receding Horizon Control of an Underactuated Hovercraft,” International Journal of Robust and Nonlinear Control, Vol. 13, Nos. 3-4, Mar.-Apr. 2003, pp. 381-398. ## DENMPC features: Nonlinear model predictive control (e.g. a quadrotor with nonlinear system dynamics) Central control of single-agent systems (e.g. a single robot) Central control of multi-agent systems (e.g. multiple robots that are interacting) Object-oriented code to easily adapt it: Controller: Interface class for implementations of controllers, e.g.CMSCGMRES Agent: Interface class for implementations of agents, respective system or robot types, e.g. Quadrotor Constraint: Interface class for implementations of single-agent constraints Coupling: Interface class for implementations for coupling agents Open-source code ## Installation # Navigate to your ROS catkin workspace (e.g. catkin_ws):` cd catkin_ws/src #Clone repository git clone https://github.com/DentOpt/denmpc.git cd .. #Build package catkin_make ### To use the AR.Drone 2.0 scenario with the tum simulator Install: cd catkin_ws/src git clone https://github.com/DentOpt/ardrone_simulator_gazebo7.git cd .. catkin_make To run the AR.Drone 2.0 scenario in gazebo, run roslaunch cvg_sim\_gazebo ardrone_testworld.launch Launch drone (Takeoff) from commandline: rostopic pub -1 /ardrone/takeoff std_msgs/Empty The AR.Drone 2.0 simulator is configured to subscribe control commands under the topic "/cmd_vel". The AR.Drone 2.0 pose is published under "/pose" To control the AR.Drone 2.0 in with denmpc: either to track center of UAV: rosrun denmpc scenario_ardrone_pose_tracking_node or to track with sensor constraint: rosrun denmpc scenario_ardrone_sensor_tracking_node and to send desired pose use rqt or commandline, e.g rostopic pub /desiredpose geometry_msgs/PoseStamd '{header: {stamp: now, frame_id: "map"}, pose: {position: {x: 0.0, y: 0.0, z: 2.0}, orientation: {x: 0.0, y: 0.0, z: 0.0, w: 1.0}}}' ### To use the Turtlebot scenario Install: cd catkin_ws/src sudo apt-get install git clone https://github.com/ros/ros_tutorials.git #Install Turtlesim git clone https://github.com/DentOpt/denmpc.git -b tutorial_turtlesim #Install DENMPC branch cd .. catkin_make Run: roscore #run roscore rosrun turtlesim turtlesim_node #Run Turtlesim in separate tab rosrun denmpc scenario_scenario_node #Run denmpc in separate tab That's it! You will see how the turtle DENMPC moves from its initial position to the position x=1 y=1. You can give any desired position by publishing it to the /turtle1/desiredpose. For example, for the new target x=5, y=5 type `rostopic pub /turtle1/desiredpose turtlesim/Pose "{x: 5.0, y: 5.0, theta: 0.0, linear_velocity: 0.0, angular_velocity: 0.0}"`