Non-linear model predictive control (NMPC) with differential dynamic drogramming (DDP)
TestDDPCartPole-WithControlLimits-20220729.mp4
- C++ header-only library
- Treats state and control input dimensions as template parameters
- Supports time-varying control input dimensions
- Supports constrained control input
See here.
See the following for a detailed algorithm.
- Y Tassa, T Erez, E Todorov. Synthesis and stabilization of complex behaviors through online trajectory optimization. IROS, 2012.
- Y Tassa, N Mansard, E Todorov. Control-limited differential dynamic programming. ICRA, 2014.
The source code implementation is based on the following.
Make sure that it is built with --catkin-make-args tests option.
Controlling on CoM-ZMP dynamics with time-variant CoM height. System is linear and time-variant.
$ rosrun nmpc_ddp TestDDPBipedal
$ rosrun nmpc_ddp plotTestDDPBipedal.py
Controlling vertical motion with time-variant number of contacts (including floating phase). System is linear and time-variant. The dimension of the control input changes (there are even time steps with an empty control input). The upper and lower limits are imposed on the control input.
$ rosrun nmpc_ddp TestDDPVerticalMotion --gtest_filter=*.WithConstraint
$ # rosrun nmpc_ddp TestDDPVerticalMotion --gtest_filter=*.WithoutConstraint # Try the unconstrained case
$ rosrun nmpc_ddp plotTestDDPVerticalMotion.py
Controlling cart-pole (also known as inverted pendulum). System is non-linear. This is an example of a realistic setup where the control and simulation have different time periods.
# 10-second simulation
$ rostest nmpc_ddp TestDDPCartPole.test --text
# Endless simulation
$ rostest nmpc_ddp TestDDPCartPole.test no_exit:=true --text
You can interactively add disturbances and change the target position via the GUI. See the video.
$ rosrun nmpc_ddp TestDDPCentroidalMotion