1,301 research outputs found
Deep Reinforcement Learning for Event-Triggered Control
Event-triggered control (ETC) methods can achieve high-performance control
with a significantly lower number of samples compared to usual, time-triggered
methods. These frameworks are often based on a mathematical model of the system
and specific designs of controller and event trigger. In this paper, we show
how deep reinforcement learning (DRL) algorithms can be leveraged to
simultaneously learn control and communication behavior from scratch, and
present a DRL approach that is particularly suitable for ETC. To our knowledge,
this is the first work to apply DRL to ETC. We validate the approach on
multiple control tasks and compare it to model-based event-triggering
frameworks. In particular, we demonstrate that it can, other than many
model-based ETC designs, be straightforwardly applied to nonlinear systems
Natural ZMP trajectories for biped robot reference generation
The control of a biped humanoid is a challenging
task due to the hard-to-stabilize dynamics. Walking reference
trajectory generation is a key problem. Linear Inverted
Pendulum Model (LIPM) and Zero Moment Point (ZMP)
Criterion based approaches in stable walking reference
generation are reported. In these methods, generally, the ZMP
reference during a stepping motion is kept fixed in the middle of
the supporting foot sole. This kind of reference generation lacks
naturalness, in that, the ZMP in the human walk does not stay
fixed, but it moves forward under the supporting foot. This paper
proposes a reference generation algorithm based on the LIPM
and moving support foot ZMP references. The application of
Fourier series approximation simplifies the solution and it
generates a smooth ZMP reference. A simple inverse kinematics
based joint space controller is used for the tests of the developed
reference trajectory through full-dynamics 3D simulation. A 12
DOF biped robot model is used in the simulations. Simulation
studies suggest that the moving ZMP references are more energy
efficient than the ones with fixed ZMP under the supporting foot.
The results are promising for implementations
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