5,270 research outputs found
Nonlinear disturbance attenuation control of hydraulic robotics
This paper presents a novel nonlinear disturbance rejection control for
hydraulic robots. This method requires two third-order filters as well as
inverse dynamics in order to estimate the disturbances. All the parameters for
the third-order filters are pre-defined. The proposed method is nonlinear,
which does not require the linearization of the rigid body dynamics. The
estimated disturbances are used by the nonlinear controller in order to achieve
disturbance attenuation. The performance of the proposed approach is compared
with existing approaches. Finally, the tracking performance and robustness of
the proposed approach is validated extensively on real hardware by performing
different tasks under either internal or both internal and external
disturbances. The experimental results demonstrate the robustness and superior
tracking performance of the proposed approach
A New Data Source for Inverse Dynamics Learning
Modern robotics is gravitating toward increasingly collaborative human robot
interaction. Tools such as acceleration policies can naturally support the
realization of reactive, adaptive, and compliant robots. These tools require us
to model the system dynamics accurately -- a difficult task. The fundamental
problem remains that simulation and reality diverge--we do not know how to
accurately change a robot's state. Thus, recent research on improving inverse
dynamics models has been focused on making use of machine learning techniques.
Traditional learning techniques train on the actual realized accelerations,
instead of the policy's desired accelerations, which is an indirect data
source. Here we show how an additional training signal -- measured at the
desired accelerations -- can be derived from a feedback control signal. This
effectively creates a second data source for learning inverse dynamics models.
Furthermore, we show how both the traditional and this new data source, can be
used to train task-specific models of the inverse dynamics, when used
independently or combined. We analyze the use of both data sources in
simulation and demonstrate its effectiveness on a real-world robotic platform.
We show that our system incrementally improves the learned inverse dynamics
model, and when using both data sources combined converges more consistently
and faster.Comment: IROS 201
Robust Whole-Body Motion Control of Legged Robots
We introduce a robust control architecture for the whole-body motion control
of torque controlled robots with arms and legs. The method is based on the
robust control of contact forces in order to track a planned Center of Mass
trajectory. Its appeal lies in the ability to guarantee robust stability and
performance despite rigid body model mismatch, actuator dynamics, delays,
contact surface stiffness, and unobserved ground profiles. Furthermore, we
introduce a task space decomposition approach which removes the coupling
effects between contact force controller and the other non-contact controllers.
Finally, we verify our control performance on a quadruped robot and compare its
performance to a standard inverse dynamics approach on hardware.Comment: 8 Page
Robust tuning of robot control systems
The computed torque control problem is examined for a robot arm with flexible, geared, joint drive systems which are typical in many industrial robots. The standard computed torque algorithm is not directly applicable to this class of manipulators because of the dynamics introduced by the joint drive system. The proposed approach to computed torque control combines a computed torque algorithm with torque controller at each joint. Three such control schemes are proposed. The first scheme uses the joint torque control system currently implemented on the robot arm and a novel form of the computed torque algorithm. The other two use the standard computed torque algorithm and a novel model following torque control system based on model following techniques. Standard tasks and performance indices are used to evaluate the performance of the controllers. Both numerical simulations and experiments are used in evaluation. The study shows that all three proposed systems lead to improved tracking performance over a conventional PD controller
Balancing experiments on a torque-controlled humanoid with hierarchical inverse dynamics
Recently several hierarchical inverse dynamics controllers based on cascades
of quadratic programs have been proposed for application on torque controlled
robots. They have important theoretical benefits but have never been
implemented on a torque controlled robot where model inaccuracies and real-time
computation requirements can be problematic. In this contribution we present an
experimental evaluation of these algorithms in the context of balance control
for a humanoid robot. The presented experiments demonstrate the applicability
of the approach under real robot conditions (i.e. model uncertainty, estimation
errors, etc). We propose a simplification of the optimization problem that
allows us to decrease computation time enough to implement it in a fast torque
control loop. We implement a momentum-based balance controller which shows
robust performance in face of unknown disturbances, even when the robot is
standing on only one foot. In a second experiment, a tracking task is evaluated
to demonstrate the performance of the controller with more complicated
hierarchies. Our results show that hierarchical inverse dynamics controllers
can be used for feedback control of humanoid robots and that momentum-based
balance control can be efficiently implemented on a real robot.Comment: appears in IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 201
Feedback MPC for Torque-Controlled Legged Robots
The computational power of mobile robots is currently insufficient to achieve
torque level whole-body Model Predictive Control (MPC) at the update rates
required for complex dynamic systems such as legged robots. This problem is
commonly circumvented by using a fast tracking controller to compensate for
model errors between updates. In this work, we show that the feedback policy
from a Differential Dynamic Programming (DDP) based MPC algorithm is a viable
alternative to bridge the gap between the low MPC update rate and the actuation
command rate. We propose to augment the DDP approach with a relaxed barrier
function to address inequality constraints arising from the friction cone. A
frequency-dependent cost function is used to reduce the sensitivity to
high-frequency model errors and actuator bandwidth limits. We demonstrate that
our approach can find stable locomotion policies for the torque-controlled
quadruped, ANYmal, both in simulation and on hardware.Comment: Paper accepted to IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS 2019
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