1,126 research outputs found
Automatic Differentiation of Rigid Body Dynamics for Optimal Control and Estimation
Many algorithms for control, optimization and estimation in robotics depend
on derivatives of the underlying system dynamics, e.g. to compute
linearizations, sensitivities or gradient directions. However, we show that
when dealing with Rigid Body Dynamics, these derivatives are difficult to
derive analytically and to implement efficiently. To overcome this issue, we
extend the modelling tool `RobCoGen' to be compatible with Automatic
Differentiation. Additionally, we propose how to automatically obtain the
derivatives and generate highly efficient source code. We highlight the
flexibility and performance of the approach in two application examples. First,
we show a Trajectory Optimization example for the quadrupedal robot HyQ, which
employs auto-differentiation on the dynamics including a contact model. Second,
we present a hardware experiment in which a 6 DoF robotic arm avoids a randomly
moving obstacle in a go-to task by fast, dynamic replanning
Distributed formation control for manipulator end-effectors
We present three classes of distributed formation controllers for achieving
and maintaining the 2D/3D formation shape of manipulator end-effectors to cope
with different scenarios due to availability of modeling parameters. We firstly
present a distributed formation controller for manipulators whose system
parameters are perfectly known. The formation control objective is achieved by
assigning virtual springs between end-effectors and by adding damping terms at
joints, which provides a clear physical interpretation of the proposed
solution. Subsequently, we extend it to the case where manipulator kinematic
and system parameters are not exactly known. An extra integrator and an
adaptive estimator are introduced for gravitational compensation and
stabilization, respectively. Simulation results with planar manipulators and
with seven degree-of-freedom humanoid manipulator arms are presented to
illustrate the effectiveness of the proposed approach.Comment: arXiv admin note: text overlap with arXiv:2103.1459
Adaptive Fuzzy Control of Puma Robot Manipulator in Task Space with Unknown Dynamic and Uncertain Kinematic
A In this paper, an adaptive direct fuzzy control system is presented to control the robot manipulator in task space. It is assumed that robot system has unknown dynamic and uncertain kinematic. The control system and adaption mechanism are firstly designed for joint space tracking. Then by using inverse Jacobian strategy, it is generalized for task space. After that, to overcome the problem of Jacobian matrix uncertainty, an improved adaptive control system is designed. All the design steps are illustrated by simulations
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