39,465 research outputs found
Extremum Seeking-based Iterative Learning Linear MPC
In this work we study the problem of adaptive MPC for linear time-invariant
uncertain models. We assume linear models with parametric uncertainties, and
propose an iterative multi-variable extremum seeking (MES)-based learning MPC
algorithm to learn on-line the uncertain parameters and update the MPC model.
We show the effectiveness of this algorithm on a DC servo motor control
example.Comment: To appear at the IEEE MSC 201
Iterative Machine Learning for Precision Trajectory Tracking with Series Elastic Actuators
When robots operate in unknown environments small errors in postions can lead
to large variations in the contact forces, especially with typical
high-impedance designs. This can potentially damage the surroundings and/or the
robot. Series elastic actuators (SEAs) are a popular way to reduce the output
impedance of a robotic arm to improve control authority over the force exerted
on the environment. However this increased control over forces with lower
impedance comes at the cost of lower positioning precision and bandwidth. This
article examines the use of an iteratively-learned feedforward command to
improve position tracking when using SEAs. Over each iteration, the output
responses of the system to the quantized inputs are used to estimate a
linearized local system models. These estimated models are obtained using a
complex-valued Gaussian Process Regression (cGPR) technique and then, used to
generate a new feedforward input command based on the previous iteration's
error. This article illustrates this iterative machine learning (IML) technique
for a two degree of freedom (2-DOF) robotic arm, and demonstrates successful
convergence of the IML approach to reduce the tracking error.Comment: 9 pages, 16 figure. Submitted to AMC Worksho
Incremental embodied chaotic exploration of self-organized motor behaviors with proprioceptor adaptation
This paper presents a general and fully dynamic embodied artificial neural system, which incrementally explores and learns motor behaviors through an integrated combination of chaotic search and reflex learning. The former uses adaptive bifurcation to exploit the intrinsic chaotic dynamics arising from neuro-body-environment interactions, while the latter is based around proprioceptor adaptation. The overall iterative search process formed from this combination is shown to have a close relationship to evolutionary methods. The architecture developed here allows realtime goal-directed exploration and learning of the possible motor patterns (e.g., for locomotion) of embodied systems of arbitrary morphology. Examples of its successful application to a simple biomechanical model, a simulated swimming robot, and a simulated quadruped robot are given. The tractability of the biomechanical systems allows detailed analysis of the overall dynamics of the search process. This analysis sheds light on the strong parallels with evolutionary search
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