3,128 research outputs found
Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module
The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project
A combined B-Spline-Neural-Network and ARX Model for Online Identi cation of Nonlinear Dynamic Actuation Systems
This paper presents a block oriented nonlinear dynamic model suitable for
online identi cation.The model has the well known Hammerstein architecture
where as a novelty the nonlinear static part is represented by a B-spline
neural network (BSNN), and the linear static one is formalized by an auto
regressive exogenous model (ARX). The model is suitable as a feed-forward
control module in combination with a classical feedback controller to regulate
velocity and position of pneumatic and hydraulic actuation systems
which present non stationary nonlinear dynamics. The adaptation of both
the linear and nonlinear parts is taking place simultaneously on a patterby-
patter basis by applying a combination of error-driven learning rules and
the recursive least squares method. This allows to decrease the amount of
computation needed to identify the model's parameters and therefore makes
the technique suitable for real time applications. The model was tested with
a silver box benchmark and results show that the parameters are converging
to a stable value after 1500 samples, equivalent to 7.5s of running time.
The comparison with a pure ARX and BSNN model indicates a substantial
improvement in terms of the RMS error, while the comparison with alternative
non linear dynamic models like the NNOE and NNARX, having the
same number of parameters but greater computational complexity, shows
comparable performances
Learning For Predictive Control: A Dual Gaussian Process Approach
An important issue in model-based control design is that an accurate dynamic
model of the system is generally nonlinear, complex, and costly to obtain. This
limits achievable control performance in practice. Gaussian process (GP) based
estimation of system models is an effective tool to learn unknown dynamics
directly from input/output data. However, conventional GP-based control methods
often ignore the computational cost associated with accumulating data during
the operation of the system and how to handle forgetting in continuous
adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based
model predictive control (MPC) strategy that enables efficient use of online
learning based predictive control without the danger of catastrophic
forgetting. The bio-inspired DGP structure is a combination of a long-term GP
and a short-term GP, where the long-term GP is used to keep the learned
knowledge in memory and the short-term GP is employed to rapidly compensate
unknown dynamics during online operation. Furthermore, a novel recursive online
update strategy for the short-term GP is proposed to successively improve the
learnt model during online operation. Effectiveness of the proposed strategy is
demonstrated via numerical simulations.Comment: arXiv admin note: substantial text overlap with arXiv:2112.1166
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