46,629 research outputs found
A Robust Variable Step Size Fractional Least Mean Square (RVSS-FLMS) Algorithm
In this paper, we propose an adaptive framework for the variable step size of
the fractional least mean square (FLMS) algorithm. The proposed algorithm named
the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step
size of the FLMS to achieve high convergence rate with low steady state error.
For the evaluation purpose, the problem of system identification is considered.
The experiments clearly show that the proposed approach achieves better
convergence rate compared to the FLMS and adaptive step-size modified FLMS
(AMFLMS).Comment: 15 pages, 3 figures, 13th IEEE Colloquium on Signal Processing & its
Applications (CSPA 2017
A MRAS-based Learning Feed-forward Controller
Inspired by learning feed–forward control structures, this paper considers the adaptation of the parameters of a model–reference based learning feed–forward controller that realizes an inverse model of the process. The actual process response is determined by a setpoint generator. For linear systems it can be proved that the controlled system is asymptotically stable in the sense of Liapunov. Compared with more standard model reference configurations this system has a superior performance. It is fast, robust and relatively insensitive for noisy measurements. Simulations with an arbitrary second–order process and with a model of a typical fourth–ordermechatronics process demonstrate this
Tracking control with adaption of kites
A novel tracking paradigm for flying geometric trajectories using tethered
kites is presented. It is shown how the differential-geometric notion of
turning angle can be used as a one-dimensional representation of the kite
trajectory, and how this leads to a single-input single-output (SISO) tracking
problem. Based on this principle a Lyapunov-based nonlinear adaptive controller
is developed that only needs control derivatives of the kite aerodynamic model.
The resulting controller is validated using simulations with a point-mass kite
model.Comment: 20 pages, 12 figure
Sufficient Conditions for Feasibility and Optimality of Real-Time Optimization Schemes - I. Theoretical Foundations
The idea of iterative process optimization based on collected output
measurements, or "real-time optimization" (RTO), has gained much prominence in
recent decades, with many RTO algorithms being proposed, researched, and
developed. While the essential goal of these schemes is to drive the process to
its true optimal conditions without violating any safety-critical, or "hard",
constraints, no generalized, unified approach for guaranteeing this behavior
exists. In this two-part paper, we propose an implementable set of conditions
that can enforce these properties for any RTO algorithm. The first part of the
work is dedicated to the theory behind the sufficient conditions for
feasibility and optimality (SCFO), together with their basic implementation
strategy. RTO algorithms enforcing the SCFO are shown to perform as desired in
several numerical examples - allowing for feasible-side convergence to the
plant optimum where algorithms not enforcing the conditions would fail.Comment: Working paper; supplementary material available at:
http://infoscience.epfl.ch/record/18807
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