2,389 research outputs found
Modeling of linear fading memory systems
Motivated by questions of approximate modeling and identification, we consider various classes of linear time-varying bounded-input-bounded output (BIBO) stable fading memory systems and the characterizations are proved. These include fading memory systems in general, almost periodic systems, and asymptotically periodic systems. We also show that the norm and strong convergence coincide for BIBO stable causal fading memory system
Codes for QPSK modulation with invariance under 90 degrees rotation
The new rate 1/2 nonlinear convolutional codes for quadrature phase shift keying (QPSK) modulation allow the achievement of full 90 degree rotational invariance of coded QPSK signal sequences at no significant loss in real coding gains when compared to linear codes. For mobile communication systems operating in a fading environment with frequent periods of low signal-to-noise ratio and the possibility of losses of carrier phase synchronization in the receiver, the invariance to 90 degree ambiguous demodulation should be a significant advantage
Data based predictive control: Application to water distribution networks
In this thesis, the main goal is to propose novel data based predictive
controllers to cope with complex industrial infrastructures such as water
distribution networks. This sort of systems have several inputs and out-
puts, complicate nonlinear dynamics, binary actuators and they are usually
perturbed by disturbances and noise and require real-time control implemen-
tation. The proposed controllers have to deal successfully with these issues
while using the available information, such as past operation data of the
process, or system properties as fading dynamics.
To this end, the control strategies presented in this work follow a predic-
tive control approach. The control action computed by the proposed data-
driven strategies are obtained as the solution of an optimization problem
that is similar in essence to those used in model predictive control (MPC)
based on a cost function that determines the performance to be optimized.
In the proposed approach however, the prediction model is substituted by
an inference data based strategy, either to identify a model, an unknown
control law or estimate the future cost of a given decision. As in MPC, the
proposed strategies are based on a receding horizon implementation, which
implies that the optimization problems considered have to be solved online.
In order to obtain problems that can be solved e ciently, most of the
strategies proposed in this thesis are based on direct weight optimization
for ease of implementation and computational complexity reasons. Linear
convex combination is a simple and strong tool in continuous domain and
computational load associated with the constrained optimization problems
generated by linear convex combination are relatively soft. This fact makes
the proposed data based predictive approaches suitable to be used in real
time applications.
The proposed approaches selects the most adequate information (similar
to the current situation according to output, state, input, disturbances,etc.),
in particular, data which is close to the current state or situation of the
system. Using local data can be interpreted as an implicit local linearisation
of the system every time we solve the model-free data driven optimization
problem. This implies that even though, model free data driven approaches
presented in this thesis are based on linear theory, they can successfully deal
with nonlinear systems because of the implicit information available in the
database.
Finally, a learning-based approach for robust predictive control design for
multi-input multi-output (MIMO) linear systems is also presented, in which
the effect of the estimation and measuring errors or the effect of unknown
perturbations in large scale complex system is considered
Efficient Multidimensional Regularization for Volterra Series Estimation
This paper presents an efficient nonparametric time domain nonlinear system
identification method. It is shown how truncated Volterra series models can be
efficiently estimated without the need of long, transient-free measurements.
The method is a novel extension of the regularization methods that have been
developed for impulse response estimates of linear time invariant systems. To
avoid the excessive memory needs in case of long measurements or large number
of estimated parameters, a practical gradient-based estimation method is also
provided, leading to the same numerical results as the proposed Volterra
estimation method. Moreover, the transient effects in the simulated output are
removed by a special regularization method based on the novel ideas of
transient removal for Linear Time-Varying (LTV) systems. Combining the proposed
methodologies, the nonparametric Volterra models of the cascaded water tanks
benchmark are presented in this paper. The results for different scenarios
varying from a simple Finite Impulse Response (FIR) model to a 3rd degree
Volterra series with and without transient removal are compared and studied. It
is clear that the obtained models capture the system dynamics when tested on a
validation dataset, and their performance is comparable with the white-box
(physical) models
Digital adaptive flight controller development
A design study of adaptive control logic suitable for implementation in modern airborne digital flight computers was conducted. Two designs are described for an example aircraft. Each of these designs uses a weighted least squares procedure to identify parameters defining the dynamics of the aircraft. The two designs differ in the way in which control law parameters are determined. One uses the solution of an optimal linear regulator problem to determine these parameters while the other uses a procedure called single stage optimization. Extensive simulation results and analysis leading to the designs are presented
Perspective on unconventional computing using magnetic skyrmions
Learning and pattern recognition inevitably requires memory of previous
events, a feature that conventional CMOS hardware needs to artificially
simulate. Dynamical systems naturally provide the memory, complexity, and
nonlinearity needed for a plethora of different unconventional computing
approaches. In this perspective article, we focus on the unconventional
computing concept of reservoir computing and provide an overview of key
physical reservoir works reported. We focus on the promising platform of
magnetic structures and, in particular, skyrmions, which potentially allow for
low-power applications. Moreover, we discuss skyrmion-based implementations of
Brownian computing, which has recently been combined with reservoir computing.
This computing paradigm leverages the thermal fluctuations present in many
skyrmion systems. Finally, we provide an outlook on the most important
challenges in this field.Comment: 19 pages and 3 figure
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