98 research outputs found
Nonlinear adaptive estimation with application to sinusoidal identification
Parameter estimation of a sinusoidal signal in real-time is encountered in applications
in numerous areas of engineering. Parameters of interest are usually amplitude, frequency
and phase wherein frequency tracking is the fundamental task in sinusoidal estimation. This thesis deals with the problem of identifying a signal that comprises n (n ≥ 1) harmonics from a measurement possibly affected by structured and unstructured disturbances. The structured perturbations are modeled as a time-polynomial so as to represent, for example, bias and drift phenomena typically present in applications, whereas the unstructured disturbances are characterized as bounded perturbation. Several approaches upon different theoretical tools are presented in this thesis, and classified into two main categories: asymptotic and non-asymptotic methodologies, depending on the qualitative characteristics of the convergence behavior over time.
The first part of the thesis is devoted to the asymptotic estimators, which typically consist
in a pre-filtering module for generating a number of auxiliary signals, independent of
the structured perturbations. These auxiliary signals can be used either directly or indirectly
to estimate—in an adaptive way—the frequency, the amplitude and the phase of the
sinusoidal signals. More specifically, the direct approach is based on a simple gradient
method, which ensures Input-to-State Stability of the estimation error with respect to the
bounded-unstructured disturbances. The indirect method exploits a specific adaptive observer scheme equipped with a switching criterion allowing to properly address in a stable way the poor excitation scenarios. It is shown that the adaptive observer method can be applied for estimating multi-frequencies through an augmented but unified framework, which is a crucial advantage with respect to direct approaches. The estimators’ stability properties are also analyzed by Input-to-State-Stability (ISS) arguments.
In the second part we present a non-asymptotic estimation methodology characterized by
a distinctive feature that permits finite-time convergence of the estimates. Resorting to the
Volterra integral operators with suitably designed kernels, the measured signal is processed, yielding a set of auxiliary signals, in which the influence of the unknown initial conditions is annihilated. A sliding mode-based adaptation law, fed by the aforementioned auxiliary signals, is proposed for deadbeat estimation of the frequency and amplitude, which are dealt with in a step-by-step manner. The worst case behavior of the proposed algorithm in the presence of bounded perturbation is studied by ISS tools.
The practical characteristics of all estimation techniques are evaluated and compared
with other existing techniques by extensive simulations and experimental trials.Open Acces
Parameter Estimation for a Sinusoidal Signal with a Time-Varying Amplitude
This paper addresses the parameter estimation
problem of a non-stationary sinusoidal signal with a timevarying amplitude, which is given by a known function of
time multiplied by an unknown constant coefficient. A robust
estimation algorithm is proposed for identifying the unknown
frequency and the amplitude coefficient in real-time. The estimation algorithm is constructed based on the Volterra integral
operator with suitably designed kernels and sliding mode
adaptation laws. It is shown that the parameter estimation error
converges to zero within an arbitrarily small finite time, and the
robustness against bounded additive disturbances is certified by
bounded-input-bounded-output arguments. The effectiveness of
the estimation technique is evaluated and compared with other
existing tools through numerical simulations
Hyperbolic Interaction Model For Hierarchical Multi-Label Classification
Different from the traditional classification tasks which assume mutual
exclusion of labels, hierarchical multi-label classification (HMLC) aims to
assign multiple labels to every instance with the labels organized under
hierarchical relations. Besides the labels, since linguistic ontologies are
intrinsic hierarchies, the conceptual relations between words can also form
hierarchical structures. Thus it can be a challenge to learn mappings from word
hierarchies to label hierarchies. We propose to model the word and label
hierarchies by embedding them jointly in the hyperbolic space. The main reason
is that the tree-likeness of the hyperbolic space matches the complexity of
symbolic data with hierarchical structures. A new Hyperbolic Interaction Model
(HyperIM) is designed to learn the label-aware document representations and
make predictions for HMLC. Extensive experiments are conducted on three
benchmark datasets. The results have demonstrated that the new model can
realistically capture the complex data structures and further improve the
performance for HMLC comparing with the state-of-the-art methods. To facilitate
future research, our code is publicly available
Event-triggered distributed MPC for resilient voltage control of an islanded microgrid
This paper addresses the problem of distributed secondary voltage control of
an islanded microgrid (MG) from a cyber-physical perspective. An
event-triggered distributed model predictive control (DMPC) scheme is designed
to regulate the voltage magnitude of each distributed generators (DGs) in order
to achieve a better trade-off between the control performance and communication
and computation burdens. By using two novel event triggering conditions that
can be easily embedded into the DMPC for the application of MG control, the
computation and communication burdens are significantly reduced with negligible
compromise of control performance. In addition, to reduce the sensor cost and
to eliminate the negative effects of non-linearity, an adaptive non-asymptotic
observer is utilized to estimate the internal and output signals of each DG.
Thanks to the deadbeat observation property, the observer can be applied
periodically to cooperate with the DMPC-based voltage regulator. Finally, the
effectiveness of the proposed control method has been tested on a simple
configuration with 4 DGs and the modified IEEE-13 test system through several
representative scenarios
Cyber-Resilient Self-Triggered Distributed Control of Networked Microgrids Against Multi-Layer DoS Attacks
Networked microgrids with high penetration of
distributed generators have ubiquitous remote information exchange, which may be exposed to various cyber security threats.
This paper, for the first time, addresses a consensus problem
in terms of frequency synchronisation in networked microgrids
subject to multi-layer denial of service (DoS) attacks, which could
simultaneously affect communication, measurement and control
actuation channels. A unified notion of Persistency-of-Data-Flow
(PoDF) is proposed to characterise the data unavailability in
different information network links, and further quantifies the
multi-layer DoS effects on the hierarchical system. With PoDF,
we provide a sufficient condition of the DoS attacks under
which the consensus can be preserved with the proposed edgebased self-triggered distributed control framework. In addition,
to mitigate the conservativeness of offline design against the
worst-case attack across all agents, an online self-adaptive scheme
of the control parameters is developed to fully utilise the latest
available information of all data transmission channels. Finally,
the effectiveness of the proposed cyber-resilient self-triggered
distributed control is verified by representative case studies
Fixed-Time Convergent Distributed Observer Design of Linear Systems: A Kernel-Based Approach
The robust distributed state estimation for a class
of continuous-time linear time-invariant systems is achieved by a
novel kernel-based distributed observer, which, for the first time,
ensures fixed-time convergence properties. The communication
network between the agents is prescribed by a directed graph
in which each node involves a fixed-time convergent estimator.
The local observer estimates and broadcasts the observable states
among neighbours so that the full state vector can be recovered
at each node and the estimation error reaches zero after a predefined fixed time in the absence of perturbation. This represents a
new distributed estimation framework that enables faster convergence speed and further reduced information exchange compared
to a conventional Luenberger-like approach. The ubiquitous timevarying communication delay across the network is suitably
compensated by a prediction scheme. Moreover, the robustness
of the algorithm in the presence of bounded measurement
and process noise is characterised. Numerical simulations and
comparisons demonstrate the effectiveness of the observer and
its advantages over the existing methods
Robust Adaptive Learning-based Path Tracking Control of Autonomous Vehicles under Uncertain Driving Environments
This paper investigates the path tracking control
problem of autonomous vehicles subject to modelling uncertainties and external disturbances. The problem is approached
by employing a 2-degree of freedom vehicle model, which is
reformulated into a newly defined parametric form with the
system uncertainties being lumped into an unknown parametric
vector. On top of the parametric system representation, a novel
robust adaptive learning control (RALC) approach is then
developed, which estimates the system uncertainties through
iterative learning while treating the external disturbances by
adopting a robust term. It is shown that the proposed approach
is able to improve the lateral tracking performance gradually
through learning from previous control experiences, despite only
partial knowledge of the vehicle dynamics being available. It is
noteworthy that a novel technique targeting at the non-square
input distribution matrix is employed so as to deal with the
under-actuation property of the vehicle dynamics, which extends
the adaptive learning control theory from square systems to
non-square systems. Moreover, the convergence properties of
the RALC algorithm are analysed under the framework of
Lyapunov-like theory by virtue of the composite energy function
and the λ-norm. The effectiveness of the proposed control
scheme is verified by representative simulation examples and
comparisons with existing methods
Distributed Model Predictive Control for Heterogeneous Vehicle Platoon with Inter-Vehicular Spacing Constraints
This paper proposes a distributed control scheme
for a platoon of heterogeneous vehicles based on the mechanism
of model predictive control (MPC). The platoon composes of a
group of vehicles interacting with each other via inter-vehicular
spacing constraints, to avoid collision and reduce communication
latency, and aims to make multiple vehicles driving on the same
lane safely with a close range and the same velocity. Each
vehicle is subject to both state constraints and input constraints,
communicates only with neighboring vehicles, and may not know
a priori desired setpoint. We divide the computation of control
inputs into several local optimization problems based on each
vehicle’s local information. To compute the control input of
each vehicle based on local information, a distributed computing
method must be adopted and thus the coupled constraint is
required to be decoupled. This is achieved by introducing the
reference state trajectories from neighboring vehicles for each
vehicle and by employing the interactive structure of computing
local problems of vehicles with odd indices and even indices. It
is shown that the feasibility of MPC optimization problems is
achieved at all time steps based on tailored terminal inequality
constraints, and the asymptotic stability of each vehicle to the
desired trajectory is guaranteed even under a single iteration
between vehicles at each time. Finally, a comparison simulation
is conducted to demonstrate the effectiveness of the proposed
distributed MPC method for heterogeneous vehicle control with
respect to normal and extreme scenarios
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