12 research outputs found
Concurrent Learning Adaptive Model Predictive Control with Pseudospectral Implementation
This paper presents a control architecture in which a direct adaptive control
technique is used within the model predictive control framework, using the
concurrent learning based approach, to compensate for model uncertainties. At
each time step, the control sequences and the parameter estimates are both used
as the optimization arguments, thereby undermining the need for switching
between the learning phase and the control phase, as is the case with
hybrid-direct-indirect control architectures. The state derivatives are
approximated using pseudospectral methods, which are vastly used for numerical
optimal control problems. Theoretical results and numerical simulation examples
are used to establish the effectiveness of the architecture.Comment: 21 pages, 13 figure
Localization of Control Synthesis Problem for Large-Scale Interconnected System Using IQC and Dissipativity Theories
The synthesis problem for the compositional performance certification of
interconnected systems is considered. A fairly unified description of control
synthesis problem is given using integral quadratic constraints (IQC) and
dissipativity. Starting with a given large-scale interconnected system and a
global performance objective, an optimization problem is formulated to search
for admissible dissipativity properties of each subsystems. Local control laws
are then synthesized to certify the relevant dissipativity properties.
Moreover, the term localization is introduced to describe a finite collection
of syntheses problems, for the local subsystems, which are a feasibility
certificate for the global synthesis problem. Consequently, the problem of
localizing the global problem to a smaller collection of disjointed sets of
subsystems, called groups, is considered. This works looks promising as another
way of looking at decentralized control and also as a way of doing performance
specifications for components in a large-scale system
Attack-Resilient Weighted Observer with Prior Pruning
Security related questions for Cyber Physical Systems (CPS) have attracted
much research attention in searching for novel methods for attack-resilient
control and/or estimation. Specifically, false data injection attacks (FDIAs)
have been shown to be capable of bypassing bad data detection (BDD), while
arbitrarily compromising the integrity of state estimators and robust
controller even with very sparse measurements corruption. Moreover, based on
the inherent sparsity of pragmatic attack signals, -minimization scheme
has been used extensively to improve the design of attack-resilient estimators.
For this, the theoretical maximum for the percentage of compromised nodes that
can be accommodated has been shown to be . In order to guarantee correct
state recoveries for larger percentage of attacked nodes, researchers have
begun to incorporate prior information into the underlying resilient observer
design framework. For the most pragmatic cases, this prior information is often
obtained through some data-driven machine learning process. Existing results
have shown strong positive correlation between the tolerated attack percentages
and the precision of the prior information. In this paper, we present a pruning
method to improve the precision of the prior information, given corresponding
stochastic uncertainty characteristics of the underlying machine learning
model. Then a weighted -minimization is proposed based on the pruned
prior. The theoretical and simulation results show that the pruning method
significantly improves the observer performance for much larger attack
percentages, even when moderately accurate machine learning model used.Comment:
Robust Resilient Signal Reconstruction under Adversarial Attacks
We consider the problem of signal reconstruction for a system under sparse
unbounded signal corruption by an adversarial agent. The reconstruction problem
follows the standard error coding problem that has been studied extensively in
literature, with the added consideration of support estimation of the attack
vector. The problem is formulated as a constrained optimization problem --
merging exciting developments in the field of machine learning and estimation
theory. Sufficient conditions for the reconstructability and the associated
reconstruction error bounds were obtained for both exact and inexact support
estimation of the attack vector. Special cases of data-driven model and linear
dynamical systems were also considered.Comment: 7 page
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Concurrent Learning Adaptive Model Predictive Control with Pseudospectral Implementation
This paper presents a control architecture in which a direct adaptive control
technique is used within the model predictive control framework, using the concurrent
learning based approach, to compensate for model uncertainties. At each time step, the
control sequences and the parameter estimates are both used as the optimization arguments,
thereby undermining the need for switching between the learning phase and the control
phase, as is the case with hybrid-direct-indirect control architectures. The state
derivatives are approximated using pseudospectral methods, which are vastly used for
numerical optimal control problems. Theoretical results and numerical simulation examples
are used to establish the effectiveness of the architecture
Recommended from our members
Localization of Control Synthesis Problem for Large-Scale Interconnected System Using IQC and Dissipativity Theories
The synthesis problem for the compositional performance certification of
interconnected systems is considered. A fairly unified description of control synthesis
problem is given using integral quadratic constraints (IQC) and dissipativity. Starting
with a given large-scale interconnected system and a global performance objective, an
optimization problem is formulated to search for admissible dissipativity properties of
each subsystems. Local control laws are then synthesized to certify the relevant
dissipativity properties. Moreover, the term localization is introduced to describe a
finite collection of syntheses problems, for the local subsystems, which are a feasibility
certificate for the global synthesis problem. Consequently, the problem of localizing the
global problem to a smaller collection of disjointed sets of subsystems, called groups, is
considered. This works looks promising as another way of looking at decentralized control
and also as a way of doing performance specifications for components in a large-scale
system