29,549 research outputs found
Multiple-Model Adaptive Control With Set-Valued Observers
This paper proposes a multiple-model adaptive control methodology, using
set-valued observers (MMAC-SVO) for the identification subsystem, that is able
to provide robust stability and performance guarantees for the closed-loop,
when the plant, which can be open-loop stable or unstable, has significant
parametric uncertainty. We illustrate, with an example, how set-valued
observers (SVOs) can be used to select regions of uncertainty for the
parameters of the plant. We also discuss some of the most problematic
computational shortcomings and numerical issues that arise from the use of this
kind of robust estimation methods. The behavior of the proposed control
algorithm is demonstrated in simulation.Comment: Combined 48th IEEE Conference on Decision and Control and 28th
Chinese Control Conference, 200
A review of convex approaches for control, observation and safety of linear parameter varying and Takagi-Sugeno systems
This paper provides a review about the concept of convex systems based on Takagi-Sugeno, linear parameter varying (LPV) and quasi-LPV modeling. These paradigms are capable of hiding the nonlinearities by means of an equivalent description which uses a set of linear models interpolated by appropriately defined weighing functions. Convex systems have become very popular since they allow applying extended linear techniques based on linear matrix inequalities (LMIs) to complex nonlinear systems. This survey aims at providing the reader with a significant overview of the existing LMI-based techniques for convex systems in the fields of control, observation and safety. Firstly, a detailed review of stability, feedback, tracking and model predictive control (MPC) convex controllers is considered. Secondly, the problem of state estimation is addressed through the design of proportional, proportional-integral, unknown input and descriptor observers. Finally, safety of convex systems is discussed by describing popular techniques for fault diagnosis and fault tolerant control (FTC).Peer ReviewedPostprint (published version
A semidefinite relaxation procedure for fault-tolerant observer design
A fault-tolerant observer design methodology is proposed. The aim is to guarantee a minimum level of closed-loop performance under all possible sensor fault combinations while optimizing performance under the nominal, fault-free condition. A novel approach is proposed to tackle the combinatorial nature of the problem, which is computationally intractable even for a moderate number of sensors, by recasting the problem as a robust performance problem, where the uncertainty set is composed of all combinations of a set of binary variables. A procedure based on an elimination lemma and an extension of a semidefinite relaxation procedure for binary variables is then used to derive sufficient conditions (necessary and sufficient in the case of one binary variable) for the solution of the problem which significantly reduces the number of matrix inequalities needed to solve the problem. The procedure is illustrated by considering a fault-tolerant observer switching scheme in which the observer outputs track the actual sensor fault condition. A numerical example from an electric power application is presented to illustrate the effectiveness of the design
Adaptive observers for nonlinearly parameterized systems subjected to parametric constraints
We consider the problem of adaptive observer design in the settings when the
system is allowed to be nonlinear in the parameters, and furthermore they are
to satisfy additional feasibility constraints. A solution to the problem is
proposed that is based on the idea of universal observers and non-uniform
small-gain theorem. The procedure is illustrated with an example.Comment: 19th IFAC World Congress on Automatic Control, 10869-10874, South
Africa, Cape Town, 24th-29th August, 201
A Multi-Observer Based Estimation Framework for Nonlinear Systems under Sensor Attacks
We address the problem of state estimation and attack isolation for general
discrete-time nonlinear systems when sensors are corrupted by (potentially
unbounded) attack signals. For a large class of nonlinear plants and observers,
we provide a general estimation scheme, built around the idea of sensor
redundancy and multi-observer, capable of reconstructing the system state in
spite of sensor attacks and noise. This scheme has been proposed by others for
linear systems/observers and here we propose a unifying framework for a much
larger class of nonlinear systems/observers. Using the proposed estimator, we
provide an isolation algorithm to pinpoint attacks on sensors during sliding
time windows. Simulation results are presented to illustrate the performance of
our tools.Comment: arXiv admin note: text overlap with arXiv:1806.0648
Control optimization, stabilization and computer algorithms for space applications
Research of control optimization, stochastic stability, and air traffic control problem
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