36,572 research outputs found
Framework for state and unknown input estimation of linear time-varying systems
The design of unknown-input decoupled observers and filters requires the
assumption of an existence condition in the literature. This paper addresses an
unknown input filtering problem where the existence condition is not satisfied.
Instead of designing a traditional unknown input decoupled filter, a
Double-Model Adaptive Estimation approach is extended to solve the unknown
input filtering problem. It is proved that the state and the unknown inputs can
be estimated and decoupled using the extended Double-Model Adaptive Estimation
approach without satisfying the existence condition. Numerical examples are
presented in which the performance of the proposed approach is compared to
methods from literature.Comment: This paper has been accepted by Automatica. It considers unknown
input estimation or fault and disturbances estimation. Existing approaches
considers the case where the effects of fault and disturbance can be
decoupled. In our paper, we consider the case where the effects of fault and
disturbance are coupled. This approach can be easily extended to nonlinear
system
Algorithm for adaptive observation based on method of instrumental variables
When the input signal and the output value of the object of control cannot be measured accurately, the state vector is estimated. The instrumental variables (IVs) method is a commonly used parameter estimation method [1-10]. The task of adaptive observation is to create state observers containing parameter estimators. In adaptive observers, the matrices A and b or c (depending on the chosen canonical state-space representation form) are assumed to be unknown. In the monitoring process, parameter estimation is performed, the unknown matrices are determined, and then the state vector is calculated. The paper aims to present a non-recurrent adaptive observation algorithm for SISO linear time-invariant (LTI) discrete systems. The algorithm is based on the instrumental variables (IVs) method, and the adaptive state observer (ASO) estimates the parameters, the initial and the current state vectors of the discrete system. The algorithm's workability and effectiveness are proved by using simulation data in MATLAB/Simulink
Observer-based robust adaptive control for uncertain stochastic Hamiltonian systems with state and input delays
This paper investigates the observer-based robust adaptive control problem for a class of stochastic Hamiltonian systems. The systems under consideration relate to parameter uncertainties, unknown state time-delay and input delay. The purpose is to design a delay-dependent observer-based adaptive control law such that for all admissible uncertainties, as well as stochasticity, the closed-loop error system is robustly asymptotically stable in the mean square. Several sufficient conditions are presented to ensure the rationality and validity of the proposed control laws and observers, which are derived based on Lyapunov functional method. Numerical simulations spell out to illustrate the effectiveness of the proposed theories
Exponentially Stable Adaptive Observation for Systems Parameterized by Unknown Physical Parameters
The method to design exponentially stable adaptive observers is proposed for
linear time-invariant systems parameterized by unknown physical parameters.
Unlike existing adaptive solutions, the system state-space matrices A, B are
not restricted to be represented in the observer canonical form to implement
the observer. The original system description is used instead, and,
consequently, the original state vector is obtained. The class of systems for
which the method is applicable is identified via three assumptions related to:
(i) the boundedness of a control signal and all system trajectories, (ii) the
identifiability of the physical parameters of A and B from the numerator and
denominator polynomials of a system input/output transfer function and (iii)
the complete observability of system states. In case they are met and the
regressor is finitely exciting, the proposed adaptive observer, which is based
on the known GPEBO and DREM procedures, ensures exponential convergence of both
system parameters and states estimates to their true values. Detailed analysis
for stability and convergence has been provided along with simulation results
to validate the developed theory.Comment: 8 pages, 2 figure
Disturbance observer-based neural network control of cooperative multiple manipulators with input saturation
In this paper, the complex problems of internal forces and position control are studied simultaneously and a disturbance observer-based radial basis function neural network (RBFNN) control scheme is proposed to: 1) estimate the unknown parameters accurately; 2) approximate the disturbance experienced by the system due to input saturation; and 3) simultaneously improve the robustness of the system. More specifically, the proposed scheme utilizes disturbance observers, neural network (NN) collaborative control with an adaptive law, and full state feedback. Utilizing Lyapunov stability principles, it is shown that semiglobally uniformly bounded stability is guaranteed for all controlled signals of the closed-loop system. The effectiveness of the proposed controller as predicted by the theoretical analysis is verified by comparative experimental studies
LMI-Based Reset Unknown Input Observer for State Estimation of Linear Uncertain Systems
This paper proposes a novel kind of Unknown Input Observer (UIO) called Reset
Unknown Input Observer (R-UIO) for state estimation of linear systems in the
presence of disturbance using Linear Matrix Inequality (LMI) techniques. In
R-UIO, the states of the observer are reset to the after-reset value based on
an appropriate reset law in order to decrease the norm and settling time
of estimation error. It is shown that the application of the reset theory to
the UIOs in the LTI framework can significantly improve the transient response
of the observer. Moreover, the devised approach can be applied to both SISO and
MIMO systems. Furthermore, the stability and convergence analysis of the
devised R-UIO is addressed. Finally, the efficiency of the proposed method is
demonstrated by simulation results
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
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