- Dettagli della rivista
- Formato
- Rivista
- eISSN
- 2083-8492
- Pubblicato per la prima volta
- 05 Apr 2007
- Periodo di pubblicazione
- 4 volte all'anno
- Lingue
- Inglese

#### Cerca

- Accesso libero

Input Reconstruction by Feedback Control for the Schlögl and FitzHugh–Nagumo Equations

Pagine: 5 - 22

#### Astratto

Dynamical reconstruction of unknown time-varying controls from inexact measurements of the state function is investigated for a semilinear parabolic equation with memory. This system includes as particular cases the Schlögl model and the FitzHugh–Nagumo equations. A numerical method is suggested that is based on techniques of feedback control. An error analysis is performed. Numerical examples confirm the theoretical predictions.

#### Parole chiave

- semilinear parabolic equation
- input reconstruction

- Accesso libero

An Information Based Approach to Stochastic Control Problems

Pagine: 23 - 34

#### Astratto

An information based method for solving stochastic control problems with partial observation is proposed. First, information-theoretic lower bounds of the cost function are analysed. It is shown, under rather weak assumptions, that reduction in the expected cost with closed-loop control compared with the best open-loop strategy is upper bounded by a non-decreasing function of mutual information between control variables and the state trajectory. On the basis of this result, an

#### Parole chiave

- stochastic control
- feedback
- information
- entropy

- Accesso libero

Nonlinear Model Predictive Control for Processes with Complex Dynamics: A Parameterisation Approach Using Laguerre Functions

Pagine: 35 - 46

#### Astratto

Classical model predictive control (MPC) algorithms need very long horizons when the controlled process has complex dynamics. In particular, the control horizon, which determines the number of decision variables optimised on-line at each sampling instant, is crucial since it significantly affects computational complexity. This work discusses a nonlinear MPC algorithm with on-line trajectory linearisation, which makes it possible to formulate a quadratic optimisation problem, as well as parameterisation using Laguerre functions, which reduces the number of decision variables. Simulation results of classical (not parameterised) MPC algorithms and some strategies with parameterisation are thoroughly compared. It is shown that for a benchmark system the MPC algorithm with on-line linearisation and parameterisation gives very good quality of control, comparable with that possible in classical MPC with long horizons and nonlinear optimisation.

#### Parole chiave

- process control
- nonlinear model predictive control
- Laguerre functions
- linearisation

- Accesso libero

Health–Aware and Fault–Tolerant Control of an Octorotor UAV System Based on Actuator Reliability

Pagine: 47 - 59

#### Astratto

A major goal in modern flight control systems is the need for improving reliability. This work presents a health-aware and fault-tolerant control approach for an octorotor UAV that allows distributing the control effort among the available actuators based on their health information. However, it is worth mentioning that, in the case of actuator fault occurrence, a reliability improvement can come into conflict with UAV controllability. Therefore, system reliability sensitivity is redefined and modified to prevent uncontrollable situations during the UAV’s mission. The priority given to each actuator is related to its importance in system reliability. Moreover, the proposed approach can reconfigure the controller to compensate actuator faults and improve the overall system reliability or delay maintenance tasks.

#### Parole chiave

- prognostics and health management
- health-aware control
- fault-tolerant control
- reliability analysis
- octorotor
- UAV

- Accesso libero

Fault Tolerant Multicontrollers for Nonlinear Systems: A Real Validation on a Chemical Process

Pagine: 61 - 74

#### Astratto

An active sensor fault tolerant controller for nonlinear systems represented by a decoupled multimodel is proposed. Active fault tolerant control requires accurate fault estimation. Thus, to estimate both state variables and sensor faults, a discrete unknown input multiobserver, based on an augmented state multimodel, is designed. The multiobserver gains are computed by solving linear matrix inequalities with equality constraints. A multicontrol strategy is proposed for the compensation of the sensor fault and recovering the desired performances. This strategy integrates a bank of controllers, corresponding to a set of partial models, to generate a set of control laws compensating the fault effect. Then, a switching strategy between the generated local control laws is established in order to apply the most suitable control law that tolerates the fault and maintains good closed loop performances. The effectiveness of the proposed strategy is proven through a numerical example and also through a real time application on a chemical reactor. The obtained results confirm satisfactory closed loop performance in terms of trajectory tracking and fault tolerance.

#### Parole chiave

- multicontroller
- experimental validation
- transesterification reactor
- discrete unknown input multiobserver
- fault tolerant control
- sensor fault estimation

- Accesso libero

Extremal Properties of Linear Dynamic Systems Controlled by Dirac’s Impulse

Pagine: 75 - 81

#### Astratto

The paper concerns the properties of linear dynamical systems described by linear differential equations, excited by the Dirac delta function. A differential equation of the form a_{n}^{n}_{1}_{0}_{m}^{m}_{1}_{0}_{i}, b_{j} >_{n}_{n}^{n}_{1}_{0} and _{m}_{m}^{m}_{1}_{0} partly interlace. The solution of the above equation is denoted by _{m}_{n}_{m}_{n}_{m}_{n}_{n}_{m}

#### Parole chiave

- extremal properties
- Dirac’s impulse
- linear systems
- transfer function

- Accesso libero

Anti–Periodic Solutions for Clifford–Valued High–Order Hopfield Neural Networks with State–Dependent and Leakage Delays

Pagine: 83 - 98

#### Astratto

A class of Clifford-valued high-order Hopfield neural networks (HHNNs) with state-dependent and leakage delays is considered. First, by using a continuation theorem of coincidence degree theory and the Wirtinger inequality, we obtain the existence of anti-periodic solutions of the networks considered. Then, by using the proof by contradiction, we obtain the global exponential stability of the anti-periodic solutions. Finally, two numerical examples are given to illustrate the feasibility of our results.

#### Parole chiave

- Clifford-valued high-order Hopfield neural network
- anti-periodic solution
- coincidence degree
- time-varying delay

- Accesso libero

Finding Robust Transfer Features for Unsupervised Domain Adaptation

Pagine: 99 - 112

#### Astratto

An insufficient number or lack of training samples is a bottleneck in traditional machine learning and object recognition. Recently, unsupervised domain adaptation has been proposed and then widely applied for cross-domain object recognition, which can utilize the labeled samples from a source domain to improve the classification performance in a target domain where no labeled sample is available. The two domains have the same feature and label spaces but different distributions. Most existing approaches aim to learn new representations of samples in source and target domains by reducing the distribution discrepancy between domains while maximizing the covariance of all samples. However, they ignore subspace discrimination, which is essential for classification. Recently, some approaches have incorporated discriminative information of source samples, but the learned space tends to be overfitted on these samples, because they do not consider the structure information of target samples. Therefore, we propose a feature reduction approach to learn robust transfer features for reducing the distribution discrepancy between domains and preserving discriminative information of the source domain and the local structure of the target domain. Experimental results on several well-known cross-domain datasets show that the proposed method outperforms state-of-the-art techniques in most cases.

#### Parole chiave

- unsupervised domain adaptation
- feature reduction
- generalized eigenvalue decomposition
- object recognition

- Accesso libero

A Second–Order TV–Based Coupling Model and an ADMM Algorithm for MR Image Reconstruction

Pagine: 113 - 122

#### Astratto

Motivated by ideas from two-step models and combining second-order TV regularization in the LLT model, we propose a coupling model for MR image reconstruction. By applying the variables splitting technique, the split Bregman iterative scheme, and the alternating minimization method twice, we can divide the proposed model into several subproblems only related to second-order PDEs so as to avoid solving a fourth-order PDE. The solution of every subproblem is based on generalized shrinkage formulas, the shrink operator or the diagonalization technique of the Fourier transform, and hence can be obtained very easily. By means of the Barzilai–Borwein step size selection scheme, an ADMM type algorithm is proposed to solve the equations underlying the proposed model. The results of numerical implementation demonstrate the feasibility and effectiveness of the proposed model and algorithm.

#### Parole chiave

- MRI reconstruction
- LLT model
- LOT model
- coupling model
- ADMM
- split Bregman
- wavelet transform

- Accesso libero

Curve Skeleton Extraction Via K –Nearest–Neighbors Based Contraction

Pagine: 123 - 132

#### Astratto

We propose a skeletonization algorithm that is based on an iterative points contraction. We make an observation that the local center that is obtained via optimizing the sum of the distance to

#### Parole chiave

- curve skeleton
- points contraction
- point cloud
- nearest neighbors

#### Astratto

Data clustering is one of the most popular methods of data mining and cluster analysis. The goal of clustering algorithms is to partition a data set into a specific number of clusters for compressing or summarizing original values. There are a variety of clustering algorithms available in the related literature. However, the research on the clustering of data parametrized by unit quaternions, which are commonly used to represent 3D rotations, is limited. In this paper we present a quaternion clustering methodology including an algorithm proposal for quaternion based k-means along with quaternion clustering quality measures provided by an enhancement of known indices and an automated procedure of optimal cluster number selection. The validity of the proposed framework has been tested in experiments performed on generated and real data, including human gait sequences recorded using a motion capture technique.

#### Parole chiave

- data clustering
- quaternions data processing
- human gait data processing

- Accesso libero

An Algorithm for Quaternion–Based 3D Rotation

Pagine: 149 - 160

#### Astratto

In this work a new algorithm for quaternion-based spatial rotation is presented which reduces the number of underlying real multiplications. The performing of a quaternion-based rotation using a rotation matrix takes 15 ordinary multiplications, 6 trivial multiplications by 2 (left-shifts), 21 additions, and 4 squarings of real numbers, while the proposed algorithm can compute the same result in only 14 real multiplications (or multipliers—in a hardware implementation case), 43 additions, 4 right-shifts (multiplications by 1/4), and 3 left-shifts (multiplications by 2).

#### Parole chiave

- quaternions
- space rotation
- design of algorithms

- Accesso libero

Heuristic Search of Exact Biclusters in Binary Data

Pagine: 161 - 171

#### Astratto

The biclustering of two-dimensional homogeneous data consists in finding a subset of rows and a subset of columns whose intersection provides a set of cells whose values fulfil a specified condition. Usually it is defined as equality or comparability. One of the presented approaches is based on the model of Boolean reasoning, in which finding biclusters in binary or discrete data comes down to the problem of finding prime implicants of some Boolean function. Due to the high computational complexity of this task, the application of some heuristics should be considered. In the paper, a modification of the well-known Johnson strategy for prime implicant approximation induction is presented, which is necessary for the biclustering problem. The new method is applied to artificial and biomedical datasets.

#### Parole chiave

- biclustering
- Boolean reasoning
- prime implicant approximation
- biomedical data analysis
- Johnson heuristic

- Accesso libero

A Genetic Algorithm for the Maximum 2–Packing Set Problem

Pagine: 173 - 184

#### Astratto

Given an undirected connected graph

#### Parole chiave

- maximum 2-packing set
- genetic algorithms
- graph algorithms

- Accesso libero

A Decomposition Approach to Type 2 Interval Arithmetic

Pagine: 185 - 201

#### Astratto

The classic interval has precise borders

#### Parole chiave

- multi-dimensional RDM interval arithmetic
- type 2 interval arithmetic
- RDM type 2 interval arithmetic
- decomposition type 2 interval arithmetic
- interval arithmetic