Conference Program of WCICA 2012

WCICA 2012
Book of Abstracts: Friday Sessions
FrB08 15:50–17:50 Room 310
Invited Session: Data-driven Control System Design and Analysis
Chair: Li, Shaoyuan
Co-Chair: Li, Kang
Shanghai Jiao Tong Univ.
Queen’s Univ. Belfast
◮ FrB08-1 15:50–16:10
Model-Based Predictive Control for Distributed Parameter Systems
Based on Local Modeling Approach, pp.1287–1292
Wang, Mengling
Zhang, Yang
Shi, Hongbo
East China Univ. of Sci. & Tech.
Shanghai Urban & Rural Construction &
Transportation Committee
East China Univ. of Sci. & Tech.
In this paper, a model-based predictive control strategy based on local
modeling approach is proposed for distributed parameter system.
As the partial differential equation (PDE) descriptions of the systems
are unknown, the local modeling approach is used to estimate the dynamics
of the system based on the input-output data. Based on finite
local models, each local controller output can obtain through minimizing
the local optimization objective. The global controlled outputs can
be solved by linear programming where the deviations of the global s-
patial temporal outputs from their spatial set points over the prediction
horizon are considered as the optimal objective. The accuracy and efficiency
of the proposed methodologies are tested in the cross-flow heat
exchanger.
◮ FrB08-2 16:10–16:30
Nonlinear Dynamic Process Monitoring Based on DLLE-SVDD,
pp.3131–3136
Ma, Yuxin
Wang, Mengling
Shi, Hongbo
East China Univ. of Sci. & Tech.
East China Univ. of Sci. & Tech.
East China Univ. of Sci. & Tech.
A novel process monitoring method for dynamic nonlinear industrial
processes is proposed by combining dynamic Locally Linear Embedding
with Support Vector Data Description. Firstly, the data matrix is
augmented taking correlation of the samples into consideration. Then,
LLE manifold learning algorithm is performed for nonlinear dimensionality
reduction and feature extraction. The mapping matrix from data
space to feature space was calculated by using local linear regression
which guarantees the real-time property. Next, in order to avoid the influence
of noise and disturbance on the traditional statistics, the fault
detection model is obtained based on SVDD in the feature space, in
which a corresponding monitoring index and its control limit are determined.
Finally, the feasibility and efficiency of the proposed method are
shown through two simulation examples.
◮ FrB08-3 16:30–16:50
Fuzzy Clustering Based Spatiotemporal Fuzzy Logic Controller Design,
pp.3167–3172
Zhang, Xianxia
LI, Jiajia
Jiang, Ye
Su, Baili
Qi, Chenkun
Zou, Tao
Shanghai Univ.
Shanghai Univ.
Shanghai Univ.
Qufu Normal Univ.
Shanghai Jiao Tong Univ.
Zhejiang Univ. of Tech.
Three-dimensional fuzzy logic controller (3-D FLC) is a novel FLC developed
for spatially-distributed parameter systems. In this study, we
concentrated on the data-driven based 3-D FLC design. Firstly, an initial
rule-base of 3-D FLC is learned by fuzzy c-means algorithm from
spatial-temporal data set. Then, the rule-base is reduced by using
distance-based similarity measure to check similar fuzzy sets and similar
rules. Finally, the parameters are refined by a gradient-descent
approach. A catalytic packed-bed reactor is taken as an application to
demonstrate the effectiveness of the proposed 3-D FLC design method.
◮ FrB08-4 16:50–17:10
A Regression Approach to LS-SVM and Sparse Realization based on
Fast Subset Selection, pp.612–617
Zhang, Jingjing
Queen’s Univ. Belfast
Li, Kang
Queen’s Univ. Belfast
The Least Squares Support Vector Machine (LS-SVM) is a modified
SVM with a ridge regression cost function and equality constraints. It
has been successfully applied in many classification problems. But, the
common issue for LS-SVM is that it lacks sparseness, which is a serious
drawback in its applications. To tackle this problem, a fast approach
is proposed in this paper for developing sparse LS-SVM. First, a new
regression solution is proposed for the LS-SVM which optimizes the
same objective function for the conventional solution. Based on this,
a new subset selection method is then adopted to realize the sparse
approximation. Simulation results on different benchmark datasets i.e.
Checkerboard, two Gaussian datasets, show that the proposed solution
can achieve better objective value than conventional LS-SVM, and
the proposed approach can achieve a more sparse LS-SVM than the
conventional LS-SVM while provide comparable predictive classification
accuracy. Additionally, the computational complexity is significantly
decreased.
◮ FrB08-5 17:10–17:30
Input Selection for Dynamic RBF Models in Process Monitoring,
pp.3037–3042
LIU, Xueqin
Li, Kang
Li, Shaoyuan
Fei, Minrui
Queen’s Univ. Belfast
Queen’s Univ. Belfast
Shanghai Jiao Tong Univ.
Shanghai Univ.
This paper investigates the monitoring of continuous processes using
dynamic nonlinear principal component analysis (NPCA). Previously, it
was shown that integrating the RBF networks with principal curves significantly
had increased the sensitivity of fault detection for nonlinear
processes. Despite this, the previous method may not function well for
processes which exhibit strong dynamic characteristics. An effective
method of capturing dynamic behaviour is to consider a time-lagged
data extension. However, the augmented data matrix may lead to the
inclusion of a large number of variables in the RBF network input, and
hence increase the computational load and network complexity. To prevent
this, an input selection scheme, based on the nonlinear dynamic
relationship underlying the process variables, is introduced. This selects
the most important and relevant time-lagged variables before constructing
the RBF network model. Consequently, a modified dynamic
NPCA approach is now proposed. The advantages of this improvement
are demonstrated using a benchmark simulation example from the literature.
FrB09 15:50–17:50 Room 311A
Invited Session: Nonlinear and Networked Systems
Chair: Wang, Yuan
Co-Chair: Xie, Lihua
Florida Atlantic Univ.
Nanyang Technological Univ.
◮ FrB09-1 15:50–16:10
Persistent awareness coverage for networked mobile sensors with
awareness loss, pp.4291–4296
Song, Cheng Univ. of Sci. & Tech. of China & City Univ. of Hong
Kong Joint Advanced Research Center
Feng, Gang
WANG, Yong
City Univ. of Hong Kong
Univ. of Sci. & Tech. of China
In this paper persistent awareness coverage problem for mobile sensor
networks with awareness loss is addressed, where the goal is to cover
the mission domain periodically and guarantee full awareness coverage
of a finite set of points of interest. A closed path for mobile sensors
is designed so that the persistent awareness coverage task can be accomplished.
Then, it is proved that the persistent awareness coverage
task can be accomplished for a given network of mobile sensors if and
only if there exists a solution to a set of linear inequalities.
◮ FrB09-2 16:10–16:30
Control of Discrete-Time Periodic Linear Systems with Input Saturation
via Multi-Step Periodic Invariant Set, pp.1372–1377
Zhou, Bin
Li, Dewei
Harbin Inst. of Tech.
Shanghai Jiaotong Univ.
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