Conference Program of WCICA 2012

Conference Program WCICA 2012
◮ SaA07-6 15:10–15:30
An Exemplar Test Problem on Parameter Convergence Analysis of
Temporal Difference Algorithms, pp.2925–2930
Brown, Martin
Tutsoy, Onder
The Univ. of Manchester
The Univ. of Manchester
Reinforcement learning techniques have been developed to solve difficult
learning control problems having small amount of a priori knowledge
about the system dynamics. In this paper, a simple unstable
exemplar test problem is proposed to investigate issues in parametric
convergence of the value function. A specific closed-form solution
for the value function is determined which has a polynomial form. It is
proved that the temporal difference error introduces a null space associated
with the finite horizon basis function during the control trajectory.
The learning problem can be only non-singular if the termination is handled
correctly, and a number of possible solutions are introduced. This
result was only revealed because of the derived closed form solution
for the value function.
SaA08 13:30–15:30 Room 310
Award: Theory (I)
Chair: Chen, Ben M.
Co-Chair: Zhang, Huanshui
National Univ. of Singapore
Shandong Univ.
◮ SaA08-1 13:30–13:50
A New Optimal Control Method for Discrete-Time Nonlinear Systems
with Approximation Error, pp.185–190
Wei, Qinglai
Liu, Derong
Inst. of Automation
CASIA
In this paper, a new optimal control method is proposed for discretetime
nonlinear systems based on iterative adaptive dynamic programming
(ADP) algorithm. The method can obtain an iterative control law,
and make the iterative performance index function converge to optimal.
In each iteration, if the iterative control law and iterative performance
index function are not accurately obtained, then the convergence conditions
of the iterative ADP algorithm are used. According to the convergence
conditions, the iterative performance index functions can converge
to a small neighborhood of the optimal performance index function.
Finally, a simulation example is given to illustrate the performance
of the proposed method.
◮ SaA08-2 13:50–14:10
Emergence of Flocks with Local Interactions, pp.3515–3519
Chen, Ge
Liu, Zhi-Xin
Chinese Acad. of Sci.
Chinese Acad. of Sci.
In a recent paper, Cucker and Smale proposed a multi-agent model
to study the flocking behavior, where they assume that all agents can
interact with all other agents. Though this model attracted much attention
of researchers, the global interactions used in that paper changed
the nature of multi-agent systems. In this paper we will investigate a
new flocking model, in which the global interactions between agents
are replaced by local interaction functions. We will provide necessary
conditions and sufficient conditions for consensus.
◮ SaA08-3 14:10–14:30
Cooperative Robust Output Regulation of Linear Uncertain Multi-Agent
Systems, pp.1299–1304
Su, Youfeng
Huang, Jie
The Chinese Univ. of Hong Kong
Chinese Univ. of Hong Kong, China
The cooperative output regulation of a linear multi-agent system has
been studied recently for the case where the system uncertain parameters
vary in a sufficiently small neighborhood of their nominal value.
This problem is handled by an internal model design which converts
the problem into a simultaneously eigenvalue placement problem of an
augmented multi-agent system. In this paper, we further consider the
cooperative robust output regulation problem for a class of minimum
phase linear multi-agent systems in the sense that the controller allows
the system uncertain parameters to vary in an arbitrarily prescribed
compact subset. For this purpose, we introduce a new type of internal
model that allows the cooperative robust output regulation problem
of the given plant to be converted into a robust stabilization problem of
an augmented multi-agent system. We then solve our problem by combining
a simultaneous high gain state feedback control technique and
a distributed high gain observer technique. A special case of our result
leads to the solution of the leader-following robust consensus problem
for a large class of uncertain multi-agent systems.
◮ SaA08-4 14:30–14:50
Stochastic Approximation Based PCA and Its Application to Identification
of EIV Systems, pp.3276–3280
Zhao, Wen-Xiao
Chen, Han-Fu
Chinese Acad. of Sci.
Chinese Acad. of Sci.
The stochastic approximation based principal component analysis (S-
APCA) algorithm is introduced to recursively estimate the eigenvectors
and the corresponding eigenvalues of a symmetric matrix A based on
observations A k = A+ε k with ε k → 0 as k → ∞. The estimates are
strongly consistent. The SAPCA algorithm is then applied to identifying
the matrix coefficients of the multivariate errors-in-variables (EIV) systems,
and the estimates are also strongly consistent. The performance
of SAPCA algorithm is testified by a simulation example.
◮ SaA08-5 14:50–15:10
Reaching Optimal Consensus for Multi-agent Systems Based on Approximate
Projection, pp.2794–2800
Lou, Youcheng Acad. of Mathematics & Sys. Sci., Chinese Acad.
of Sci.
Shi, Guodong
Johansson, Karl Henrik
Hong, Yiguang
KTH Royal Inst. of Tech.
Royal Inst. of Tech.
Chinese Acad. of Sci.
In this paper, we propose an approximately projected consensus algorithm
for a multi-agent system to cooperatively compute the intersection
of several convex sets, each of which is known only to a particular n-
ode. Instead of assuming the exact convex projection, we allow each
node to just compute an approximate projection. The communication
graph is directed and time-varying, and nodes can only exchange information
via averaging among local view. We present sufficient and/or
necessary conditions for the considered algorithm on how much projection
accuracy is required to ensure a global consensus within the
intersection set, under the assumption that the communication graph
is uniformly jointly strongly connected. A numerical example indicates
that the approximately projected consensus algorithm achieves better
performance than the exact projected consensus algorithm. The results
add the understanding of the fundamentals of distributed convex
intersection computation.
◮ SaA08-6 15:10–15:30
Quantum Multi-Channel Decoupling, pp.2286–2290
Liu, Pei-Lan
Li, Jr-Shin
Tarn, Tzyh-Jong
Washington Univ. in St. Louis
Washington Univ. in St. Louis
Washington Univ., St. Louis, MO
We study multiple-input multiple-output (MIMO) quantum systems and
explore quantum decoupling techniques that can be used to facilitate
the design of quantum networks. We provide a quantum state feedback
control law to change the structure of a quantum system in order
to decouple the multiple channels. We show that an MIMO quantum
system can be decoupled as an aggregate of n separated single-input
single-output (SISO) quantum systems from an input-output point of
view. We also discuss the conditions under which quantum decoupling
can be achieved. We illustrate our method with several qubit and atomic
systems.
SaA09 13:30–15:30 Room 311A
Award: Application (I)
Chair: Chen, Jie
Co-Chair: Chen, Hong
Beijing Inst. of Tech.
Jilin Univ.
◮ SaA09-1 13:30–13:50
Bilateral Teleoperation of Force/Motion for aRobotic Manipulator with
Communication RandomDelays, pp.3915–3920
Kang, Yu
Univ. of Sci. & Tech. of China
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