Hybrid Soft Computing Approach to Identification and Control of Nonlinear Systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@PhdThesis{YuehuiChen:thesis,
  author =       "Yuehui Chen",
  title =        "Hybrid Soft Computing Approach to Identification and
                 Control of Nonlinear Systems",
  school =       "Department of Computer Science, Kumamoto University",
  year =         "2001",
  address =      "Japan",
  month =        mar,
  email =        "CHEN Yuehui ",
  keywords =     "genetic algorithms, genetic programming, PIPE
                 Algorithm",
  URL =          "http://www31.freeweb.ne.jp/computer/chen_yh/thesis.pdf.001",
  URL =          "http://www31.freeweb.ne.jp/computer/chen_yh/thesis.pdf.002",
  URL =          "http://www31.freeweb.ne.jp/computer/chen_yh/thesis.pdf.003",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/yuehui.chen/YuehuiChenThesis.pdf",
  size =         "182 pages",
  abstract =     "

                 Recently, complex industrial plants such as mobile
                 robots, flexible manufacturing system etc., are often
                 required to perform complex tasks with high precision
                 under ill-defined conditions, and conventional control
                 techniques may not be quite effective in these systems.
                 Soft computing approaches are some computational models
                 inspired by the simulated human and/or natural
                 intelligence, and includes fuzzy logic, artificial
                 neural networks, genetic and evolutionary algorithms.
                 There have been many successful researches for the
                 identification and control of nonlinear systems by
                 using various soft computing techniques with different
                 computational architectures. The experiences gained
                 over the past decade indicate that it can be more
                 effective to use the various soft computing approaches
                 in a combined manner. But there is no common
                 recognition about how to combine them in an effective
                 way, and a unified framework of hybrid soft computing
                 models in which various soft computing models can be
                 developed, evolved and evaluated has not been
                 established.",
  abstract =     "In this research, a unified framework of hybrid soft
                 computing models is proposed and it is applied to the
                 identification and control of industrial plants. First,
                 a scheme for identification and control of nonlinear
                 systems using probabilistic incremental program
                 evolution algorithm (PIPE) is proposed. Based on the
                 modified PIPE (MPIPE) and some parameter tuning
                 strategies, a unified framework of hybrid soft
                 computing models is constructed for the identification
                 of nonlinear systems, and then the hybrid soft
                 computing based controller design principles and
                 methods are developed. As an application, the proposed
                 methods are applied to the identification and control
                 of the thrust force (cutting torque) in the drilling
                 system.

                 This dissertation consists of six chapters as
                 follows:

                 In Chapter 1, the background and the current state of
                 soft computing researches, and the purpose of the
                 thesis are described briefly.

                 In chapter 2, the basic elements of soft computing
                 technique are discussed, including the evolutionary
                 algorithms and random search algorithm, neural networks
                 and fuzzy logic systems. The problems and disadvantages
                 of the soft computing approaches are pointed out and
                 their modification and improvements are given.",
  abstract =     "In chapter 3, in order to cope with the problems of
                 architecture selection and parameter optimization of
                 soft computing models simultaneously, a unified
                 framework is constructed in which various hybrid soft
                 computing models can be developed, evolved and
                 evaluated. In the proposed method, the architecture of
                 the hybrid soft computing models is evolved by MPIPE
                 and the parameters used in soft computing models are
                 optimized by hybrid or non-hybrid parameter
                 optimization strategy, respectively. The effectiveness
                 of the proposed method has been confirmed by simulation
                 studies.

                 In chapter 4, some common soft computing based
                 controller design principles are discussed briefly.
                 Then a new control scheme for nonlinear systems based
                 on PIPE algorithm is proposed. Finally, based on the
                 basis function networks a unified framework for control
                 of affine and non-affine nonlinear systems is presented
                 with guaranteed stability analysis. The simulation and
                 experimental results show the effectiveness of the
                 proposed controller.",
  abstract =     "In chapter 5, the soft computing based identification
                 and control schemes developed in chapter 3 and 4 are
                 applied to the drilling system. In order to control
                 thrust force (cutting torque) in the process of drill,
                 a number of thrust force (cutting torque)
                 identification methods are developed, and then thrust
                 force (cutting torque) soft model based neural control
                 scheme are presented. Real time implementations show
                 that the soft computing approaches based control
                 schemes are efficient and effective.

                 Finally in chapter 6, the results obtained in previous
                 chapter are summarized, and some topics for future
                 research in this direction are given.

                 In this research, the applicability of PIPE algorithm
                 to identification and control of nonlinear systems is
                 confirmed. Based on the MPIPE and some parameter tuning
                 strategies, a unified framework of hybrid soft
                 computing models is constructed. Simulation and
                 experiments results for the identification and control
                 of nonlinear systems show the effectiveness of the
                 proposed methods. The key point of the research is that
                 various soft computing based identification and control
                 schemes can be re-evaluated in a unified framework and
                 then it is valuable for the proposed approach in order
                 to construct the unified soft computing theories and
                 applications.",
  notes =        "my PDF reader barfed 20 July 2001. url_2 ok",
}

Genetic Programming entries for Yuehui Chen

Citations