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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.",
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.",
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.",
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.",
Genetic Programming entries for Yuehui Chen