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A novel active disturbance rejection-based control strategy for a gun control system
Qiang Gao,Zhan Sun,Guolai Yang,Runmin Hou,Li Wang,Yuan-Long Hou 대한기계학회 2012 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.26 No.12
To compensate for the nonlinearity and to achieve finely-tuned tracking accuracy of a gun control system driven by an AC machine,an improved active disturbance rejection control (IADRC) strategy with neural network embedding (NN-IADRC) is developed in this paper. The proposed IADRC, which has amnestic memory effects, can be regarded as an extension of the conventional ADRC (CADRC),making it a special case of the IADRC. To further attenuate the dependence on system models and enhance the disturbance rejection capacities of the IADRC strategy, an on-line NN-based optimum updating approach is also developed in this paper. Finally, a series of experiments are conducted on the semi-physical simulation platform to estimate the performance of the control system and the effects of the memory factor on the system. The experimental results confirm that the proposed NN-IADRC is highly robust. The results also confirm that it performs more excellently than the CADRC and that its fine tuning has attained tracking accuracy.
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On Generating Fuzzy Systems based on Pareto Multi-objective Cooperative Coevolutionary Algorithm
Zong-Yi Xing,Yong Zhang,Yuan-Long Hou,Li-Min Jia 대한전기학회 2007 International Journal of Control, Automation, and Vol.5 No.4
An approach to construct multiple interpretable and precise fuzzy systems based on the Pareto Multi-objective Cooperative Coevolutionary Algorithm (PMOCCA) is proposed in this paper. First, a modified fuzzy clustering algorithm is used to construct antecedents of fuzzy system, and consequents are identified separately to reduce computational burden. Then, the PMOCCA and the interpretability-driven simplification techniques are executed to optimize the initial fuzzy system with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets; thus both the precision and the interpretability of the fuzzy systems are improved. In order to select the best individuals from each species, we generalize the NSGA-Ⅱ algorithm from one species to multi-species, and propose a new non-dominated sorting technique and collaboration mechanism for cooperative coevolutionary algorithm. Finally, the proposed approach is applied to two benchmark problems, and the results show its validity.
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