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An improved predictive control model for stochastic max-plus-linear systems

机译:随机MAX-PLUS-LINEAR系统的改进预测控制模型

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In order to improve the robustness and stability of the model predictive control system, this paper research the problem by combination of stochastic predictive control and max-plus theory. Based on the analysis of the stochastic predictive control model, the maximum plus stochastic predictive control model is constructed, which is improved by the max-plus algebraic theory. The superiority of the maximum plus stochastic predictive control model is verified by simulation and experiment. The max-plus algebra is an algorithm which is suitable for noise processing of input signal, which can stabilize the input of the control system. The disadvantage of stochastic predictive control model is that the input signal is subjected to random disturbance in the external environment, max-plus algebraic theory can better compensate for the defect. The simulation results show that the stochastic predictive control model has significant advantages in accuracy, stability and robustness. (C) 2019 Elsevier Ltd. All rights reserved.
机译:为了提高模型预测控制系统的鲁棒性和稳定性,本文通过随机预测控制和最大加上理论的组合研究了问题。基于对随机预测控制模型的分析,构造了最大加随机预测控制模型,由MAX-PLUS代数理论得到改善。通过模拟和实验验证了最大加速预测控制模型的优越性。 MAX-PLUS代数是一种适用于输入信号的噪声处理的算法,其可以稳定控制系统的输入。随机预测控制模型的缺点是输入信号在外部环境中进行随机干扰,MAX-PLUS代数理论可以更好地补偿缺陷。仿真结果表明,随机预测控制模型的准确性,稳定性和鲁棒性具有显着的优势。 (c)2019年elestvier有限公司保留所有权利。

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