李旭生, 牛宏, 陶金梅. 基于深度学习的非线性广义预测控制[J]. 信息与控制, 2023, 52(2): 202-210. DOI: 10.13976/j.cnki.xk.2023.2064
引用本文: 李旭生, 牛宏, 陶金梅. 基于深度学习的非线性广义预测控制[J]. 信息与控制, 2023, 52(2): 202-210. DOI: 10.13976/j.cnki.xk.2023.2064
LI Xusheng, NIU Hong, TAO Jinmei. Nonlinear Generalized Predictive Control Based on Deep Learning[J]. INFORMATION AND CONTROL, 2023, 52(2): 202-210. DOI: 10.13976/j.cnki.xk.2023.2064
Citation: LI Xusheng, NIU Hong, TAO Jinmei. Nonlinear Generalized Predictive Control Based on Deep Learning[J]. INFORMATION AND CONTROL, 2023, 52(2): 202-210. DOI: 10.13976/j.cnki.xk.2023.2064

基于深度学习的非线性广义预测控制

Nonlinear Generalized Predictive Control Based on Deep Learning

  • 摘要: 针对一类离散时间单输入-单输出(single-input single-output,SISO)非线性动态系统,将非线性切换控制与基于深度学习的未建模动态估计方法相结合,提出了一种新的非线性广义预测控制方法。该方法针对未建模动态的未知增量,通过使用基于深度学习技术的长短记忆神经网络(long short-term memory,LSTM)进行预估,设计了一种带有未建模动态增量估计的非线性广义预测控制器,增强控制性能。对所提的控制算法进行了稳定性和收敛性分析,最后通过数值仿真实验验证了所提方法的有效性。

     

    Abstract: In this study, we propose a new nonlinear generalized predictive control method for a class of discrete-time single-input single-output nonlinear dynamic systems. For this, we combine nonlinear switching control and unmodeled dynamics estimation methods based on deep learning technology. In the new algorithm, we use long short-term memory neural networks to estimate the unknown increment of unmodeled dynamics based on deep learning technology. We then enhance control performance by designing a nonlinear generalized predictive controller with unmodeled dynamic increment estimation. We also analyze the stability and convergence of the proposed control algorithm. The results of our simulation experiments verified the effectiveness of the proposed method.

     

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