Multivariable ORVFL Network Adaptive Predictive Control Based on ISSA

doi:10.12422/j.issn.1672-6952.2023.01.014

Journal of Liaoning Petrochemical University ›› 2023, Vol. 43 ›› Issue (1): 80-88.DOI: 10.12422/j.issn.1672-6952.2023.01.014

• Information and Control Engineering • Previous Articles Next Articles

Multivariable ORVFL Network Adaptive Predictive Control Based on ISSA

Xinyu Na(), Huapeng Yu, Xin Jin(), Yue Wang

School of Information and Control Engineering，Liaoning Petrochemical University，Fushun Liaoning 113001，China

Received:2021-07-24 Revised:2021-09-19 Published:2023-02-25 Online:2023-03-13
Contact: Xin Jin

基于ISSA的多变量ORVFL网络自适应预测控制

那新宇(), 余华鹏, 金鑫(), 王越

辽宁石油化工大学信息与控制工程学院，辽宁抚顺 113001

通讯作者: 金鑫
作者简介:那新宇（1989⁃），男，硕士研究生，从事工业过程先进控制研究；E⁃mail：540633884@qq.com。
基金资助:
国家自然科学基金面上项目(62073158)

Abstract

Abstract:

For the MIMO nonlinear systems, a multivariable ORVFL neural network adaptive predictive control algorithm based on Improved Sparrow Search Algorithm was proposed in this paper. The algorithm uses the ORVFL network to approximate the nonlinear system model, and applies to the multi?step prediction of the system process. In order to improve the performance of the Sparrow Search Algorithm, the algorithm is used to optimize the system performance index online and solve the optimal control law of each sampling period. The results show that the algorithm has good control performance and good anti?model mismatch ability.

Key words: Model predictive control, Sparrow search algorithm, Nonlinear system, Neural networks

摘要：

针对多输入多输出（Multiple?Input Multiple?Output, MIMO）的非线性系统，提出了一种基于改进的麻雀搜索算法（Improved Sparrow Search Algorithm, ISSA）的在线序列随机权值网络( Online Random Vector Functional?Link Net, ORVFL)自适应预测控制算法(ISSA?MPC)。该算法采用ORVFL网络逼近非线性系统模型，并用于系统过程的多步预测。为了提高麻雀搜索算法的性能，使用该算法对系统性能指标进行了在线优化，求解了每一个采样周期的最优控制律。结果表明，该算法控制性能良好并具有较好的抗模型失配能力。

关键词: 模型预测控制, 麻雀搜索算法, 非线性系统, 神经网络

CLC Number:

TP273

Xinyu Na, Huapeng Yu, Xin Jin, Yue Wang. Multivariable ORVFL Network Adaptive Predictive Control Based on ISSA[J]. Journal of Liaoning Petrochemical University, 2023, 43(1): 80-88.

那新宇, 余华鹏, 金鑫, 王越. 基于ISSA的多变量ORVFL网络自适应预测控制[J]. 辽宁石油化工大学学报, 2023, 43(1): 80-88.

Figures/Tables 15

操作参数	数值
q₁/(mL $⋅$ s^-1)	16.60
q₂/(mL $⋅$ s^-1)	0.55
q₃/(mL $⋅$ s^-1)	15.60
W_a₁/mol	0.003 00
W_a₂/mol	-0.030 00
W_a₃/mol	-0.003 00
W_b₁/mol	0
W_b₂/mol	0.030 00
W_b₃/mol	0.000 05
C₀/(mL•cm^-1•s^-1)	8.75
h/cm	14.0
pk₁	6.35
pk₂	10.25
pH₄	7.0
A/cm²	207
T/s	15

操作参数	数值
q₁/(mL $⋅$ s^-1)	16.60
q₂/(mL $⋅$ s^-1)	0.55
q₃/(mL $⋅$ s^-1)	15.60
W_a₁/mol	0.003 00
W_a₂/mol	-0.030 00
W_a₃/mol	-0.003 00
W_b₁/mol	0
W_b₂/mol	0.030 00
W_b₃/mol	0.000 05
C₀/(mL•cm^-1•s^-1)	8.75
h/cm	14.0
pk₁	6.35
pk₂	10.25
pH₄	7.0
A/cm²	207
T/s	15

模型	液位根均方误差/cm	pH根均方误差
RVFL网络	0.005	0.300
BP网络	0.018	0.350

模型	液位根均方误差/cm	pH根均方误差
RVFL网络	0.039	1.340
ORVFL网络	0.008	0.470

算法	液位根均方误差/cm	pH根均方误差
ISSA⁃MPC	0.271	0.43
SSA⁃MPC	0.274	0.45
PSO⁃MPC	0.287	0.46

模型	液位根均方误差/cm	pH根均方误差
RVFL网络	0.243	0.43
ORVFL网络	0.273	0.54

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Multivariable ORVFL Network Adaptive Predictive Control Based on ISSA

基于ISSA的多变量ORVFL网络自适应预测控制

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