非线性交互系统模型预测控制器的多模型自适应控制策略

V. Ravi, T. Thyagarajan, M. Monika Darshini
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引用次数: 27

摘要

模型预测控制(MPC)已成为化工过程中先进多变量控制的主要形式。本文的目的是为多变量MPC引入一种多模型自适应控制策略。方法是设计多个线性MPC控制器。该策略在广泛的操作水平范围内保持多个线性MPC控制器的性能。一个重要的贡献是该策略结合了几个多个线性MPC控制器,每个控制器都有自己的线性状态空间模型,描述特定操作级别的过程动态。根据测量过程变量的当前值,选择线性MPC控制器输出中的一个作为多模型自适应控制器的输出。采用遗传算法获得了线性MPC控制器的整定参数。通过计算机仿真研究了双罐锥形相互作用系统(TTCIS)中MPC控制器多模型自适应策略的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Multiple Model Adaptive Control Strategy for Model Predictive Controller for Interacting Non Linear Systems
Model predictive control (MPC) has become the leading form of advanced multivariable control in the chemical process industry. The objective of this work is to introduce a multiple model adaptive control strategy for multivariable MPC. The method of approach is to design multiple linear MPC controllers. This strategy maintains performance of multiple linear MPC controllers over a wide range of operating levels. One important contribution is that the strategy combines several multiple linear MPC controllers, each with their own linear state space model describing process dynamics at a specific level of operation. One of the linear MPC controller output is selected as multiple model adaptive controller's output based on the current value of the measured process variable. The tuning parameters for the linear MPC controller are obtained using Genetic Algorithm (GA). The capabilities of the multiple model adaptive strategy for MPC controller are investigated on Two Tank Conical Interacting System (TTCIS) through computer simulation.
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