一种新的无模型自适应控制模型参数整定方法

doi:10.11949/0438-1157.20190356

摘要/Abstract

摘要：

无模型自适应控制（MFAC）有四个模型参数，现有研究认为其无关联，且全程守恒。通过虚拟系统在初始时刻的状态，基于第一时刻输出值应与期望值接近的原则，采用遗传算法找到了参数关联，从而将四参数问题转化成单参数问题；并在控制过程中，依系统输出与期望差异自动改变步长因子取值，将其动态化，不但加速了初期计算，还避免了收敛时可能出现的超调或振荡；由此以参数关联和动态化为切入，改进了现有紧格式动态线性化MFAC方法，使其更为简捷和准确。某炼油单元3参数调优案例表明新方法可行，较老方法实现系统最大增益的参数调整次数由33次减少到14次，最大增益由413.4万元/年提高到542.9万元/年。

关键词: 无模型自适应控制, 参数, 操作优化, 遗传算法, 过程系统, 过程控制

Abstract:

Model-free adaptive control (MFAC) has four model parameters, and existing studies consider it to be uncorrelated and conserved throughout. The relationship among 4 parameters is found out in this paper by assuming an initial state of the controlled system at the initial moment, based on the rule of the first moment output value should be close to the target value with genetic algorithm (GA), making the issue of 4 parameters to be simplified into the one of single parameter. Further, an automatic estimation method on step-length factor is suggested according to whether the absolute value of difference between output value and target value is less than a default value, thus it is regarded as a parameter with characteristic of time-variable and its maximum value is also enlarged into positive infinity from 1. The proposed two changes improve the present MFAC greatly, resulting in a faster calculation speed at the early control stage as well as avoiding overshoot and vibration at the convergence stage. A case application in a unit of oil refinery shows that the improved MFAC only needs 14 iterations for reaching the optimal target, and the system’s maximum added-benefit (MAD) can reach 5.43 million CNY/a. The number of parameter adjustments for the maximum gain of the system is reduced from 33 to 14 times, and the maximum gain is increased from 4.134 million CNY/a to 5.429 million CNY/a.

Key words: model-free adaptive control, parameter, operation optimization, genetic algorithm, process systems, process control

中图分类号:

TP 273

宋泽雨, 李国庆, 刘凌轩. 一种新的无模型自适应控制模型参数整定方法[J]. 化工学报, 2019, 70(9): 3430-3440.

Zeyu SONG, Guoqing LI, Lingxuan LIU. New determination method of parameters for model-free adaptive control[J]. CIESC Journal, 2019, 70(9): 3430-3440.

图/表 17

图1 DDC基本思路[1]

Fig1 Configuration of DDC theory [1]

图2 多输入多输出控制系统

Fig.2 Sketch of control system with MIMO

图3 步长因子动态化步骤

Fig.3 Flow chart of dynamization of parameter ρ

图4 基于模型参数关联化和动态化的MIMO系统CFDL-MFAC控制方法

Fig.4 Control method of CFDL-MFAC with new parameter treatments for MIMO system

图5 改进前后的MIMO系统y 1跟踪性能

Fig.5 Tracking performance of y 1 of MIMO system

图8 改进后的MIMO系统ρ 2值

Fig.8 ρ 2 value of MIMO system with the improved method

图6 改进前后的MIMO系统y 2跟踪性能

Fig.6 Tracking performance of y 2 of MIMO system

图7 改进后的MIMO系统ρ 1值

Fig.7 ρ 1 value of MIMO system with the improved method

图9 改进MFAC方法的系统跟踪性能

Fig.9 Tracking performance of MISO system with the improved MFAC method

图10 单神经元自适应PID控制方法的系统跟踪性能

Fig.10 Tracking performance of MISO system with the single neuron adaptive PID method

图11 加以扰动后改进MFAC方法的系统跟踪性能

Fig.11 Tracking performance of MISO system with the improved MFAC method after disturbance

图12 加以扰动后单神经元自适应PID控制方法的系统跟踪性能

Fig.12 Tracking performance of MISO system with the single neuron adaptive PID method after disturbance

图13 改进MFAC方法的系统跟踪性能

Fig.13 Tracking performance of MISO system with the improved MFAC method

图14 单神经元自适应PID控制方法的系统跟踪性能

Fig.14 Tracking performance of MISO system with the single neuron adaptive PID method

图15 某催化裂化装置主分馏和吸收稳定系统原则流程

Fig.15 Process sketch of main fractional and absorption& stabilization system of FCCU

图16 案例控制进程

Fig.16 Control process of the case

表1 两种控制方案的主要参数

Table 1 Main parameters of new and old MFAC cases for Fig.16 process

No.	参数	单位	现有操作	原型MFAC	改进MFAC
1	补充吸收剂流量	t/h	61.0	51.90	47.71
2	补充吸收剂温度	℃	37.9	30.3	30.2
3	吸收剂温度	℃	37.5	30.0	30.0
4	柴油产量	t/h	26.14	26.15	26.15
5	汽油产量	t/h	64.89	64.89	64.89
6	LPG产量	t/h	29.68	29.67	29.61
7	干气产量	t/h	11.62	11.64	11.70
8	透平3.5MPa汽耗	t/h	29.37	29.37	29.37
9	C303当量3.5MPa汽耗	t/h	17.00	16.36	16.06
10	C302当量1.0MPa汽耗	t/h	10.26	9.43	8.89
11	循环水耗	t/h	911.41	932.73	917.44
12	电耗	kW	289.89	276.35	270.19
13	收敛迭代次数			33	14
14	系统增益	CNY/h	0	492.2	646.3
14	系统增益	万元/年	0	413.4	542.9

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