具有复杂根系和枯死时间的植物PI控制器的幅度优化设计

IF 1.2 4区 计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS Archives of Control Sciences Pub Date : 2023-07-20 DOI:10.24425/acs.2022.140862
J. Cvejn
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引用次数: 0

摘要

基于幅度最优准则的线性对象pid型控制器的解析设计通常具有很好的控制质量,并且可以直接应用于具有死区时间的高阶线性模型,而无需进行任何模型化简。本文分析了该整定方法在PI控制器情况下的性质,表明该方法对无零的稳定线性对象保证了闭环稳定性和较大的稳定裕度,尽管在振荡对象情况下存在局限性。尽管幅值最优准则仅规定了低频时的闭环响应,并且设计目标中没有明确包括稳定裕度要求,但它表明,在中高频范围内适当的开环行为对闭环的稳定性和鲁棒性具有决定性作用。对于所考虑的一类线性系统,如果与无死时间项的植物传递函数的极点相对应的所有阻尼比都足够高,则自动确保。
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The magnitude optimum design of the PI controller for plants with complex roots and dead time
Analytical design of the PID-type controllers for linear plants based on the magnitude optimum criterion usually results in very good control quality and can be applied directly for high-order linear models with dead time, without need of any model reduction. This paper brings an analysis of properties of this tuning method in the case of the PI controller, which shows that it guarantees closed-loop stability and a large stability margin for stable linear plants without zeros, although there are limitations in the case of oscillating plants. In spite of the fact that the magnitude optimum criterion prescribes the closed-loop response only for low frequencies and the stability margin requirements are not explicitly included in the design objective, it reveals that proper open-loop behavior in the middle and high frequency ranges, decisive for the closed-loop stability and robustness, is ensured automatically for the considered class of linear systems if all damping ratios corresponding to poles of the plant transfer function without the dead-time term are sufficiently high.
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来源期刊
Archives of Control Sciences
Archives of Control Sciences Mathematics-Modeling and Simulation
CiteScore
2.40
自引率
33.30%
发文量
0
审稿时长
14 weeks
期刊介绍: Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.
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