报告人：Radu-Emil Precup 院士
工作单位：蒂米什瓦拉理工学院（罗马尼亚）
报告题目：二自由度模糊控制器设计与应用中的问题
报告时间：2023年5月19日（周五）14:30-15:30
报告链接：Teams Link
https://teams.microsoft.com/l/meetup-join/19%3aB4gmRcUATAMA2iJqi-xXvtfPFfTbxVJPxSW_pcAPBao1%40thread.tacv2/1638719716825?context=%7b%22Tid%22%3a%2222804ebb-30d5-47df-942f-f3a3722f0225%22%2c%22Oid%22%3a%2216a60c03-ad7a-4b85-a403-8ebd947e010c%22%7d
内容摘要：
正如各种关于控制的经典论文中所强调的，控制器从一自由度（1-DOF）扩展到二自由度（2-DOF）实际上有助于对控制器（或设定点）和干扰输入和适当的附加块进行单独设计和调整。这使得控制系统的设计和调整对于参考输入和干扰输入都具有良好的性能，即良好的设定点跟踪和良好的干扰抑制。针对线性控制器的结果的转换在追溯到1999年和2003年Precup和Preitl，由此产生了2-DOF模糊控制器，它最初被称为对输入信道具有非均匀动力学的模糊控制器。这些控制器在后来的2009年和2012年发表的论文中得到了进一步的发展，并应用于伺服系统和电气驱动器。这种转变允许增强控制系统的性能指标，特别是当控制器处理非线性过程时。在元启发式（优化）算法的背景下，自然启发的优化算法（NIOAs）变得非常流行，因为它们在效率和复杂性方面比经典算法要好得多。在相关的优化问题中，利用模糊控制器的调优参数作为变量，成功地将NIOAs应用于模糊控制器参数的最优调优。这是一种可行的方法，可以确保系统地设计和调整模糊控制器，并在非线性过程的控制中取得成功，其中过程和模糊控制器的非线性特征会影响优化问题的有效解决，并可能导致局部解。NIOAs的一个改进方法是对无法找到解析解的复杂优化问题进行性能上的改进。NIOAs的三个缺点是： (i)NIOAs算法无法提前设置解决问题的最佳参数，(2)敏感性对算法的参数，和（iii）大量的评估目标函数。模糊控制器的稳定设计是保证其系统设计的另一种方法。在稳定性分析中推导出的稳定性条件为控制器的调整提供了有用的信息，也可以作为优化问题中的不等式型约束。本报告将介绍2-DOF模糊控制器的设计、调优和实现的几个问题，重点是2-DOF-PI-模糊控制器和2-DOF-模糊控制器的两种形式。该调优是基于根据模态等效原理将线性PI和PID控制器的参数映射到模糊控制器的参数。线性控制器由Preitl和Precup的扩展对称优化方法（1999）进行调谐。同时还采用基于丢番图方程的经典代数方法，并将2-DOF线性控制器的参数映射到2-DOF模糊控制器的参数上。本报告还将讨论关于基于NIOA的模糊控制器调优及其稳定设计的各个方面，并强调了罗马尼亚蒂米索瓦拉大学过程控制小组在该小组实验室的代表性应用中获得的部分成果。
个人简介：
Radu-Emil Precup院士目前担任罗马尼亚蒂米什瓦拉理工大学博士研究委员会主任、布加勒斯特理工大学自动控制和计算机博士学院委员会委员，蒂米什瓦拉理工大学自动系统工程研究中心主任，自动化与信息应用系教授；同时兼任瑞士国家科学基金会 (SNSF)、意大利博洛尼亚CINECA、墨西哥城国家科学技术委员会 (CONACYT)以及斯洛伐克科学院流动性与重返社会计划 (MoRePro)等评委。Radu-Emil Precup 院士目前的研究领域包括新控制结构和算法的开发和分析，包括常规控制、模糊控制、基于数据的控制、无模型控制、滑模控制、神经模糊控制；软计算理论与应用；系统建模、识别和优化（包括启发算法）；控制系统的计算机辅助设计；在机电一体化系统（包括汽车系统和移动机器人）、嵌入式系统、电厂控制、伺服系统、电气驱动系统等方面的应用。迄今为止Radu-Emil Precup院士已出版专著近30部，发表高质量论文380余篇，19次受邀发表学术演讲。
【编辑：王健】
英文版：
Academic Report Notice of Radu-Emil Precup : Issues in the Design and Application of Two-Degree-of-Freedom Fuzzy Controllers
Speaker: Academician Radu-Emil Precup
Title: Issues in the Design and Application of Two-Degree-of-Freedom Fuzzy Controllers
Time: 14:30-15:30, May 19, 2023 (Friday)
Website: Teams Link
https://teams.microsoft.com/l/meetup-join/19%3aB4gmRcUATAMA2iJqi-xXvtfPFfTbxVJPxSW_pcAPBao1%40thread.tacv2/1638719716825?context=%7b%22Tid%22%3a%2222804ebb-30d5-47df-942f-f3a3722f0225%22%2c%22Oid%22%3a%2216a60c03-ad7a-4b85-a403-8ebd947e010c%22%7d
Abstract:
As highlighted in various classical papers on control, the extension of the controllers from one-degree-of-freedom (1-DOF) ones to two-degree-of-freedom (2-DOF) ones actually helps in the separate design and tuning with respect to the reference input (or the set-point) and the disturbance input (usually of load-type) in terms of a feedforward connection and appropriate additional block inserted in the controller (and thus control system) structure. This allows the design and tuning of control systems with good performance with respect to both the reference input and the disturbance input, namely good set-point tracking and good disturbance rejection. The transition of the results specific to linear controllers is coined back to Precup and Preitl in 1999 and 2003 leading to 2-DOF fuzzy controllers, which were initially called fuzzy controllers with non-homogenous dynamics with respect to the input channels. These controllers were further developed in the later papers published 2009 and 2012 and applied to servo systems and electrical drives. This transition allows the enhancement of the control system performance indices especially if the controllers cope with nonlinear processes.In the context of metaheuristic (optimization) algorithms, nature-inspired optimization algorithms (NIOAs), became very popular as they are much better in terms of efficiency and complexity than classical algorithms. Using the tuning parameters of the fuzzy controllers as variables in appropriately defined optimization problems, NIOAs are successfully applied to the optimal tuning of the parameters of fuzzy controllers. This is a viable approach to ensure the systematic design and tuning of fuzzy controllers and successful in the control of nonlinear processes, where the nonlinear feature of both the processes and the fuzzy controllers affect the efficient solving of the optimization problems and can lead to local solutions.An advantage of NIOAs is the performance improvement for complicated optimization problems where analytical solutions cannot be found. Three shortcomings of NIOAs are: (i) there are not yet methods to know the best parameters of NIOAs to solve problems that can be set at the beginning when using the algorithms, (ii) sensitivity with respect to the parameters of the algorithms, and (iii) large number of evaluations of the objective functions.The stable design of fuzzy controllers is another approach to ensure their systematic design. The stability conditions derived in the context of the stability analysis give useful information in the tuning of the controllers and can also be employed as inequality-type constraints in the optimization problems. This lecture presents several issues concerning the design, tuning and implementation of 2-DOF fuzzy controllers focusing on 2-DOF PI-fuzzy controllers and 2-DOF PID-fuzzy controllers in their Mamdani and Takagi-Sugeno-Kang forms. The tuning is based on mapping the parameters of the linear PI and PID controllers to the parameters of the fuzzy controllers in terms of the modal equivalence principle. The linear controllers are tuned by Preitl’s and Precup’s Extended Symmetrical Optimum method (1999). The classical algebraic approach based on Diophantine equations and mapping the parameters of the 2-DOF linear controllers to the parameters of the 2-DOF fuzzy ones will be treated as well. The lecture also treats aspects concerning the NIOA-based tuning of fuzzy controllers and their stable design, and highlights a part of the results obtained by the Process Control group of the Politehnica University of Timisoara, Romania, in representative applications of the group’s labs.
Personal Introduction:
Radu-Emil Precup is currently a Director of the Council of Doctoral Studies of the Politehnica University of Timisoara, Romania, a member of the Council of the Doctoral School Automatic Control and Computers, Politehnica University of Bucharest, Romania, a member of the Computers, information technology and systems engineering committee as part of the CNATDCU, a director of the Automatic Systems Engineering Research Centre with the Politehnica University of Timisoara, Romania, a professor with the Department of Automation and Applied (previously named Industrial) Informatics, Faculty of Automation and Computers, Politehnica University of Timisoara, Romania, a reviewer of the Swiss National Science Foundation (SNSF), Bern, Switzerland, CINECA, Bologna, Italy, the National Council of Science and Technology (CONACYT), Ciudad de Mexico, Mexico, and the Mobility and Reintegration Programme (MoRePro) of the Slovak Academy of Sciences, Bratislava, Slovakia, etc. He's current research fields include development and analysis of new control structures and algorithms including conventional control, fuzzy control, data-based control, model-free control, sliding mode control, neuro-fuzzy control; theory and applications of soft computing; systems modelling, identification and optimization (including nature-inspired algorithms); computer-aided design of control systems; applications to mechatronic systems (including automotive systems and mobile robots), embedded systems, control of power plants, servo systems, electrical driving systems, etc. So far, he has published nearly 30 monographs, published more than 380 high-quality papers, and was invited to give 19 academic speeches.
[Editor: Jian Wang]