闭环自适应逆控制及在热力系统中的应用研究
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摘要
基于系统逆模型的控制策略主要包括自适应逆控制和逆系统控制两种典型的控制方法。近年来,基于系统逆模型的控制方法及其应用研究受到广泛关注。对系统逆动力学过程建模方法、基于系统逆模型的控制策略与经典反馈控制策略之间内在联系及有效融合等问题进行深入研究,可望为复杂系统提供新的控制思路和技术手段。
模糊模型是以模糊语言变量、模糊集合及模糊逻辑为数学基础的模糊推理规则,被广泛地应用于复杂非线性系统的辨识及控制中。本文研究了基于T-S模糊模型的闭环自适应逆控制方法及其在热力系统中的应用问题,主要内容包括以下四个方面:
①针对现有的T-S模糊模型辨识方法存在的问题,从模糊聚类方案入手,提出了一种基于最差子集分解聚类的T-S模糊模型辨识方法,以及基于最差子集分解聚类的T-S模糊模型分步辨识方法,并将该方法应用于典型热工过程及其逆系统建模。在系统辨识过程中,引入了新的聚类目标函数和辨识目标函数,根据建模精度要求自动产生新的模糊规则,避免了规则数需要人为给定的弊端;当模型精度未达到要求时,只需辨识新增子模型的结构和参数。相对于常规的T-S模糊模型辨识方法,具有辨识精度高,模糊规则数少及计算成本低等优点,提高了模型的实时跟踪能力,为开展热工过程自适应逆控制提供了有效支持。文中通过非线性系统辨识仿真试验对该方法的有效性进行了验证。
②针对复杂热工过程通常具有较大的时间迟延和热惯性等特征,建立了一种基于校正基准量的增量式控制算法;论证了被控对象逆模型参数与控制器特征参数之间的对应关系,提出了一种增量式闭环自适应逆控制系统,实现了自适应逆控制与经典反馈控制的有机结合,并能够根据对被控对象逆模型的在线辨识结果直接对控制器的特征参数进行在线调整。文中针对几类典型的热工对象(包括SISO热工对象和MIMO热工对象)采用增量式闭环自适应逆控制方法进行了仿真试验,结果表明,与直接自适应逆控制方法相比,增量式闭环自适应逆控制方法能够有效地降低被控对象逆动力学模型的精度对控制性能的影响,具有良好的自适应能力和良好的鲁棒性。
③提出了一种基于对象逆模型的自适应PID(PID-IMO)控制器的设计方法。在所建立的PID-IMO控制系统中,通过对逆模型输入向量的恰当选取,实现了对象逆模型与PID控制器两者结构上的统一;论证了自适应PID控制器特征参数与控制对象逆模型参数之间的内在联系和等价关系,进而根据逆模型的在线辨识结果直接获得PID控制器的参数,形成与对象特性相匹配的自适应PID控制器,并将PID-IMO方法推广到串级PID控制系统。通过对两种典型的热力系统过程控制的仿真试验表明,自适应PID-IMO控制系统具有良好的自适应能力、抗干扰能力和鲁棒性。
④逆系统控制是一种具有广泛适用性的非线性控制策略。本文将模糊辨识引入到逆系统控制方法中,采用所提出的基于最差子集分解聚类的分步模糊辨识方法建立被控对象的逆模型,避免了求解被控对象解析逆的困难,考察了基于模糊模型的系统逆模型对多变量非线性系统的线性化及解耦效果;根据被控对象所具有的不同迟延特性,采用PID控制器和Smith预估器作为附加控制器,设计了两种常用的模糊逆控制系统。文中通过仿真试验对设计的控制系统的性能进行了检验。
The control strategies based on system’s inverse model mainly include two control method, those are adaptive inverse control and inverse system control. In recent years, the control methods based on the system’s inverse model and its application researches have aroused widely attention. To research deeply the problems such as the method of modeling the process of system’s inverse dynamic, the relationships and the effective integrations between the control strategies based on system’s inverse model and the classical feedback control strategies, which are expected to provide new control idea and technological means.
Fuzzy model is a set of fuzzy inference rules based on fuzzy linguistic variables, fuzzy sets and fuzzy logic, which has been used widely to modeling and controlling complex nonlinear systems. In this paper, we have studied the problems of closed-loop adaptive inverse control methods based on T-S fuzzy model and its application in thermal systems, the main work includes the following four aspects:
①For the existing problems of T-S fuzzy identification methods, we started from fuzzy clustering schemes and proposed an identification method of T-S fuzzy model and a sub-step identification method of T-S fuzzy based on decomposing cluster of the worst subset, and applied them in modeling typical thermal processes and their inverse systems. In the identification process, new clustering and identifying objective functions were introduced, and new fuzzy rules can be automatically generated according to the required modeling accuracy, which avoided the disadvantages of the number of fuzzy rules should be given in advance. When the identification accuracy does not meet the predetermined target, only the structure and parameters of new sub-model should be identified. Comparing with the traditional identification methods of T-S fuzzy model, the proposed method has the advantages of high precision, less the number of fuzzy rules and less calculation cost and good real-time tracking ability, which provides an effective support for the development of adaptive inverse control method for thermal process. In this paper, the effectivenesses of the proposed methods were verified by simulation experiment of identifying nonlinear systems.
②For the features of complex thermal processes with the characteristics of large delay and thermal inertia, an incremental control algorithm was established based on corrected datumquantity; The correspondent relationship between the parameters of the inverse model of controlled object and the characteristic parameters of controller were demonstrated, and an closed-loop adaptive inverse control system based on increment form was proposed, which realized the organic combination of adaptive inverse control with classical feedback control scheme. The characteristic parameters of controller can be adjusted directly according to the online identification results of the inverse model of the controlled object. In this paper, for several typical thermal systems (including SISO and MIMO thermal systems), the incremental closed-loop adaptive inverse control method was utilized to control them by simulation experiments. The results showed that, comparing with conventional adaptive inverse control method, the incremental closed-loop adaptive inverse control method can effectively reduced the influence of the precision of the inverse dynamic model of the controlled object on the control performance, and has good adaptive ability and robustness.
③An adaptive PID controller design method based on the inverse model of the object (PID-IMO) was proposed. In the established PID-IMO control system, by choosing the appropriate structure of the inverse model, the accordance of structures of the inverse model and PID controller was realized; The inner and equal relationships between the characteristic parameters of adaptive PID controller and the parameters of the inverse model of controlled object were demonstrated. The parameters of PID controller were obtained directly according to the on-line identification result of the inverse model, and the PID controller which matches with the controlled object’s properties was formed, and this method was extended cascade control system. The simulation result of controlling two typical thermal systems showed that the proposed adaptive PID-IMO control system has good adaptive ability, anti-interference ability and robustness.
④Inverse system control is a nonlinear control strategy with widely applicability. In this paper, fuzzy model was introduced to this control scheme. The proposed sub-step identification method of T-S fuzzy based on decomposing cluster of the worst subset was adopted to establish the inverse model of the controlled object, which avoided the difficulty of solving the analytical inverse model of the controlled object, and the linearizing and decoupling effect of the inverse system based on fuzzy model was investigated; According to the different delay characteristic of the controlled object, PID controller and Smith predictor were adopted as the additional controller, and two fuzzy inverse control system were designed. In this paper, the performances of the designed control systems were verified by simulation experiment.
模糊模型是以模糊语言变量、模糊集合及模糊逻辑为数学基础的模糊推理规则,被广泛地应用于复杂非线性系统的辨识及控制中。本文研究了基于T-S模糊模型的闭环自适应逆控制方法及其在热力系统中的应用问题,主要内容包括以下四个方面:
①针对现有的T-S模糊模型辨识方法存在的问题,从模糊聚类方案入手,提出了一种基于最差子集分解聚类的T-S模糊模型辨识方法,以及基于最差子集分解聚类的T-S模糊模型分步辨识方法,并将该方法应用于典型热工过程及其逆系统建模。在系统辨识过程中,引入了新的聚类目标函数和辨识目标函数,根据建模精度要求自动产生新的模糊规则,避免了规则数需要人为给定的弊端;当模型精度未达到要求时,只需辨识新增子模型的结构和参数。相对于常规的T-S模糊模型辨识方法,具有辨识精度高,模糊规则数少及计算成本低等优点,提高了模型的实时跟踪能力,为开展热工过程自适应逆控制提供了有效支持。文中通过非线性系统辨识仿真试验对该方法的有效性进行了验证。
②针对复杂热工过程通常具有较大的时间迟延和热惯性等特征,建立了一种基于校正基准量的增量式控制算法;论证了被控对象逆模型参数与控制器特征参数之间的对应关系,提出了一种增量式闭环自适应逆控制系统,实现了自适应逆控制与经典反馈控制的有机结合,并能够根据对被控对象逆模型的在线辨识结果直接对控制器的特征参数进行在线调整。文中针对几类典型的热工对象(包括SISO热工对象和MIMO热工对象)采用增量式闭环自适应逆控制方法进行了仿真试验,结果表明,与直接自适应逆控制方法相比,增量式闭环自适应逆控制方法能够有效地降低被控对象逆动力学模型的精度对控制性能的影响,具有良好的自适应能力和良好的鲁棒性。
③提出了一种基于对象逆模型的自适应PID(PID-IMO)控制器的设计方法。在所建立的PID-IMO控制系统中,通过对逆模型输入向量的恰当选取,实现了对象逆模型与PID控制器两者结构上的统一;论证了自适应PID控制器特征参数与控制对象逆模型参数之间的内在联系和等价关系,进而根据逆模型的在线辨识结果直接获得PID控制器的参数,形成与对象特性相匹配的自适应PID控制器,并将PID-IMO方法推广到串级PID控制系统。通过对两种典型的热力系统过程控制的仿真试验表明,自适应PID-IMO控制系统具有良好的自适应能力、抗干扰能力和鲁棒性。
④逆系统控制是一种具有广泛适用性的非线性控制策略。本文将模糊辨识引入到逆系统控制方法中,采用所提出的基于最差子集分解聚类的分步模糊辨识方法建立被控对象的逆模型,避免了求解被控对象解析逆的困难,考察了基于模糊模型的系统逆模型对多变量非线性系统的线性化及解耦效果;根据被控对象所具有的不同迟延特性,采用PID控制器和Smith预估器作为附加控制器,设计了两种常用的模糊逆控制系统。文中通过仿真试验对设计的控制系统的性能进行了检验。
The control strategies based on system’s inverse model mainly include two control method, those are adaptive inverse control and inverse system control. In recent years, the control methods based on the system’s inverse model and its application researches have aroused widely attention. To research deeply the problems such as the method of modeling the process of system’s inverse dynamic, the relationships and the effective integrations between the control strategies based on system’s inverse model and the classical feedback control strategies, which are expected to provide new control idea and technological means.
Fuzzy model is a set of fuzzy inference rules based on fuzzy linguistic variables, fuzzy sets and fuzzy logic, which has been used widely to modeling and controlling complex nonlinear systems. In this paper, we have studied the problems of closed-loop adaptive inverse control methods based on T-S fuzzy model and its application in thermal systems, the main work includes the following four aspects:
①For the existing problems of T-S fuzzy identification methods, we started from fuzzy clustering schemes and proposed an identification method of T-S fuzzy model and a sub-step identification method of T-S fuzzy based on decomposing cluster of the worst subset, and applied them in modeling typical thermal processes and their inverse systems. In the identification process, new clustering and identifying objective functions were introduced, and new fuzzy rules can be automatically generated according to the required modeling accuracy, which avoided the disadvantages of the number of fuzzy rules should be given in advance. When the identification accuracy does not meet the predetermined target, only the structure and parameters of new sub-model should be identified. Comparing with the traditional identification methods of T-S fuzzy model, the proposed method has the advantages of high precision, less the number of fuzzy rules and less calculation cost and good real-time tracking ability, which provides an effective support for the development of adaptive inverse control method for thermal process. In this paper, the effectivenesses of the proposed methods were verified by simulation experiment of identifying nonlinear systems.
②For the features of complex thermal processes with the characteristics of large delay and thermal inertia, an incremental control algorithm was established based on corrected datumquantity; The correspondent relationship between the parameters of the inverse model of controlled object and the characteristic parameters of controller were demonstrated, and an closed-loop adaptive inverse control system based on increment form was proposed, which realized the organic combination of adaptive inverse control with classical feedback control scheme. The characteristic parameters of controller can be adjusted directly according to the online identification results of the inverse model of the controlled object. In this paper, for several typical thermal systems (including SISO and MIMO thermal systems), the incremental closed-loop adaptive inverse control method was utilized to control them by simulation experiments. The results showed that, comparing with conventional adaptive inverse control method, the incremental closed-loop adaptive inverse control method can effectively reduced the influence of the precision of the inverse dynamic model of the controlled object on the control performance, and has good adaptive ability and robustness.
③An adaptive PID controller design method based on the inverse model of the object (PID-IMO) was proposed. In the established PID-IMO control system, by choosing the appropriate structure of the inverse model, the accordance of structures of the inverse model and PID controller was realized; The inner and equal relationships between the characteristic parameters of adaptive PID controller and the parameters of the inverse model of controlled object were demonstrated. The parameters of PID controller were obtained directly according to the on-line identification result of the inverse model, and the PID controller which matches with the controlled object’s properties was formed, and this method was extended cascade control system. The simulation result of controlling two typical thermal systems showed that the proposed adaptive PID-IMO control system has good adaptive ability, anti-interference ability and robustness.
④Inverse system control is a nonlinear control strategy with widely applicability. In this paper, fuzzy model was introduced to this control scheme. The proposed sub-step identification method of T-S fuzzy based on decomposing cluster of the worst subset was adopted to establish the inverse model of the controlled object, which avoided the difficulty of solving the analytical inverse model of the controlled object, and the linearizing and decoupling effect of the inverse system based on fuzzy model was investigated; According to the different delay characteristic of the controlled object, PID controller and Smith predictor were adopted as the additional controller, and two fuzzy inverse control system were designed. In this paper, the performances of the designed control systems were verified by simulation experiment.
引文
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