大时滞系统控制方法的研究
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摘要
工业生产过程中,具有时滞特性的被控对象是非常普遍的,而时滞对象也是比较难控制的,尤其是大时滞对象的控制一直是一个难题。本文介绍了目前国内外在克服大时滞方面的一些先进控制方法,着重分析了预测控制理论和模糊预测控制理论在克服大时滞方面的一般理论方法。
模糊辨识与控制理论在解决复杂系统控制问题的过程中表现出了巨大的潜力。本文针对复杂系统控制的研究,考虑将模糊控制与预测控制相结合起来,吸收各自的优点,提出了一种无辨识模糊预测自适应控制方法。通过研究和仿真结果表明,这种算法能获得良好的控制效果。
基于T_S模型的模糊辨识算法是一种较好的模糊控制算法。本文在此基础上提出改进的基于递推模糊聚类的在线辨识方法,对模型进行在线自适应调整。改进后的算法极大的减少了参数辨识和结构辨识的计算量,能够保证在线辨识的要求,仿真取得了较好的跟踪结果。然后在辨识模糊模型的基础上对其进行广义预测控制,并对GPC的递推算法进行修正,得到了较为理想的控制效果,具有较强的鲁棒性、抗干扰性与克服大时滞的能力。
In the industrial production process, the controled object with time-delay characteristics is very common, but it is difficult to control, especially the long time-delay control is always a difficult problem. This paper introduces the current some advanced control methods in dealing with the long time-delay system, especially analyzes the general theory methods to overcome the long time-delay field of the predictive control and fuzzy predictive control theory.
In solving the problem of complex system control, the fuzzy identification and control theory show enormous potential. For the control problem of complex systems, this paper combines fuzzy control and predictive control, and presents a fuzzy prediction identification-free algorithm for adaptive control method by absorbing their respective advantages. The research and simulation results show that the algorithm above can obtain good control effect.
The fuzzy identification algorithm based on T_S model is a good fuzzy control algorithm. An improved on-line identification method based on recursive fuzzy clustering is presented, which can adaptive adjust the model on line. The improved algorithm reduces the computation of parameter identification and structural identification significantly, and guarantees the requirements of online identification.The simulations obtain a better tracking result. The improved on-line identification method presents generalized predictive control based on the identification of fuzzy model, and amends the GPC recursive algorithm, then obtains a better control effect, which has robustness, anti-interference and the ability to overcome the long time-delay.
模糊辨识与控制理论在解决复杂系统控制问题的过程中表现出了巨大的潜力。本文针对复杂系统控制的研究,考虑将模糊控制与预测控制相结合起来,吸收各自的优点,提出了一种无辨识模糊预测自适应控制方法。通过研究和仿真结果表明,这种算法能获得良好的控制效果。
基于T_S模型的模糊辨识算法是一种较好的模糊控制算法。本文在此基础上提出改进的基于递推模糊聚类的在线辨识方法,对模型进行在线自适应调整。改进后的算法极大的减少了参数辨识和结构辨识的计算量,能够保证在线辨识的要求,仿真取得了较好的跟踪结果。然后在辨识模糊模型的基础上对其进行广义预测控制,并对GPC的递推算法进行修正,得到了较为理想的控制效果,具有较强的鲁棒性、抗干扰性与克服大时滞的能力。
In the industrial production process, the controled object with time-delay characteristics is very common, but it is difficult to control, especially the long time-delay control is always a difficult problem. This paper introduces the current some advanced control methods in dealing with the long time-delay system, especially analyzes the general theory methods to overcome the long time-delay field of the predictive control and fuzzy predictive control theory.
In solving the problem of complex system control, the fuzzy identification and control theory show enormous potential. For the control problem of complex systems, this paper combines fuzzy control and predictive control, and presents a fuzzy prediction identification-free algorithm for adaptive control method by absorbing their respective advantages. The research and simulation results show that the algorithm above can obtain good control effect.
The fuzzy identification algorithm based on T_S model is a good fuzzy control algorithm. An improved on-line identification method based on recursive fuzzy clustering is presented, which can adaptive adjust the model on line. The improved algorithm reduces the computation of parameter identification and structural identification significantly, and guarantees the requirements of online identification.The simulations obtain a better tracking result. The improved on-line identification method presents generalized predictive control based on the identification of fuzzy model, and amends the GPC recursive algorithm, then obtains a better control effect, which has robustness, anti-interference and the ability to overcome the long time-delay.
引文
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