导管架式海洋平台振动智能自适应逆控制研究
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摘要
随着海洋开发的不断发展,越来越多大型柔性结构的海洋平台被用于海底钻探和石油开采。海洋平台长期在恶劣的海洋环境中工作,经常要承受风、浪、流和潮汐等环境载荷的作用,在这些环境载荷的作用下海洋平台可能发生有害振动。振动响应过大不但会危害人员的身心健康,使平台设备仪器失灵或损坏,还会导致海洋平台结构疲劳破坏,降低平台可靠性,威胁平台结构安全,因此如何有效地控制海洋平台的有害振动就显得非常重要。采用被动控制方法控制海洋平台的振动,控制的频带范围有限。应用基于精确数学模型的主动控制方法控制海洋平台的振动,对于海洋平台这种结构复杂、参数易变,外载荷具有随机性和不确定性的系统很难达到理想的控制效果。在海洋平台振动主动控制过程中,控制信号传输延时会使控制系统发生振荡、不稳定甚至发散,因此如何减小控制过程中的时延也是一个急待解决的问题。近些年发展起来的智能控制方法可有效地解决海洋平台振动控制中存在的问题,为此本文应用智能自适应逆控制方法对随机波浪载荷和风载荷激励下导管架式海洋平台的振动响应进行控制。本文的主要工作如下:
(1)基于动态刚度阵法的导管架式海洋平台动力响应计算
本文通过数值方法模拟作用在导管架式海洋平台上的波浪载荷和风载荷。动态刚度阵法是一种非常有效的结构动力分析方法,具有计算速度快、计算准确的特点,所以本文应用动态刚度阵法计算导管架式海洋平台在波浪载荷和风载荷作用下的动力响应,为导管架式海洋平台振动主动控制研究提供了前提条件。
(2)建立了基于动态刚度阵法的导管架式海洋平台振动智能自适应逆控制模型
本文将动态刚度阵法、智能算法和自适应逆控制方法相结合,建立了基于动态刚度阵法的导管架式海洋平台振动智能自适应逆控制模型。自适应逆控制是通过建立被控系统的逆模型,然后将逆模型作为控制器,分别利用前馈控制器和扰动消除控制器控制被控对象的动态性能和扰动。智能算法具有很强的辨识和泛化能力,适合处理具有不确定性和非线性的问题,为此本文应用智能算法辨识导管架式海洋平台的逆模型。将动态刚度阵法计算出的波浪载荷作用下海洋平台振动响应作为前馈控制器的输入信号,利用前馈控制器对波浪载荷激励下的平台振动响应进行控制,将前馈控制后的平台响应作为扰动消除控制器的输入信号,利用扰动消除控制器对风载荷等扰动进行控制。
(3)智能算法及导管架式海洋平台振动智能自适应预测逆控制研究
(a)本文应用模糊神经网络辨识导管架式海洋平台的逆模型,将逆模型作为逆控制器,对随机波浪载荷和风载荷激励下导管架式海洋平台的有害振动进行控制,通过数值算例验证了本文的控制模型是有效、可行的。
(b)本文首次将支持向量机应用于导管架式海洋平台振动主动控制中,应用支持向量机辨识海洋平台的逆模型,并将辨识的逆模型作为逆控制器,对导管架式海洋平台进行自适应预测逆控制,通过数值算例验证了该控制方法可有效地控制平台的振动响应。
(c)本文将粗糙集和神经网络相结合构造一种新的神经网络结构,利用粗糙集理论简化网络结构,提高网络的训练、计算速度。应用粗神经网络对导管架式海洋平台进行自适应预测逆控制,数值算例验证了粗神经网络具有计算速度快,辨识和泛化能力强等特点,基于粗神经网络的自适应逆控制方法可有效地控制导管架式海洋平台的振动响应。
(d)本文将灰局势决策理论和神经网络相结合构造一种新的神经网络结构,基于以往效果测度的神经网络辨识和泛化能力比较弱,为此本文提出一种新的效果测度,通过定义和数值算例证明了本文所提出效果测度的合理性和有效性。将基于新效果测度的灰局势决策理论和神经网络相融合,构建了一个新型的灰神经网络,该神经网络结构明确,计算简单,充分发挥了灰局势决策理论和神经网络各自的优点。将构建的灰神经网络作为自适应预测逆控制器,对导管架式海洋平台进行振动主动控制。通过数值仿真结果可以看出,基于灰神经网络的自适应预测逆控制方法可以有效地控制在波浪载荷和风载荷共同作用下导管架式海洋平台的振动响应。
(e)本文将灰预测理论、动态刚度阵法、智能算法和自适应逆控制方法结合,建立了一种新的基于灰预测理论和动态刚度阵法的导管架式海洋平台振动智能自适应逆控制模型,通过灰预测控制解决控制过程中的时延对控制系统控制性能的影响。数值算例证明本文所提出的方法是有效的。
(4)导管架式海洋平台振动主动质量阻尼控制系统设计
本文对主动质量阻尼控制系统进行了设计,这为基于智能算法的导管架式海洋平台振动自适应逆控制系统的物理实现提供了理论基础。
With the development of ocean exploitation, more and more large-scale flexible offshore platforms are used for drilling operations and oil exploration. In a hostile ocean environment, offshore platforms usually experience various environmental loads such as wind, wave, flow and tidal loads, which will induce continuous vibration of the offshore platforms. Excessive vibrations will result in injury to human health, capacity degradation or even halt of the equipments and apparatus. Also, the vibrations can cause structural fatigue damages, decrease the reliability and threat the structure safety of the platforms. Hence, it is important to find effective control schemes for mitigating the vibrational response of the offshore platforms. When passive control methods are used to control vibrations in jacket offshore platforms, the cotrolled frequency band range is narrow. Active control methods using accurate mathematical models cannot achieve ideal purposes, because the jacket offshore platform is a system with complex structure, undetermined structural parameters and stochastic uncertain external load. Moreover, the delay of control signal transmission may cause instability and surge problems of the system. Therefore, in vibration control of jacket offshore platform, it is urgent to find methods of reducing the effects of the delay. The problems in vibration control can be solved effectively by intelligent methods. To better control the vibration of the jacket offshore platform under the wave and wind load, this paper applies intelligent adaptive inverse control methods. The main work includes:
(1) Dynamic response calculation of jacket offshore platforms based on dynamic stiffness matrix method
In this dissertation, we carried out the simulation of wave load and wind load that act on jacket offshore platforms via numerical methods. Dynamic stiffness matrix method is a quite effective method of dynamic structural analysis, which can increase the computation speed and precision. Therefore, we apply the dynamic stiffness matrix method to analysize dynamic response of jacket offshore platforms under wave and wind load. This has provided precondition for control vibration of jacket offshore platforms.
(2) Based on dynamic stiffness matrix method, a model is established for vibration intelligent adaptive inverse control of jacket offshore platform
Combining the advantages of dynamic stiffness matrix method, intelligent method and adaptive inverse control method, we proposed a new model for intelligent adaptive inverse control of jacket offshore platform based on dynamic stiffness matrix method. The adaptive inverse control method identifies the inverse model of the controlled system, then the inverse model is used as a controller that utilize feed-forward controller and disturbance eliminator controller to control the dynamic performance and disturbance of the controlled object. In this research, the intelligent method is adopted to identify the inverse model of the jacket offshore platform, because it has strong capability of handling uncertain and nonlinear information. Vibration responses of the offshore platform under random wave loads are calculated by the dynamic stiffness matrix method, the calculated responses are taken as the input signals of feed-forward controller, which controls the vibration responses correspondingly. The responses of feed-forward control are taken as the input signals of disturbance eliminator controller, which hinders the influence of the above factors on the control system performance.
(3) Study on the intelligent methods and vibration intelligent adaptive inverse control of jacket offshore platform
(a) Fuzzy neural network was used to identify the predictive inverse model of the jacket offshore platform in this paper. The inverse model is used as the adaptive inverse controller, which controls the vibration of jacket offshore platform under random wave and wind load. Simulation results show that our model is effective and practical.
(b) Support vector machine was firstly applied to control vibration of the jacket offshore platform. Inverse control method based on a support vector machine can be adopted to estimate the inverse plant model. The inverse model is used as the adaptive inverse controller, which controls the vibration of jacket offshore platform. Simulation results indicate that the method is effective for control vibration responses of jacket offshore platform.
(c) A novel rough neural network was proposed in this paper by combining rough set and neural network. In our study, the rough set theory is used to simplify network structure and increase the speed of training and calculation. Rough neural network is implemented in the active control vibration of jacket offshore platform. Simulation results show that the obtained rough neural network has advantages of fast calculation, better identification and generalization capacity, the adaptive inverse control method based on rough neural network is effective to control vibrations of jacket offshore platform.
(d) Combining the grey situation decision theory with the neural network, we propose a new structure for construction of the neural network. Neural network based on quondam effect measure has weak capacity of identification and generalization, so we provide a new effect measure. Theoretical proofs and simulation results show the new measure is effective and reasonable. A novel grey neural network was proposed by combining neural network and grey situation decision theory using the new effect measure. The grey neural network has advantages such as clear structure, simple calculation. The grey neural network takes full advantages of grey situation decision theory and neural network, and it is used as the adaptive inverse controller, which controls vibrations of jacket offshore platform. Simulation results show that the adaptive inverse control method based on grey neural network can effectively decrease the harmful vibration of jacket offshore platform under the wave and wind load.
(e) Combining the advantages of grey forecasting theory, dynamic stiffness matrix method, intelligent methods and adaptive inverse control method, we proposed a new model for intelligent adaptive inverse control of jacket offshore platform based on grey forecasting theory and dynamic stiffness matrix method. In addition, the problem of time delay during the control process was solved using the grey predictive control method. Simulation results show that our method is effective.
(4) Design of vibration active mass damper control system of jacket offshore platform
Furthermore, we designed an active mass damper control system, which provided a theoretical basis for physical realization of jacket offshore platform adaptive inverse control system based on intelligent methods.
(1)基于动态刚度阵法的导管架式海洋平台动力响应计算
本文通过数值方法模拟作用在导管架式海洋平台上的波浪载荷和风载荷。动态刚度阵法是一种非常有效的结构动力分析方法,具有计算速度快、计算准确的特点,所以本文应用动态刚度阵法计算导管架式海洋平台在波浪载荷和风载荷作用下的动力响应,为导管架式海洋平台振动主动控制研究提供了前提条件。
(2)建立了基于动态刚度阵法的导管架式海洋平台振动智能自适应逆控制模型
本文将动态刚度阵法、智能算法和自适应逆控制方法相结合,建立了基于动态刚度阵法的导管架式海洋平台振动智能自适应逆控制模型。自适应逆控制是通过建立被控系统的逆模型,然后将逆模型作为控制器,分别利用前馈控制器和扰动消除控制器控制被控对象的动态性能和扰动。智能算法具有很强的辨识和泛化能力,适合处理具有不确定性和非线性的问题,为此本文应用智能算法辨识导管架式海洋平台的逆模型。将动态刚度阵法计算出的波浪载荷作用下海洋平台振动响应作为前馈控制器的输入信号,利用前馈控制器对波浪载荷激励下的平台振动响应进行控制,将前馈控制后的平台响应作为扰动消除控制器的输入信号,利用扰动消除控制器对风载荷等扰动进行控制。
(3)智能算法及导管架式海洋平台振动智能自适应预测逆控制研究
(a)本文应用模糊神经网络辨识导管架式海洋平台的逆模型,将逆模型作为逆控制器,对随机波浪载荷和风载荷激励下导管架式海洋平台的有害振动进行控制,通过数值算例验证了本文的控制模型是有效、可行的。
(b)本文首次将支持向量机应用于导管架式海洋平台振动主动控制中,应用支持向量机辨识海洋平台的逆模型,并将辨识的逆模型作为逆控制器,对导管架式海洋平台进行自适应预测逆控制,通过数值算例验证了该控制方法可有效地控制平台的振动响应。
(c)本文将粗糙集和神经网络相结合构造一种新的神经网络结构,利用粗糙集理论简化网络结构,提高网络的训练、计算速度。应用粗神经网络对导管架式海洋平台进行自适应预测逆控制,数值算例验证了粗神经网络具有计算速度快,辨识和泛化能力强等特点,基于粗神经网络的自适应逆控制方法可有效地控制导管架式海洋平台的振动响应。
(d)本文将灰局势决策理论和神经网络相结合构造一种新的神经网络结构,基于以往效果测度的神经网络辨识和泛化能力比较弱,为此本文提出一种新的效果测度,通过定义和数值算例证明了本文所提出效果测度的合理性和有效性。将基于新效果测度的灰局势决策理论和神经网络相融合,构建了一个新型的灰神经网络,该神经网络结构明确,计算简单,充分发挥了灰局势决策理论和神经网络各自的优点。将构建的灰神经网络作为自适应预测逆控制器,对导管架式海洋平台进行振动主动控制。通过数值仿真结果可以看出,基于灰神经网络的自适应预测逆控制方法可以有效地控制在波浪载荷和风载荷共同作用下导管架式海洋平台的振动响应。
(e)本文将灰预测理论、动态刚度阵法、智能算法和自适应逆控制方法结合,建立了一种新的基于灰预测理论和动态刚度阵法的导管架式海洋平台振动智能自适应逆控制模型,通过灰预测控制解决控制过程中的时延对控制系统控制性能的影响。数值算例证明本文所提出的方法是有效的。
(4)导管架式海洋平台振动主动质量阻尼控制系统设计
本文对主动质量阻尼控制系统进行了设计,这为基于智能算法的导管架式海洋平台振动自适应逆控制系统的物理实现提供了理论基础。
With the development of ocean exploitation, more and more large-scale flexible offshore platforms are used for drilling operations and oil exploration. In a hostile ocean environment, offshore platforms usually experience various environmental loads such as wind, wave, flow and tidal loads, which will induce continuous vibration of the offshore platforms. Excessive vibrations will result in injury to human health, capacity degradation or even halt of the equipments and apparatus. Also, the vibrations can cause structural fatigue damages, decrease the reliability and threat the structure safety of the platforms. Hence, it is important to find effective control schemes for mitigating the vibrational response of the offshore platforms. When passive control methods are used to control vibrations in jacket offshore platforms, the cotrolled frequency band range is narrow. Active control methods using accurate mathematical models cannot achieve ideal purposes, because the jacket offshore platform is a system with complex structure, undetermined structural parameters and stochastic uncertain external load. Moreover, the delay of control signal transmission may cause instability and surge problems of the system. Therefore, in vibration control of jacket offshore platform, it is urgent to find methods of reducing the effects of the delay. The problems in vibration control can be solved effectively by intelligent methods. To better control the vibration of the jacket offshore platform under the wave and wind load, this paper applies intelligent adaptive inverse control methods. The main work includes:
(1) Dynamic response calculation of jacket offshore platforms based on dynamic stiffness matrix method
In this dissertation, we carried out the simulation of wave load and wind load that act on jacket offshore platforms via numerical methods. Dynamic stiffness matrix method is a quite effective method of dynamic structural analysis, which can increase the computation speed and precision. Therefore, we apply the dynamic stiffness matrix method to analysize dynamic response of jacket offshore platforms under wave and wind load. This has provided precondition for control vibration of jacket offshore platforms.
(2) Based on dynamic stiffness matrix method, a model is established for vibration intelligent adaptive inverse control of jacket offshore platform
Combining the advantages of dynamic stiffness matrix method, intelligent method and adaptive inverse control method, we proposed a new model for intelligent adaptive inverse control of jacket offshore platform based on dynamic stiffness matrix method. The adaptive inverse control method identifies the inverse model of the controlled system, then the inverse model is used as a controller that utilize feed-forward controller and disturbance eliminator controller to control the dynamic performance and disturbance of the controlled object. In this research, the intelligent method is adopted to identify the inverse model of the jacket offshore platform, because it has strong capability of handling uncertain and nonlinear information. Vibration responses of the offshore platform under random wave loads are calculated by the dynamic stiffness matrix method, the calculated responses are taken as the input signals of feed-forward controller, which controls the vibration responses correspondingly. The responses of feed-forward control are taken as the input signals of disturbance eliminator controller, which hinders the influence of the above factors on the control system performance.
(3) Study on the intelligent methods and vibration intelligent adaptive inverse control of jacket offshore platform
(a) Fuzzy neural network was used to identify the predictive inverse model of the jacket offshore platform in this paper. The inverse model is used as the adaptive inverse controller, which controls the vibration of jacket offshore platform under random wave and wind load. Simulation results show that our model is effective and practical.
(b) Support vector machine was firstly applied to control vibration of the jacket offshore platform. Inverse control method based on a support vector machine can be adopted to estimate the inverse plant model. The inverse model is used as the adaptive inverse controller, which controls the vibration of jacket offshore platform. Simulation results indicate that the method is effective for control vibration responses of jacket offshore platform.
(c) A novel rough neural network was proposed in this paper by combining rough set and neural network. In our study, the rough set theory is used to simplify network structure and increase the speed of training and calculation. Rough neural network is implemented in the active control vibration of jacket offshore platform. Simulation results show that the obtained rough neural network has advantages of fast calculation, better identification and generalization capacity, the adaptive inverse control method based on rough neural network is effective to control vibrations of jacket offshore platform.
(d) Combining the grey situation decision theory with the neural network, we propose a new structure for construction of the neural network. Neural network based on quondam effect measure has weak capacity of identification and generalization, so we provide a new effect measure. Theoretical proofs and simulation results show the new measure is effective and reasonable. A novel grey neural network was proposed by combining neural network and grey situation decision theory using the new effect measure. The grey neural network has advantages such as clear structure, simple calculation. The grey neural network takes full advantages of grey situation decision theory and neural network, and it is used as the adaptive inverse controller, which controls vibrations of jacket offshore platform. Simulation results show that the adaptive inverse control method based on grey neural network can effectively decrease the harmful vibration of jacket offshore platform under the wave and wind load.
(e) Combining the advantages of grey forecasting theory, dynamic stiffness matrix method, intelligent methods and adaptive inverse control method, we proposed a new model for intelligent adaptive inverse control of jacket offshore platform based on grey forecasting theory and dynamic stiffness matrix method. In addition, the problem of time delay during the control process was solved using the grey predictive control method. Simulation results show that our method is effective.
(4) Design of vibration active mass damper control system of jacket offshore platform
Furthermore, we designed an active mass damper control system, which provided a theoretical basis for physical realization of jacket offshore platform adaptive inverse control system based on intelligent methods.
引文
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