电厂冷凝器清洗机器人的神经网络控制理论研究
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摘要
冷凝器是火力发电厂、化工、机械等行业的大型换热设备,它在汽轮机装置的热力循环中起冷源的作用,其工作性能好坏将直接影响整个汽轮机组的经济性和安全性。由于热交换时伴随化学反应以及冷却水不洁净等因素,因而导致了冷凝管内壁积聚了许多的污垢,这些污垢如果不及时清洗,会降低汽轮机发电机的效率、增加发电成本、甚至会导致冷凝管堵塞以及腐蚀穿孔而引发事故。针对该问题,本项目组研究了一种电厂冷凝器自动化清洗机器人来对冷凝器进行长期自主在线清洗,合理、高效地实现冷凝器污垢的在线清洗,保障大型火力发电厂安全生产、解决人工清洗效率低、工作强度大、环境恶劣、实现节能降耗等问题,减少机组停机造成的经济损失,提高汽轮机组的运行效率。为此,本文重点开展了电厂冷凝器自动化清洗机器人的神经网路智能控制理论方法研究。全文共七章,各章内容安排如下:
第一章,阐明了本论文的研究背景及意义,介绍了电厂冷凝器清洗机器人的结构、工作原理和研究现状,然后阐述了冷凝器清洗机器人、移动机器人以及神经网络控制的研究现状。并阐述了鲁棒自适应控制、模糊控制、智能控制、滑模控制等方法,最后给出了全文内容的结构安排。
第二章,结合一类实际的神经网络系统介绍了模糊控制的一些理论知识。通过运用LMI,研究了一类不连续时滞模糊神经网络模型的鲁棒模糊控制问题。得到了不连续模糊神经网络在其平衡点全局渐近稳定的充分条件准则。比较已有的文献,去掉了一些对神经激励函数的假设条件,如Lipschitz条件、单调性、有界性以及在不连续点左极限大于右极限等条件，论文的结果更具有一般性和普遍性。通过两个数值实验来证明所设计的模糊控制器的可控性和可行性。
第三章,提出了电厂冷凝器清洗机器人的神经网络鲁棒自适应控制策略。对电厂冷凝器清洗机器人进行了动力学建模。通过联合神经网络鲁棒自适应控制方法和滑模方法,设计了一个电厂冷凝器清洗机器人的神经网络鲁棒自适应滑模控制器。通过应用李雅普诺夫稳定性理论,将所设计的控制器能够保证系统的稳定性以及跟踪性能的一致有界性。因电厂冷凝器清洗机器人系统中含有不确定项和扰动,论文使用了RBF神经网络对其进行补偿。通过仿真分析和实验研究,验证了所得结果的有效性和优越性,结果表明,该控制方法具有良好的动、静态性能和抗干扰性能,是一种行之有效的控制方法。
第四章,研究了电厂冷凝器清洗机器人的神经网络模糊控制问题。通过联合模糊小脑神经网络和智能鲁棒自适应控制方法,设计了一个神经网络模糊控制器来控制电厂冷凝器清洗机器人系统。通过应用Lyapunov稳定性理论,将所设计的控制器来保证系统的稳定性。在电厂冷凝器清洗机器人系统控制器的设计过程中,本章使用模糊小脑神经网络来补偿系统中含有不确定项和外部扰动。通过仿真实验,验证了提出方法的可控性和有效性。
第五章,研究了一类三关节电厂冷凝器清洗机器人系统的滑模控制问题。通过对移动平台进行了运动学建模,给出了三关节机械臂的运动学模型,以及对三关节电厂冷凝器清洗机器人系统进行统一建模。采用模糊小波神经网络来逼近系统中的参数不确定和干扰项。通过李雅普诺夫稳定性理论,设计了一个电厂冷凝器清洗机器人系统的鲁棒自适应滑模控制器,保证了系统的全局渐近稳定性以及跟踪误差的最终一致有界性。通过仿真实验来验证了所得到的控制器的有效性和可控性。
第六章,研究了电厂冷凝器清洗机器人系统的智能神经网络控制问题。通过应用RBF神经网络的逼近非线性函数的能力来补偿冷凝器清洗机器人系统中的局部非线性性和不确定性。基于Lyapunov稳定性理论,设计了一个神经网络控制器来保证系统的鲁棒性和稳定性。通过仿真研究和实验分析,验证了所得智能神经网络控制策略的鲁棒性和理想的跟踪性能。
第七章,介绍了电厂冷凝器清洗机器人系统模型以及模糊高斯基函数神经网络的基本结构和逼近性能。通过应用模糊高斯基函数神经网络的逼近能力来补偿电厂冷凝器清洗机器人系统中的不确定扰动项。设计了一个电厂冷凝器清洗机器人系统的自适应模糊控制器。基于Lyapunov稳定性理论,得到了电厂冷凝器清洗机器人系统的稳定性定理。通过仿真实验分析,验证了所得神经网络自适应模糊控制策略的鲁棒性和理想的跟踪性能。
Condenser is a large heat transfer equipment in the thermal powerplant、chemical、machinery and other industries. It is the cold source of the ther-modynamic cycle of the large steam turbine. Good or bad performance of its work willdirectly affect the economics and safety of the entire steam turbine. For the chemicalreactions and unclear cooling water during heat exchange when the condenser is running,the fouling, which is not favorable for heat transfer, is accumulated in the inner wall ofcondenser tube. These fouling have produced such harm: reduces the efficiency of thesteam turbine, increases the cost of electricity and even leads to accidents because of theblockage and corrosion perforation of condenser tube. To solve the problem, this paperdesigns an intelligent condenser-cleaning mobile robot for large condenser in order tothe purpose of improving the heat transfer performance of condenser and increasing theefficiency of thermal cycle of turbo-generator unit. Thus, this dissertation focuses on themethods of neural network intelligent robust control for the condenser-cleaning mobilerobot. Main results and contributions of this dissertation can be list as the following sevenchapters.
In the first chapter, a review on the theoretical research and practical significance ofthe dissertation is presented, the history and basic principles of the condenser-cleaningrobot, the mobile robot, the neural network control are described. The paper introduces themethods of the robust adaptive control, the fuzzy control, the intelligent control and thesliding-mode control. Finally, the structure arrangement of the dissertation is given.
In the second chapter, the paper presents the speculative knowledge about the fuzzycontrol for a class of the delayed neural networks system. Based on the linear matrix in-equality(LMI), a robust fuzzy control to guarantee the global robust asymptotical stabilityof fuzzy neural networks with discontinuous activation functions is designed. Comparedwith the existing literature, this paper remove the assumptions on the neuron activationssuch as Lipschitz conditions, bounded, monotonic increasing property or the right limitvalue is bigger than the left one at the discontinuous point. Thus, the results of this pa-per are more general and widest. Finally, two numerical examples are given to show theeffectiveness and feasibility of the proposed fuzzy controller.
In the third chapter, the paper presents the radial basis functions neural network-basedadaptive robust control(RBFNNARC) for condenser-cleaning mobile robot. First, a dy- namic modeling is obtained based on the practical condenser-cleaning mobile manipulatorsystem. Second, the RBF neural network is used to identify the unstructured system dy-namics directly due to its very good compensation nonlinear function ability. Using learn-ing ability of neural network, RBFNNARC can coordinately control the mobile platformand the mounted manipulator with different dynamics efficiently. The implementation ofthe control algorithm is dependent on the sliding mode control (SMC). Finally, based onthe Lyapunov stability theory, the stability of the whole control system, the boundednessof the neural network weight estimation errors, and the uniformly ultimately boundednessof the tracking error are all strictly guaranteed. Moreover, simulation and experiment aregiven to demonstrate that the proposed RBFNNARC approach can guarantee the wholesystem’s converges to the desired manifold with prescribed performance.
In the fourth chapter, the paper studies the neural network fuzzy control for thecondenser-cleaning mobile robot. A neural network fuzzy controller for the condenser-cleaning mobile robot is designed using the fuzzy wavelet neural network and the robustadaptive control method. The fuzzy wavelet neural network is used to identify the uncer-tainties and disturbances of the robot system due to its very good compensation nonlinearfunction ability. Based on the Lyapunov stability theory, the stability of the condenser-cleaning mobile robot system is guaranteed. Finally, simulation is give to demonstrate theeffectiveness and feasibility of the proposed controller.
In the fifth chapter, the paper researches the sliding mode control problem of a classof3-DOF condenser-cleaning mobile robot system with uncertainties and disturbances.First, the kinematic modeling of the mobile platform and the three joint manipulators aregiven. Then, the paper presents the dynamic modeling of the three joint mobile robotsystem. Second, the fuzzy wavelet neural network is used to identify the unstructuredsystem dynamics directly due to its very good compensation nonlinear function ability.Based on the Lyapunov stability theory, the paper designs a robust adaptive sliding modecontroller to guarantee the global robust asymptotical stability and the uniformly ultimatelyboundedness of the tracking error of the mobile robot system. Finally, simulation is giveto demonstrate the effectiveness and feasibility of the proposed controller.
In the sixth chapter, the paper studies the intelligent neural networks control problemfor condenser-cleaning mobile robot. The RBF neural network is used to identify the localuncertainties and disturbances of the condenser-cleaning mobile robot system due to itsvery good compensation nonlinear function ability. A neural network controller for thecondenser-cleaning mobile robot is designed using the Lyapunov stability theory. Finally,simulation and experiment are give to demonstrate the robustness and the perfect tracking performance of the proposed controller.
In the seventh chapter, a dynamic modeling is obtained based on the practicalcondenser-cleaning manipulator system. The fundamental structure and approximabil-ity of the fuzzy Gaussian function neural network are introduced. The fuzzy Gaussianfunction neural network is used to identify the unstructured system dynamics directly ofthe condenser-cleaning manipulator system due to its very good compensation nonlinearfunction ability. The paper designs an adaptive fuzzy controller for the condenser-cleaningmobile robot. Based on the Lyapunov stability theory, the stability theorem of the wholecontrol system is guaranteed. Moreover, simulation experiment is given to demonstrate therobustness and the perfect tracking performance of the proposed adaptive fuzzy controller.
第一章,阐明了本论文的研究背景及意义,介绍了电厂冷凝器清洗机器人的结构、工作原理和研究现状,然后阐述了冷凝器清洗机器人、移动机器人以及神经网络控制的研究现状。并阐述了鲁棒自适应控制、模糊控制、智能控制、滑模控制等方法,最后给出了全文内容的结构安排。
第二章,结合一类实际的神经网络系统介绍了模糊控制的一些理论知识。通过运用LMI,研究了一类不连续时滞模糊神经网络模型的鲁棒模糊控制问题。得到了不连续模糊神经网络在其平衡点全局渐近稳定的充分条件准则。比较已有的文献,去掉了一些对神经激励函数的假设条件,如Lipschitz条件、单调性、有界性以及在不连续点左极限大于右极限等条件，论文的结果更具有一般性和普遍性。通过两个数值实验来证明所设计的模糊控制器的可控性和可行性。
第三章,提出了电厂冷凝器清洗机器人的神经网络鲁棒自适应控制策略。对电厂冷凝器清洗机器人进行了动力学建模。通过联合神经网络鲁棒自适应控制方法和滑模方法,设计了一个电厂冷凝器清洗机器人的神经网络鲁棒自适应滑模控制器。通过应用李雅普诺夫稳定性理论,将所设计的控制器能够保证系统的稳定性以及跟踪性能的一致有界性。因电厂冷凝器清洗机器人系统中含有不确定项和扰动,论文使用了RBF神经网络对其进行补偿。通过仿真分析和实验研究,验证了所得结果的有效性和优越性,结果表明,该控制方法具有良好的动、静态性能和抗干扰性能,是一种行之有效的控制方法。
第四章,研究了电厂冷凝器清洗机器人的神经网络模糊控制问题。通过联合模糊小脑神经网络和智能鲁棒自适应控制方法,设计了一个神经网络模糊控制器来控制电厂冷凝器清洗机器人系统。通过应用Lyapunov稳定性理论,将所设计的控制器来保证系统的稳定性。在电厂冷凝器清洗机器人系统控制器的设计过程中,本章使用模糊小脑神经网络来补偿系统中含有不确定项和外部扰动。通过仿真实验,验证了提出方法的可控性和有效性。
第五章,研究了一类三关节电厂冷凝器清洗机器人系统的滑模控制问题。通过对移动平台进行了运动学建模,给出了三关节机械臂的运动学模型,以及对三关节电厂冷凝器清洗机器人系统进行统一建模。采用模糊小波神经网络来逼近系统中的参数不确定和干扰项。通过李雅普诺夫稳定性理论,设计了一个电厂冷凝器清洗机器人系统的鲁棒自适应滑模控制器,保证了系统的全局渐近稳定性以及跟踪误差的最终一致有界性。通过仿真实验来验证了所得到的控制器的有效性和可控性。
第六章,研究了电厂冷凝器清洗机器人系统的智能神经网络控制问题。通过应用RBF神经网络的逼近非线性函数的能力来补偿冷凝器清洗机器人系统中的局部非线性性和不确定性。基于Lyapunov稳定性理论,设计了一个神经网络控制器来保证系统的鲁棒性和稳定性。通过仿真研究和实验分析,验证了所得智能神经网络控制策略的鲁棒性和理想的跟踪性能。
第七章,介绍了电厂冷凝器清洗机器人系统模型以及模糊高斯基函数神经网络的基本结构和逼近性能。通过应用模糊高斯基函数神经网络的逼近能力来补偿电厂冷凝器清洗机器人系统中的不确定扰动项。设计了一个电厂冷凝器清洗机器人系统的自适应模糊控制器。基于Lyapunov稳定性理论,得到了电厂冷凝器清洗机器人系统的稳定性定理。通过仿真实验分析,验证了所得神经网络自适应模糊控制策略的鲁棒性和理想的跟踪性能。
Condenser is a large heat transfer equipment in the thermal powerplant、chemical、machinery and other industries. It is the cold source of the ther-modynamic cycle of the large steam turbine. Good or bad performance of its work willdirectly affect the economics and safety of the entire steam turbine. For the chemicalreactions and unclear cooling water during heat exchange when the condenser is running,the fouling, which is not favorable for heat transfer, is accumulated in the inner wall ofcondenser tube. These fouling have produced such harm: reduces the efficiency of thesteam turbine, increases the cost of electricity and even leads to accidents because of theblockage and corrosion perforation of condenser tube. To solve the problem, this paperdesigns an intelligent condenser-cleaning mobile robot for large condenser in order tothe purpose of improving the heat transfer performance of condenser and increasing theefficiency of thermal cycle of turbo-generator unit. Thus, this dissertation focuses on themethods of neural network intelligent robust control for the condenser-cleaning mobilerobot. Main results and contributions of this dissertation can be list as the following sevenchapters.
In the first chapter, a review on the theoretical research and practical significance ofthe dissertation is presented, the history and basic principles of the condenser-cleaningrobot, the mobile robot, the neural network control are described. The paper introduces themethods of the robust adaptive control, the fuzzy control, the intelligent control and thesliding-mode control. Finally, the structure arrangement of the dissertation is given.
In the second chapter, the paper presents the speculative knowledge about the fuzzycontrol for a class of the delayed neural networks system. Based on the linear matrix in-equality(LMI), a robust fuzzy control to guarantee the global robust asymptotical stabilityof fuzzy neural networks with discontinuous activation functions is designed. Comparedwith the existing literature, this paper remove the assumptions on the neuron activationssuch as Lipschitz conditions, bounded, monotonic increasing property or the right limitvalue is bigger than the left one at the discontinuous point. Thus, the results of this pa-per are more general and widest. Finally, two numerical examples are given to show theeffectiveness and feasibility of the proposed fuzzy controller.
In the third chapter, the paper presents the radial basis functions neural network-basedadaptive robust control(RBFNNARC) for condenser-cleaning mobile robot. First, a dy- namic modeling is obtained based on the practical condenser-cleaning mobile manipulatorsystem. Second, the RBF neural network is used to identify the unstructured system dy-namics directly due to its very good compensation nonlinear function ability. Using learn-ing ability of neural network, RBFNNARC can coordinately control the mobile platformand the mounted manipulator with different dynamics efficiently. The implementation ofthe control algorithm is dependent on the sliding mode control (SMC). Finally, based onthe Lyapunov stability theory, the stability of the whole control system, the boundednessof the neural network weight estimation errors, and the uniformly ultimately boundednessof the tracking error are all strictly guaranteed. Moreover, simulation and experiment aregiven to demonstrate that the proposed RBFNNARC approach can guarantee the wholesystem’s converges to the desired manifold with prescribed performance.
In the fourth chapter, the paper studies the neural network fuzzy control for thecondenser-cleaning mobile robot. A neural network fuzzy controller for the condenser-cleaning mobile robot is designed using the fuzzy wavelet neural network and the robustadaptive control method. The fuzzy wavelet neural network is used to identify the uncer-tainties and disturbances of the robot system due to its very good compensation nonlinearfunction ability. Based on the Lyapunov stability theory, the stability of the condenser-cleaning mobile robot system is guaranteed. Finally, simulation is give to demonstrate theeffectiveness and feasibility of the proposed controller.
In the fifth chapter, the paper researches the sliding mode control problem of a classof3-DOF condenser-cleaning mobile robot system with uncertainties and disturbances.First, the kinematic modeling of the mobile platform and the three joint manipulators aregiven. Then, the paper presents the dynamic modeling of the three joint mobile robotsystem. Second, the fuzzy wavelet neural network is used to identify the unstructuredsystem dynamics directly due to its very good compensation nonlinear function ability.Based on the Lyapunov stability theory, the paper designs a robust adaptive sliding modecontroller to guarantee the global robust asymptotical stability and the uniformly ultimatelyboundedness of the tracking error of the mobile robot system. Finally, simulation is giveto demonstrate the effectiveness and feasibility of the proposed controller.
In the sixth chapter, the paper studies the intelligent neural networks control problemfor condenser-cleaning mobile robot. The RBF neural network is used to identify the localuncertainties and disturbances of the condenser-cleaning mobile robot system due to itsvery good compensation nonlinear function ability. A neural network controller for thecondenser-cleaning mobile robot is designed using the Lyapunov stability theory. Finally,simulation and experiment are give to demonstrate the robustness and the perfect tracking performance of the proposed controller.
In the seventh chapter, a dynamic modeling is obtained based on the practicalcondenser-cleaning manipulator system. The fundamental structure and approximabil-ity of the fuzzy Gaussian function neural network are introduced. The fuzzy Gaussianfunction neural network is used to identify the unstructured system dynamics directly ofthe condenser-cleaning manipulator system due to its very good compensation nonlinearfunction ability. The paper designs an adaptive fuzzy controller for the condenser-cleaningmobile robot. Based on the Lyapunov stability theory, the stability theorem of the wholecontrol system is guaranteed. Moreover, simulation experiment is given to demonstrate therobustness and the perfect tracking performance of the proposed adaptive fuzzy controller.
引文
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