冷凝器清洗移动作业机器人智能自适应运动控制方法研究
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摘要
冷凝器是热发电厂,核能发电厂,和其他工业电厂的重要装置。通常,在工业冷凝器系统由水平或垂直方向起热交换作用的小冷凝器(管板结构)组成。然而实际上,冷凝器通常需要在不同环境下工作很长的时间,由此造成的损伤普遍存在。当这些损害出现时,冷凝器的运行效率下降。此外,来自冷凝器故障的风险不仅会影响单元热效率和电厂的其它设备,而且增加了生产成本。为了克服这些问题,近年来,湖南大学研究团队针对大型冷凝器清洗工作研究了一种冷凝器清洗移动作业机器人。基于前人的研究,为了提高冷凝器机器人的运行效率,本论文致力于研究基于神经网络的智能控制方法。研究的主要成果阐述如下：
研究的第一部分内容提出了一种基于模糊小波神经网络(FWNNs)和递归FWNNs (RFWNNs)的机械臂控制的自适应智能跟踪控制方法。这里针对两种智能控制器,自适应RFWNNs和FWNNs控制器,分别做了研究和比较。该方法中,控制系统是在没有稳定性先验知识的情况下设计的。机器人系统的动力学模型由RFWNNs/FWNNs近似。通过小波技术与模糊神经网络(FNNs)的结合,提出一种相比FNNs控制器拥有更高的跟踪性能的基于FWNNs的控制器。另外,FWNNs控制器具有良好的重现性。另外,为了适应参数的变化,本论文在之前提出基于FWNNs的控制器的基础上设计了RFWNNs控制器,RFWNNs具有动态网络结构,可以克服FWNNs静态数据结构的不足,并且可能更加方便的应用于时变不确定性高阶非线性系统。通过仿真和实验验证了所提出方法的有效性和鲁棒性。所提出的控制方法在移动机械手(MMR)中得到了良好应用。
研究的第二部分内容中,研究了基于RFWNNs的自适应智能运动控制系统在履带式冷凝器清洗移动作业机器人(CCMMR)中的应用。由于CCMMR和MMR系统具有时变不确定性,基于RFWNNs/FWNNs的自适应智能运动控制系统可提高基于神经网络(NNs)控制器的灵活性,并且减少了跟踪误差。通过对MMR控制结果的发展,RFWNNs模型被用来近似CCMMR控制系统的未知动态需求。与机械手结构相比,CCMMR具有完整或者非完整的约束具,因而具有更复杂的动态性。基于此,本论文还提出了一种自适应鲁棒控制方法。此外,控制参数的在线学习算法由Lyapunov稳定性定理获得,使得所提出的控制系统的稳定性得到保证。通过仿真和实验结果验证了所提方法的有效性和鲁棒性。
研究的第三部分内容,提出了一种新的递归模糊小波小脑模型的精确控制神经网络(RFWCMACNNs)结构。这种结构是小波技术,高木(TSK)模糊结构,WNNs和小脑精确控制模型的结合。因此,RFWCMACNNs是更广义的神经网络,它们适用于控CCMMR/MMR系统。论文研究的目的在于提高基于NNs机器人控制的有效性。为此,提出了一种改进型的小脑近似模型机器人控制方法。如CMAC (FCMAC), RCMAC和迭代FCMAC (RFCMAC).这种RFWCMACNNs应用于位置跟踪控制器来逼近CCMMR控制系统的未知动态模型。基于位置跟踪控制,本论文还针对CCMMR/MMR的非完整约束设计了一种自适应鲁棒控制策略。所有控制参数的在线学习算法都是由Lyapunov稳定性定理推导得到的,从而保证了控制系统的稳定性。此外,论文还阐述了一种简化的基于模糊小波网络的控制器CMAC NNs (FWCMACNNs)与本研究提出的方法对比。通过对比仿真和实验结果验证了本文所设计的控制器在CCMMR和MMR控制系统的有效性和鲁棒性。
研究的第四部分内容中,基于传统的反向推理控制系统,针对CCMMR控制系统提出了一种智能控制系统。由于第二和第三部分研究内容取得了良好的实验结果,RFWCMACNNs被应用到位置跟踪反向推理控制系统中,从而逼近CCMMR控制系统的未知动态模型。本论文的目标是提高之前提出的基于NNs和CMAC的CCMMR系统的有效性和鲁棒性。和之前两项研究一样,自适应鲁棒控制器也考虑了CCMMR的非完整约束。所有控制参数的在线学习算法都是由Lyapunov稳定性定理推导得到的,从而保证了控制系统的稳定性。另外,对比仿真和实验结果验证了本控制系统的有效性和鲁棒性。
Condenser is the vital apparatus of the thermal power plants, nuclear power plants and other industrial plants. Commonly, the industrial-condenser-system is constructed as a bank of horizontal or vertical small-condenser-tubes that play the role of heat exchangers. In fact, the condensers usually operate under impact of environmental-conditions for very long periods of time, and the hazards to which they are prone during normal service always exist. When the ills appear on the condensers, the operating-effectiveness of the condensers will be reduced. In addition, the risks from the condensers will affect not only unit heat rate and other equipment of plants but also the operating-cost. To overcome this problem, a condenser cleaning mobile manipulator robot for large condenser-cleaning has been researched and tested in recent years. By inheriting this advantage, this dissertation focuses on intelligent control methods-based hybrid neural networks (NNs) in order to improve the operating-effectiveness for this robotic control system. Main results and contributions of this dissertation are presented as follows.
The first contribution proposes an adaptive intelligent tracking control method based on fuzzy wavelet neural networks (FWNNs) and recurrent FWNNs (RFWNNs) for robot manipulators control. Here, there are two intelligent controllers, adaptive RFWNNs and FWNNs controllers, are proposed to examine and compare. In this method, the control system is designed without the knowledge of robotic system. The unknown dynamics of the robotic system is approximated by the proposed RFWNNs/FWNNs. By combining the wavelet technique with the fuzzy neural networks (FNNs), the proposed controllers-based the FWNNs can achieve higher tracking-performances in comparison with the FNNs/NNs controllers. In addition, the recurrent technique is also considered in the first contribution, the second proposed RFWNNs-controller, to improve the flexibility/adaptation for the first proposed FWNNs-controller under parameter variation conditions. The RFWNNs structure is a dynamic network structure. Thus, it can overcome the static-structure problem of the FWNNs structure, and it can be applied more convenient in highly nonlinear systems in the presence of time-vary ing uncertainties. The effectiveness and robustness of the proposed methods are confirmed by comparative simulation and experimental results. The robot manipulators-control is first examined here to set the premise properly for the mobile manipulator robot control (MMR).
In the second contribution, an adaptive intelligent force/motion control system based on the RFWNNs is applied for the condenser cleaning mobile manipulator robot (CCMMR) control. The purpose of this contribution is to improve the flexibility and tracking errors of the previous controllers-based neural networks for the CCMMR or MMR control under time-varying uncertainties. By inheriting the good results from the first contribution, the RFWNNs/FWNNs are also utilized to relax the unknown-dynamics-requirements of the CCMMR control system. When compared with the robot manipulators structure, the dynamics of CCMMR are more complex with impact of the holonomic/nonholonomic constraints. Therefore, an adaptive robust control strategy in the proposed control system is also developed for the nonholomic constraint force from the CCMMR. In addition, the online-learning algorithms of the proposed control-parameters are obtained by the Lyapunov stability theorem such that the stability of the proposed control systems is guaranteed. The effectiveness and robustness of the proposed methods are verified by comparative simulation and experimental results.
The third contribution proposes a novel recurrent fuzzy wavelet cerebellar model articulation control neural networks (RFWCMACNNs) structure. This structure is the combination of the wavelet technique, the Takagi-Sugeno-Kang (TSK) fuzzy structure, WNNs, and the recurrent cerebellar model articulation control (RCMAC) neural networks structure. Therefore, the RFWCMACNNs are more generalized networks and they are suitable for the control of the CCMMR. The purpose of this contribution is to improve the effectiveness of controllers-based NNs for the robotics-control. In addition, this contribution also presents the improvement for the approximation processes-based cerebellar model articulation control (CMAC) techniques, such as fuzzy CMAC (FCMAC), RCMAC and recurrent FCMAC (RFCMAC). The RFWCMACNNs are applied in the proposed position tracking-controller to approximate the unknown dynamics of the CCMMR control system. Based on the design of the position-tracking controller, an adaptive robust control scheme is also developed for the nonholonomic constraint force from the CCMMR/MMR. All the online-learning algorithms of RFWCMACNNs control-parameters are derived by the Lyapunov stability theorem. Therefore, the stability of the proposed methods is guaranteed. Moreover, this contribution also presents the design brief of the controller-based fuzzy wavelet CMAC NNs (FWCMACNNs) to compare with the proposed method. The effectiveness and robustness of the proposed intelligent controllers are verified by comparative simulation and experimental result that are implemented in the CCMMR control system.
In the fourth contribution, an intelligent control system is proposed for the CCMMR by inheriting the advantage of the conventional backstepping control system (BCS). Based on the good control-results from the second and third contributions, the RFWCMACNNs are applied in the position-tracking-BCS to approximate the unknown dynamics of the CCMMR/MMR control system. The purpose of this contribution is to further improve the effectiveness and robustness of the previous controllers-based NNs and CMAC for the CCMMR control. Similar to two previous contributions, the control of the nonholonomic constraint force from CCMMR is also considered by an adaptive robust controller. All the online-learning algorithms of control-parameters in the proposed controllers are obtained by the Lyapunov stability theorem such that the stability of the controlled system is guaranteed. In addition, comparative simulation and experimental results are provided to confirm the effectiveness and robustness of the proposed control systems.
研究的第一部分内容提出了一种基于模糊小波神经网络(FWNNs)和递归FWNNs (RFWNNs)的机械臂控制的自适应智能跟踪控制方法。这里针对两种智能控制器,自适应RFWNNs和FWNNs控制器,分别做了研究和比较。该方法中,控制系统是在没有稳定性先验知识的情况下设计的。机器人系统的动力学模型由RFWNNs/FWNNs近似。通过小波技术与模糊神经网络(FNNs)的结合,提出一种相比FNNs控制器拥有更高的跟踪性能的基于FWNNs的控制器。另外,FWNNs控制器具有良好的重现性。另外,为了适应参数的变化,本论文在之前提出基于FWNNs的控制器的基础上设计了RFWNNs控制器,RFWNNs具有动态网络结构,可以克服FWNNs静态数据结构的不足,并且可能更加方便的应用于时变不确定性高阶非线性系统。通过仿真和实验验证了所提出方法的有效性和鲁棒性。所提出的控制方法在移动机械手(MMR)中得到了良好应用。
研究的第二部分内容中,研究了基于RFWNNs的自适应智能运动控制系统在履带式冷凝器清洗移动作业机器人(CCMMR)中的应用。由于CCMMR和MMR系统具有时变不确定性,基于RFWNNs/FWNNs的自适应智能运动控制系统可提高基于神经网络(NNs)控制器的灵活性,并且减少了跟踪误差。通过对MMR控制结果的发展,RFWNNs模型被用来近似CCMMR控制系统的未知动态需求。与机械手结构相比,CCMMR具有完整或者非完整的约束具,因而具有更复杂的动态性。基于此,本论文还提出了一种自适应鲁棒控制方法。此外,控制参数的在线学习算法由Lyapunov稳定性定理获得,使得所提出的控制系统的稳定性得到保证。通过仿真和实验结果验证了所提方法的有效性和鲁棒性。
研究的第三部分内容,提出了一种新的递归模糊小波小脑模型的精确控制神经网络(RFWCMACNNs)结构。这种结构是小波技术,高木(TSK)模糊结构,WNNs和小脑精确控制模型的结合。因此,RFWCMACNNs是更广义的神经网络,它们适用于控CCMMR/MMR系统。论文研究的目的在于提高基于NNs机器人控制的有效性。为此,提出了一种改进型的小脑近似模型机器人控制方法。如CMAC (FCMAC), RCMAC和迭代FCMAC (RFCMAC).这种RFWCMACNNs应用于位置跟踪控制器来逼近CCMMR控制系统的未知动态模型。基于位置跟踪控制,本论文还针对CCMMR/MMR的非完整约束设计了一种自适应鲁棒控制策略。所有控制参数的在线学习算法都是由Lyapunov稳定性定理推导得到的,从而保证了控制系统的稳定性。此外,论文还阐述了一种简化的基于模糊小波网络的控制器CMAC NNs (FWCMACNNs)与本研究提出的方法对比。通过对比仿真和实验结果验证了本文所设计的控制器在CCMMR和MMR控制系统的有效性和鲁棒性。
研究的第四部分内容中,基于传统的反向推理控制系统,针对CCMMR控制系统提出了一种智能控制系统。由于第二和第三部分研究内容取得了良好的实验结果,RFWCMACNNs被应用到位置跟踪反向推理控制系统中,从而逼近CCMMR控制系统的未知动态模型。本论文的目标是提高之前提出的基于NNs和CMAC的CCMMR系统的有效性和鲁棒性。和之前两项研究一样,自适应鲁棒控制器也考虑了CCMMR的非完整约束。所有控制参数的在线学习算法都是由Lyapunov稳定性定理推导得到的,从而保证了控制系统的稳定性。另外,对比仿真和实验结果验证了本控制系统的有效性和鲁棒性。
Condenser is the vital apparatus of the thermal power plants, nuclear power plants and other industrial plants. Commonly, the industrial-condenser-system is constructed as a bank of horizontal or vertical small-condenser-tubes that play the role of heat exchangers. In fact, the condensers usually operate under impact of environmental-conditions for very long periods of time, and the hazards to which they are prone during normal service always exist. When the ills appear on the condensers, the operating-effectiveness of the condensers will be reduced. In addition, the risks from the condensers will affect not only unit heat rate and other equipment of plants but also the operating-cost. To overcome this problem, a condenser cleaning mobile manipulator robot for large condenser-cleaning has been researched and tested in recent years. By inheriting this advantage, this dissertation focuses on intelligent control methods-based hybrid neural networks (NNs) in order to improve the operating-effectiveness for this robotic control system. Main results and contributions of this dissertation are presented as follows.
The first contribution proposes an adaptive intelligent tracking control method based on fuzzy wavelet neural networks (FWNNs) and recurrent FWNNs (RFWNNs) for robot manipulators control. Here, there are two intelligent controllers, adaptive RFWNNs and FWNNs controllers, are proposed to examine and compare. In this method, the control system is designed without the knowledge of robotic system. The unknown dynamics of the robotic system is approximated by the proposed RFWNNs/FWNNs. By combining the wavelet technique with the fuzzy neural networks (FNNs), the proposed controllers-based the FWNNs can achieve higher tracking-performances in comparison with the FNNs/NNs controllers. In addition, the recurrent technique is also considered in the first contribution, the second proposed RFWNNs-controller, to improve the flexibility/adaptation for the first proposed FWNNs-controller under parameter variation conditions. The RFWNNs structure is a dynamic network structure. Thus, it can overcome the static-structure problem of the FWNNs structure, and it can be applied more convenient in highly nonlinear systems in the presence of time-vary ing uncertainties. The effectiveness and robustness of the proposed methods are confirmed by comparative simulation and experimental results. The robot manipulators-control is first examined here to set the premise properly for the mobile manipulator robot control (MMR).
In the second contribution, an adaptive intelligent force/motion control system based on the RFWNNs is applied for the condenser cleaning mobile manipulator robot (CCMMR) control. The purpose of this contribution is to improve the flexibility and tracking errors of the previous controllers-based neural networks for the CCMMR or MMR control under time-varying uncertainties. By inheriting the good results from the first contribution, the RFWNNs/FWNNs are also utilized to relax the unknown-dynamics-requirements of the CCMMR control system. When compared with the robot manipulators structure, the dynamics of CCMMR are more complex with impact of the holonomic/nonholonomic constraints. Therefore, an adaptive robust control strategy in the proposed control system is also developed for the nonholomic constraint force from the CCMMR. In addition, the online-learning algorithms of the proposed control-parameters are obtained by the Lyapunov stability theorem such that the stability of the proposed control systems is guaranteed. The effectiveness and robustness of the proposed methods are verified by comparative simulation and experimental results.
The third contribution proposes a novel recurrent fuzzy wavelet cerebellar model articulation control neural networks (RFWCMACNNs) structure. This structure is the combination of the wavelet technique, the Takagi-Sugeno-Kang (TSK) fuzzy structure, WNNs, and the recurrent cerebellar model articulation control (RCMAC) neural networks structure. Therefore, the RFWCMACNNs are more generalized networks and they are suitable for the control of the CCMMR. The purpose of this contribution is to improve the effectiveness of controllers-based NNs for the robotics-control. In addition, this contribution also presents the improvement for the approximation processes-based cerebellar model articulation control (CMAC) techniques, such as fuzzy CMAC (FCMAC), RCMAC and recurrent FCMAC (RFCMAC). The RFWCMACNNs are applied in the proposed position tracking-controller to approximate the unknown dynamics of the CCMMR control system. Based on the design of the position-tracking controller, an adaptive robust control scheme is also developed for the nonholonomic constraint force from the CCMMR/MMR. All the online-learning algorithms of RFWCMACNNs control-parameters are derived by the Lyapunov stability theorem. Therefore, the stability of the proposed methods is guaranteed. Moreover, this contribution also presents the design brief of the controller-based fuzzy wavelet CMAC NNs (FWCMACNNs) to compare with the proposed method. The effectiveness and robustness of the proposed intelligent controllers are verified by comparative simulation and experimental result that are implemented in the CCMMR control system.
In the fourth contribution, an intelligent control system is proposed for the CCMMR by inheriting the advantage of the conventional backstepping control system (BCS). Based on the good control-results from the second and third contributions, the RFWCMACNNs are applied in the position-tracking-BCS to approximate the unknown dynamics of the CCMMR/MMR control system. The purpose of this contribution is to further improve the effectiveness and robustness of the previous controllers-based NNs and CMAC for the CCMMR control. Similar to two previous contributions, the control of the nonholonomic constraint force from CCMMR is also considered by an adaptive robust controller. All the online-learning algorithms of control-parameters in the proposed controllers are obtained by the Lyapunov stability theorem such that the stability of the controlled system is guaranteed. In addition, comparative simulation and experimental results are provided to confirm the effectiveness and robustness of the proposed control systems.
引文
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