基于RBF网络逼近的机器人自适应动态面控制方法研究
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摘要
机器人通常由驱动关节（由伺服电机和传动减速器组成）、机体、传感器和控制器等部件组成，是一个高度复杂的非线性系统，而RBF网络是解决具有高度非线性和不确定性的复杂系统控制问题的一种行之有效的方法。本文中的机器人特别是其子系统柔性滤波驱动机构（由伺服电机与柔性滤波传动机构组成）具有高阶、多变量、强耦合、参数时变、部分状态变量不易或者无法检测等特点，而这些特点会使得控制器的设计非常复杂。另外，随着机器人在特殊环境下的应用，传统的控制方法显然不能满足高精度控制要求，因此研究先进的控制方法，提高机器人的动静态性能，是一个富有理论意义和工程应用价值的研究课题。基于RBF网络逼近的机器人自适应动态面技术能够很好的克服摩擦、混沌运动、外界干扰和参数不确定等因素的影响，从而实现系统的高品质控制。
论文主要研究成果如下：
首先，提出了一类具有输出约束的N阶系统的RBF动态面控制方法，并以此为理论基础，研究了无刷直流电机系统的混沌控制问题。针对具有不确定时滞的无刷直流电机的控制问题，考虑存在输出约束的情况，利用RBF网络的万能逼近特性，从而提出了无刷直流电机混沌系统的自适应RBF网络动态面控制方法。所提控制方法保证了系统的稳定性及系统中所有信号的最终一致有界性，同时系统输出满足一定的约束条件。实验验证了所提控制器的收敛性和混沌抑制能力，同时对参数扰动表现出一定的鲁棒性。
其次，研究了基于RBF网络动态面的永磁同步电机混沌系统的速度与位置跟踪控制问题。考虑未知有界外界扰动、控制方向未定、系统参数不确定等因素的影响，应用快速终端滑模和动态面方法，结合自适应技术与Nussbaum增益对系统中的未知函数进行估计，从而提出了基于单参数权值RBF网络快速终端滑模的永磁同步电机混沌系统的速度控制与基于Nussbaum增益的RBF网络动态面永磁同步电机混沌系统的位置控制。实验结果表明所提方案都具有一定的鲁棒性。与指数滑模控制相比，所提方法优势明显。
再次，研究了柔性滤波驱动机构的RBF网络动态面控制。以机器人驱动关节—柔性滤波驱动机构为研究对象，对柔性滤波传动机构进行了机械设计，同时考虑柔性变形、摩擦、传动误差和系统效率等非线性因素的影响，建立了柔性滤波驱动机构的数学模型，进而提出了考虑LuGre摩擦模型的柔性滤波驱动机构的RBF网络动态面控制。另外，利用Lyapunov理论证明了所提控制方法的稳定性。实验分析表明所提方法具有较强的鲁棒性与稳定性。与PID相比，所提控制方法具有明显的优越性。
最后，研究了基于柔性滤波驱动机构的机器人RBF网络的控制问题。以具有摩擦、扰动和参数不确定等因素影响的机器人为研究对象，设计了基于柔性滤波驱动机构的工业机器人的自适应单参数权值RBF网络反演法控制器与基于柔性滤波驱动机构的移动机器人的干扰观测器RBF网络动态面控制器。同时，利用Lyapunov理论论证了所提控制方法的稳定性。仿真实验分析结果（包括与传统DSC比较）表明所提控制器具有很强的跟踪精度、鲁棒性和干扰补偿能力。
The robot which usually consists of the driving joint composed of servo motor anddrive reducer, body, sensors and controller, etc is a highly complex nonlinear system,while RBF network is an effective way to solve the controlling problem of complexsystems with highly nonlinear and uncertain characters. The robot in this paper has thecharacteristics of high-order, multi-variable, strong coupling, parameter variation,partial state variables be difficultly or hardly detected, etc as well as its subsystems suchas the flexible filtering drive mechanism composed of the servo motor and the flexiblefilter transmission mechanism, and the existence of these characteristics will make thenonlinear system controller design become complex. In addition, traditional controlmethods obviously don’t satisfy the requirements of high-precision control as theapplication in the special and extreme environment. Therefore, the study of advancedcontrol method is a challenging research subject full of the theoretical significance andthe project application value to improve dynamic and static performance of the robotbased on the flexible filter transmission mechanism. The adaptive dynamic surfacecontrol based on RBF network of the robot can overcome friction, chaotic motion,outside interference, parameter uncertain, etc to achieve the high control performance ofthe system.
Main research results of the thesis are as follows:
Firstly, the RBF network dynamic surface control method is proposed for a class ofN-order system with output constraint, as a theoretical basis, and then chaos controlproblem of the brushless DC motor drive system is researched. Aiming at the controlproblem of the brushless DC motor system with uncertain time-delay, considering theoutput constraints and using universal approximation properties of RBF network, thenan adaptive RBF network dynamic surface control method of the brushless DC motorchaotic system is proposed. Asymptotically tracking stable and uniformly ultimateboundedness of the tracking error are ensured without violation of the constraint.Experiment results show that the designed controller has convergence, chaotic behaviorsuppressession ability and high-robustness for system parameters perturbation.
Secondly, the speed tracking and position tracking control problems of thepermanent magnet synchronous motor chaotic system are studied. Consideringunknown bounded external disturbances, controlling direction uncertainty and system parameter uncertainty, applying fast terminal sliding mode and dynamic surface methodand combining with adaptive technology and Nussbaum gain to estimate the unknownfunction in the system, then a fast terminal sliding mode speed tracking control based onone-parameter weights RBF network and position tracking control based on Nussbaumgain RBF network dynamic surface of the PMSM chaotic system are proposedrespectively. The experiment results show that the proposed control schemes have agood convergence and robustness. Compared with the index sliding mode control, theproposed method has clear advantages.
Thirdly, a RBF network dynamic surface control of flexible filtering drivemechanism is researched. Selecting the robot drive joints—flexible filtering drivemechanism as the research object, mechanical design of the transmission mechanism ofthe flexible filtering is conducted. Then taking into account the flexible deformation,friction and transmission errors, system efficiency and other nonlinear factors effect,mathematical models of flexible filter drive mechanism is established. Then, anadaptive dynamic surface of flexible filtering drive mechanism is proposed based onRBF network. Besides, using Lyapunov theory proves the stability of the proposedcontrol method. The results show that the proposed control method has a strongrobustness and stability. Compared with the PID control, the proposed control methodhas obvious advantages, which may extend to the electromechanical transmission.
Finally, the RBF network control problem of the robot based on the flexible filterdrive mechanism is researched. Selecting the robot with friction, disturbance anduncertain parameters, etc as the research object, an adaptive RBF network backsteppingcontroller with one-parameter weights for the industry robot is designed based on theflexible filtering drive mechanism firstly. Secondly, a RBF network dynamic surfacecontroller with the disturbance observer of the mobile robot is designed based on theflexible filtering drive mechanism. In the meantime, the Lyapunov theory is employedto ensure the stability of the proposed control method. Containing to compare with thetraditional dynamic surface control, the experiment results show that the proposedcontrollers have a higher tracking accuracy, stronger robustness and bettercompensation ability for disturbance.
论文主要研究成果如下：
首先，提出了一类具有输出约束的N阶系统的RBF动态面控制方法，并以此为理论基础，研究了无刷直流电机系统的混沌控制问题。针对具有不确定时滞的无刷直流电机的控制问题，考虑存在输出约束的情况，利用RBF网络的万能逼近特性，从而提出了无刷直流电机混沌系统的自适应RBF网络动态面控制方法。所提控制方法保证了系统的稳定性及系统中所有信号的最终一致有界性，同时系统输出满足一定的约束条件。实验验证了所提控制器的收敛性和混沌抑制能力，同时对参数扰动表现出一定的鲁棒性。
其次，研究了基于RBF网络动态面的永磁同步电机混沌系统的速度与位置跟踪控制问题。考虑未知有界外界扰动、控制方向未定、系统参数不确定等因素的影响，应用快速终端滑模和动态面方法，结合自适应技术与Nussbaum增益对系统中的未知函数进行估计，从而提出了基于单参数权值RBF网络快速终端滑模的永磁同步电机混沌系统的速度控制与基于Nussbaum增益的RBF网络动态面永磁同步电机混沌系统的位置控制。实验结果表明所提方案都具有一定的鲁棒性。与指数滑模控制相比，所提方法优势明显。
再次，研究了柔性滤波驱动机构的RBF网络动态面控制。以机器人驱动关节—柔性滤波驱动机构为研究对象，对柔性滤波传动机构进行了机械设计，同时考虑柔性变形、摩擦、传动误差和系统效率等非线性因素的影响，建立了柔性滤波驱动机构的数学模型，进而提出了考虑LuGre摩擦模型的柔性滤波驱动机构的RBF网络动态面控制。另外，利用Lyapunov理论证明了所提控制方法的稳定性。实验分析表明所提方法具有较强的鲁棒性与稳定性。与PID相比，所提控制方法具有明显的优越性。
最后，研究了基于柔性滤波驱动机构的机器人RBF网络的控制问题。以具有摩擦、扰动和参数不确定等因素影响的机器人为研究对象，设计了基于柔性滤波驱动机构的工业机器人的自适应单参数权值RBF网络反演法控制器与基于柔性滤波驱动机构的移动机器人的干扰观测器RBF网络动态面控制器。同时，利用Lyapunov理论论证了所提控制方法的稳定性。仿真实验分析结果（包括与传统DSC比较）表明所提控制器具有很强的跟踪精度、鲁棒性和干扰补偿能力。
The robot which usually consists of the driving joint composed of servo motor anddrive reducer, body, sensors and controller, etc is a highly complex nonlinear system,while RBF network is an effective way to solve the controlling problem of complexsystems with highly nonlinear and uncertain characters. The robot in this paper has thecharacteristics of high-order, multi-variable, strong coupling, parameter variation,partial state variables be difficultly or hardly detected, etc as well as its subsystems suchas the flexible filtering drive mechanism composed of the servo motor and the flexiblefilter transmission mechanism, and the existence of these characteristics will make thenonlinear system controller design become complex. In addition, traditional controlmethods obviously don’t satisfy the requirements of high-precision control as theapplication in the special and extreme environment. Therefore, the study of advancedcontrol method is a challenging research subject full of the theoretical significance andthe project application value to improve dynamic and static performance of the robotbased on the flexible filter transmission mechanism. The adaptive dynamic surfacecontrol based on RBF network of the robot can overcome friction, chaotic motion,outside interference, parameter uncertain, etc to achieve the high control performance ofthe system.
Main research results of the thesis are as follows:
Firstly, the RBF network dynamic surface control method is proposed for a class ofN-order system with output constraint, as a theoretical basis, and then chaos controlproblem of the brushless DC motor drive system is researched. Aiming at the controlproblem of the brushless DC motor system with uncertain time-delay, considering theoutput constraints and using universal approximation properties of RBF network, thenan adaptive RBF network dynamic surface control method of the brushless DC motorchaotic system is proposed. Asymptotically tracking stable and uniformly ultimateboundedness of the tracking error are ensured without violation of the constraint.Experiment results show that the designed controller has convergence, chaotic behaviorsuppressession ability and high-robustness for system parameters perturbation.
Secondly, the speed tracking and position tracking control problems of thepermanent magnet synchronous motor chaotic system are studied. Consideringunknown bounded external disturbances, controlling direction uncertainty and system parameter uncertainty, applying fast terminal sliding mode and dynamic surface methodand combining with adaptive technology and Nussbaum gain to estimate the unknownfunction in the system, then a fast terminal sliding mode speed tracking control based onone-parameter weights RBF network and position tracking control based on Nussbaumgain RBF network dynamic surface of the PMSM chaotic system are proposedrespectively. The experiment results show that the proposed control schemes have agood convergence and robustness. Compared with the index sliding mode control, theproposed method has clear advantages.
Thirdly, a RBF network dynamic surface control of flexible filtering drivemechanism is researched. Selecting the robot drive joints—flexible filtering drivemechanism as the research object, mechanical design of the transmission mechanism ofthe flexible filtering is conducted. Then taking into account the flexible deformation,friction and transmission errors, system efficiency and other nonlinear factors effect,mathematical models of flexible filter drive mechanism is established. Then, anadaptive dynamic surface of flexible filtering drive mechanism is proposed based onRBF network. Besides, using Lyapunov theory proves the stability of the proposedcontrol method. The results show that the proposed control method has a strongrobustness and stability. Compared with the PID control, the proposed control methodhas obvious advantages, which may extend to the electromechanical transmission.
Finally, the RBF network control problem of the robot based on the flexible filterdrive mechanism is researched. Selecting the robot with friction, disturbance anduncertain parameters, etc as the research object, an adaptive RBF network backsteppingcontroller with one-parameter weights for the industry robot is designed based on theflexible filtering drive mechanism firstly. Secondly, a RBF network dynamic surfacecontroller with the disturbance observer of the mobile robot is designed based on theflexible filtering drive mechanism. In the meantime, the Lyapunov theory is employedto ensure the stability of the proposed control method. Containing to compare with thetraditional dynamic surface control, the experiment results show that the proposedcontrollers have a higher tracking accuracy, stronger robustness and bettercompensation ability for disturbance.
引文
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