A pneumatic parallel platform driven by an air cylinder and three circumambient pneumatic muscles was considered. Firstly, a mathematical model of the pneumatic servo system was developed for the MIMO nonlinear model-...A pneumatic parallel platform driven by an air cylinder and three circumambient pneumatic muscles was considered. Firstly, a mathematical model of the pneumatic servo system was developed for the MIMO nonlinear model-based controller designed. The pneumatic muscles were controlled by three proportional position valves, and the air cylinder was controlled by a proportional pressure valve. As the forward kinematics of this structure had no analytical solution, the control strategy should be designed in joint space. A cross-coupling integral adaptive robust controller(CCIARC) which combined cross-coupling control strategy and traditional adaptive robust control(ARC) theory was developed by back-stepping method to accomplish trajectory tracking control of the parallel platform. The cross-coupling part of the controller stabilized the length error in joint space as well as the synchronization error, and the adaptive robust control part attenuated the adverse effects of modelling error and disturbance. The force character of the pneumatic muscles was difficult to model precisely, so the on-line recursive least square estimation(RLSE) method was employed to modify the model compensation. The projector mapping method was used to condition the RLSE algorithm to bound the parameters estimated. An integral feedback part was added to the traditional robust function to reduce the negative influence of the slow time-varying characteristic of pneumatic muscles and enhance the ability of trajectory tracking. The stability of the controller designed was proved through Laypunov's theory. Various contrast controllers were designed to testify the newly designed components of the CCIARC. Extensive experiments were conducted to illustrate the performance of the controller.展开更多
针对Stewart平台的六自由度(six degrees of freedom,6-DOF)轨迹跟踪问题,提出一种基于神经网络的非奇异终端滑模控制方法并应用于Stewart平台的位置姿态控制中。通过分析Stewart平台的位置反解和速度反解,建立运动学方程,利用牛顿-欧...针对Stewart平台的六自由度(six degrees of freedom,6-DOF)轨迹跟踪问题,提出一种基于神经网络的非奇异终端滑模控制方法并应用于Stewart平台的位置姿态控制中。通过分析Stewart平台的位置反解和速度反解,建立运动学方程,利用牛顿-欧拉方程建立动力学方程,并结合加速度反解得到了平台的状态空间表达式;基于非奇异滑模面函数,设计非奇异终端滑模控制律。考虑到径向基函数(radial Basis function,RBF)神经网络的逼近特性,采用RBF神经网络对模型未知部分进行自适应逼近,并利用Lyapunov第二法设计了自适应律;通过仿真证明控制器设计的有效性。仿真结果表明,相比于比例积分微分(proportional integral derivative,PID)控制器,提出的RBF神经网络非奇异终端滑模控制器具有更好的轨迹跟踪精度和动态特性。展开更多
500m口径球面射电望远镜(Five hundred meter aperture spherical radio telescope,FAST)的馈源支撑与指向跟踪机构由宏微并联机器人系统构成,大跨度柔索驱动的宏并联机器人保证系统的大工作空间,精密电动缸驱动的Stewart平台作为微并...500m口径球面射电望远镜(Five hundred meter aperture spherical radio telescope,FAST)的馈源支撑与指向跟踪机构由宏微并联机器人系统构成,大跨度柔索驱动的宏并联机器人保证系统的大工作空间,精密电动缸驱动的Stewart平台作为微并联机器人保证系统的末端精度并扩展其伺服带宽。为了降低宏并联机器人的柔性对末端定位精度的影响,提出基于并联机构学原理的三维机动目标解耦跟踪预测算法,对馈源舱的运动进行跟踪预测。引入自适应交互算法解决PID参数的实时调整,设计自适应交互PID监督控制器,根据馈源舱的预测运动和馈源平台的目标轨迹产生电动缸规划级控制量。此外,在电动缸执行级采用带前馈的数字伺服滤波器实现电动缸的高精度轨迹跟踪。FAST50m缩尺模型试验表明,结合解耦预测算法对馈源舱的运动预测,自适应交互PID监督控制器效果良好,能够确保宏微并联机器人系统在以期望的跟踪速度运行时,获得完全满足控制要求的定位精度和指向精度。展开更多
基金Project(51375430)supported by the National Natural Science Foundation of China
文摘A pneumatic parallel platform driven by an air cylinder and three circumambient pneumatic muscles was considered. Firstly, a mathematical model of the pneumatic servo system was developed for the MIMO nonlinear model-based controller designed. The pneumatic muscles were controlled by three proportional position valves, and the air cylinder was controlled by a proportional pressure valve. As the forward kinematics of this structure had no analytical solution, the control strategy should be designed in joint space. A cross-coupling integral adaptive robust controller(CCIARC) which combined cross-coupling control strategy and traditional adaptive robust control(ARC) theory was developed by back-stepping method to accomplish trajectory tracking control of the parallel platform. The cross-coupling part of the controller stabilized the length error in joint space as well as the synchronization error, and the adaptive robust control part attenuated the adverse effects of modelling error and disturbance. The force character of the pneumatic muscles was difficult to model precisely, so the on-line recursive least square estimation(RLSE) method was employed to modify the model compensation. The projector mapping method was used to condition the RLSE algorithm to bound the parameters estimated. An integral feedback part was added to the traditional robust function to reduce the negative influence of the slow time-varying characteristic of pneumatic muscles and enhance the ability of trajectory tracking. The stability of the controller designed was proved through Laypunov's theory. Various contrast controllers were designed to testify the newly designed components of the CCIARC. Extensive experiments were conducted to illustrate the performance of the controller.
文摘针对Stewart平台的六自由度(six degrees of freedom,6-DOF)轨迹跟踪问题,提出一种基于神经网络的非奇异终端滑模控制方法并应用于Stewart平台的位置姿态控制中。通过分析Stewart平台的位置反解和速度反解,建立运动学方程,利用牛顿-欧拉方程建立动力学方程,并结合加速度反解得到了平台的状态空间表达式;基于非奇异滑模面函数,设计非奇异终端滑模控制律。考虑到径向基函数(radial Basis function,RBF)神经网络的逼近特性,采用RBF神经网络对模型未知部分进行自适应逼近,并利用Lyapunov第二法设计了自适应律;通过仿真证明控制器设计的有效性。仿真结果表明,相比于比例积分微分(proportional integral derivative,PID)控制器,提出的RBF神经网络非奇异终端滑模控制器具有更好的轨迹跟踪精度和动态特性。
文摘500m口径球面射电望远镜(Five hundred meter aperture spherical radio telescope,FAST)的馈源支撑与指向跟踪机构由宏微并联机器人系统构成,大跨度柔索驱动的宏并联机器人保证系统的大工作空间,精密电动缸驱动的Stewart平台作为微并联机器人保证系统的末端精度并扩展其伺服带宽。为了降低宏并联机器人的柔性对末端定位精度的影响,提出基于并联机构学原理的三维机动目标解耦跟踪预测算法,对馈源舱的运动进行跟踪预测。引入自适应交互算法解决PID参数的实时调整,设计自适应交互PID监督控制器,根据馈源舱的预测运动和馈源平台的目标轨迹产生电动缸规划级控制量。此外,在电动缸执行级采用带前馈的数字伺服滤波器实现电动缸的高精度轨迹跟踪。FAST50m缩尺模型试验表明,结合解耦预测算法对馈源舱的运动预测,自适应交互PID监督控制器效果良好,能够确保宏微并联机器人系统在以期望的跟踪速度运行时,获得完全满足控制要求的定位精度和指向精度。
