An improved hybrid position/force controller design of a flexible robot manipulator is presented using a sliding observer. The friction between the end effector and the environment is considered and compensated. For s...An improved hybrid position/force controller design of a flexible robot manipulator is presented using a sliding observer. The friction between the end effector and the environment is considered and compensated. For systematic reasons the controller is designed taking into consideration the rigid link subsystems and the flexible joints. The proposed control system satisfies the stability of the two subsystems and copes with the uncertainty of robot dynamics. A sliding observer is designed to estimate the time derivative of the torque applied as input to the rigid part of the robot. For the stability of the observer, it is assumed that the uncertainty of the observed system is bounded. A MRAC algorithm is used for the estimation of the friction forces at the contact point between the end effector and the environment. Finally simulation and experimental results are given, to demonstrate the effectiveness of the proposed 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
提出一种具有自适应预测时域的输入重构弹性自触发模型预测控制(self-triggered model predictive control,ST-MPC)算法,平衡机器人系统网络安全和资源受限之间的矛盾.首先,基于自触发非周期采样特征和虚假数据注入(false data injectio...提出一种具有自适应预测时域的输入重构弹性自触发模型预测控制(self-triggered model predictive control,ST-MPC)算法,平衡机器人系统网络安全和资源受限之间的矛盾.首先,基于自触发非周期采样特征和虚假数据注入(false data injection,FDI)攻击模型设计输入重构机制,确保机器人系统可快速重构,能削弱FDI攻击影响的可行控制序列.其次,结合输入重构机制设计关键数据选取条件和预测时域调节机制,从实现最大化触发间隔和降低优化问题复杂度两个方面降低资源消耗.然后,基于输入重构和预测时域调节机制设计弹性ST-MPC镇定控制算法,并推导FDI攻击下算法的可行性和闭环系统稳定性条件.最后,通过仿真实验验证所提出算法能够在抵御FDI攻击前提下保持较好的控制性能及资源利用率.展开更多
文摘An improved hybrid position/force controller design of a flexible robot manipulator is presented using a sliding observer. The friction between the end effector and the environment is considered and compensated. For systematic reasons the controller is designed taking into consideration the rigid link subsystems and the flexible joints. The proposed control system satisfies the stability of the two subsystems and copes with the uncertainty of robot dynamics. A sliding observer is designed to estimate the time derivative of the torque applied as input to the rigid part of the robot. For the stability of the observer, it is assumed that the uncertainty of the observed system is bounded. A MRAC algorithm is used for the estimation of the friction forces at the contact point between the end effector and the environment. Finally simulation and experimental results are given, to demonstrate the effectiveness of the proposed controller.
基金Supported by National Natural Science Foundation of China（60874002） Key Project of Shanghai Education Committee （09ZZ158） Leading Academic Discipline Project of Shanghai Municipal Government （S30501）
基金Supported by National Natural Science Foundation of China （61273108）, the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, the Fundamental Research Funds for the Central Universities （106112013CD- JZR175501）
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。
文摘提出一种具有自适应预测时域的输入重构弹性自触发模型预测控制(self-triggered model predictive control,ST-MPC)算法,平衡机器人系统网络安全和资源受限之间的矛盾.首先,基于自触发非周期采样特征和虚假数据注入(false data injection,FDI)攻击模型设计输入重构机制,确保机器人系统可快速重构,能削弱FDI攻击影响的可行控制序列.其次,结合输入重构机制设计关键数据选取条件和预测时域调节机制,从实现最大化触发间隔和降低优化问题复杂度两个方面降低资源消耗.然后,基于输入重构和预测时域调节机制设计弹性ST-MPC镇定控制算法,并推导FDI攻击下算法的可行性和闭环系统稳定性条件.最后,通过仿真实验验证所提出算法能够在抵御FDI攻击前提下保持较好的控制性能及资源利用率.
