空间绳系机器人目标抓捕鲁棒自适应控制器设计

针对空间绳系机器人（Tethered space robot,TSR）目标抓捕过程中的稳定控制问题,建立空间绳系机器人系统模型,根据阻抗控制原理,设计基于位置的阻抗控制方法;针对空间绳系机器人系统的模型不确定性问题,利用神经网络对不确定性进行估计补偿,设计鲁棒项对空间系绳干扰和神经网络估计误差的影响进行抑制,在此基础上设计空间绳系机器人目标抓捕鲁棒自适应稳定控制器,并进行稳定性证明.最后对设计的控制器进行仿真验证.作为对比,对无鲁棒项自适应的稳定控制器进行仿真.仿真结果表明,设计的基于阻抗控制的鲁棒自适应控制可以实现对空间绳系机器人目标抓捕过程中的稳定控制,与无鲁棒项自适应的稳定控制器仿真结果相比,本文采用的鲁棒自适应控制方法可以有效地对不确定性进行补偿,控制过程中超调量更小,收敛时间更短,并且控制精度更高.     

漂浮基空间机器人的径向基神经网络鲁棒自适应控制

针对一类同时具有参数及非参数不确定性的自由漂浮空间机器人系统的轨迹跟踪问题,采用了一种RBF神经网络的自适应鲁棒补偿控制策略.对于系统的参数不确定性,通过对径向基神经网络来自适应学习并补偿,逼近误差通过滑模控制器消除,神经网络权重的自适应修正规则基于Lyapunov函数方法得到;而非参数不确定通过鲁棒控制器来实时自适应...     

空间机器人地面实验系统的一种鲁棒自适应控制方法

本庆于李亚普诺夫直接方法,针对具有参数及模型不确定性的空间机器人地面实验系统。提出了一种新的鲁棒自适应控制器。该控制器由三部分组成：线性ＰＤ反馈项、补偿动力学的自适应控制项和补偿建模不确定性的鲁棒控制项,控制策略能有效地克服未建模的摩擦力和外部扰动以及吊丝配重系统未补偿掉的重力影响,并且能保证全局一致最后有界。仿真表明了算法的可行性和有效性。     

基于一类饱和函数的机器人混合鲁棒/自适应控制

提出一类不需要线性PD反馈的混合鲁棒/自适应控制策略,用于不确定性机器人的轨迹 跟踪.其控制结构由一个补偿参数不确定性的自适应控制器和补偿非参数不确定性的鲁棒控 制器构成. 其主要特点是基于一类饱和型函数,提出了一类新颖的鲁棒控制器和非线性滑动 变量的设计方法.基于Lyapunov方法的理论分析和计算机仿真,均保证设计的控制策略能够消 除系统所有的不确定性影响,并达到全局的渐近稳定.     

基于多种群协同进化算法的绳索牵引并联机器人末端位置误差补偿

对于绳索牵引并联机器人来说,影响其末端位置精度的模型不确定性主要包括几何参数误差和非几何参数误差.这两种不同类型的误差具有非常强的非线性且相互耦合,难以通过传统的标定手段来进行参数标定.针对这一问题,提出了一种基于神经网络的末端位置误差补偿方法.将两种不同类型的参数误差等效视作伪误差,通过神经网络来逼近伪误差造成的末端位置误差曲线,建立末端位置误差与绳索长度之间的映射关系,并在关节空间中进行位置误差补偿.为了提高神经网络的拟合精度,设计了基于多种群协同进化算法和反向传播算法的神经网络优化方法,该优化方法能够同时优化网络的权值、阈值和结构,提高神经网络的泛化能力和拟合精度.在实际3自由度绳索牵引并联机器人上进行了位置误差补偿实验,结果表明补偿后的位置误差均值从6.64 mm下降到1.08 mm,轨迹误差均值从7.5 mm下降到1.6 mm,末端位置的精度得到了显著提高.     

双起升桥吊双吊具同步协调控制

针对双起升桥式吊车双吊具同步协调运行过程中普遍存在的系统参数变化和摩擦等不确定扰动问题, 本文基于双吊具的非线性感应电机动态数学模型及其耦合动力学模型, 提出了一种时变分层增量式滑模控制和自适应补偿相结合的双吊具同步协调控制方法. 该方法首先利用时变滑模技术实现了控制器在滑模趋近阶段的鲁棒性控制, 并采用分层增量形式的滑模面设计方法简化了控制器参数选取. 然后, 采用自适应补偿技术抑制了双吊具运行中存在的不确定扰动, 同时减小了切换函数的增益值. 此外, 在切换函数设计中采用了指数趋近技术, 使滑模控制器的抖振现象明显降低. 最后, 利用Lyapunov方法证明了该方法的全局稳定性和收敛性, 并通过数值仿真和物理实验验证了该方法的有效性.     

多船舶的神经网络自适应同步控制

为了实现多艘船舶的同步运动,提出了多艘船的自适应同步控制策略.由于船舶模型参数不确定,采用了径向基神经网络来逼近不确定项,建立船舶的数学模型;其次,利用了数学图论来描述船舶之间的信息交流;接着为每艘船预先设定期望路径,并且将船舶的同步误差引入到控制器,控制船舶的运动使其不但沿各自期望路径运动,而且与多艘船舶保持同步;利用李雅普诺夫稳定性理论,证明了所设计的自适应同步控制器的稳定性;通过对三艘船舶的仿真表明,所提出神经网络自适应同步控制可以很好地解决多艘船同步控制问题.     

压水堆功率跟踪自适应保性能控制器设计

针对压水堆动态模型的高度非线性和不确定性特点,本文提出一种自适应保性能跟踪控制器(adaptive guaranteed cost control,AGCC)设计方法.首先以堆芯的点堆方程为基础,引入功率跟踪误差的积分项,构造反应堆的增广状态空间模型,再结合线性参数变化(linear parameter varying,LPV)理论,建立了堆芯系统的多胞LPV模型.该控制器的控制输入由状态反馈控制和不确定性补偿组成,结合保性能控制理论和多胞模型理论,求解线性矩阵不等式得到变增益状态反馈矩阵,确保闭环系统全局渐近稳定;利用李亚普诺夫稳定理论得到不确定性参数的自适应律,实现对系统不确定性的动态补偿.仿真结果表明,该控制器不仅对系统不确定项具有自适应性,而且有较好的负荷跟踪性能.     

直升机强跟踪自适应变稳控制器设计

变稳控制是直升机飞行模拟的关键技术之一,气动参数的不确定性是其设计的困难所在.对此,提出了利用双目标直升机模型构造原型直升机的强跟踪自适应变稳控制器,它将强跟踪的反馈补偿机构和自适应控制参数设计相分离,简化了控制设计,同时实现了提高自适应参数修正过程的系统稳定裕度和显著降低跟踪误差的目的.仿真实验结果验证了所提出方法的有效性和可行性.     

直升机强跟踪自适应变稳控制器设计

变稳控制是直升机飞行模拟的关键技术之一, 气动参数的不确定性是其设计的困难所在. 对此, 提出了利用双目标直升机模型构造原型直升机的强跟踪自适应变稳控制器, 它将强跟踪的反馈补偿机构和自适应控制参数设计相分离, 简化了控制设计, 同时实现了提高自适应参数修正过程的系统稳定裕度和显著降低跟踪误差的目的. 仿真实验结果验证了所提出方法的有效性和可行性.     

Robust adaptive control of uncertain force/motion constrained nonholonomic mobile manipulators

In this paper, force/motion tracking control is investigated for nonholonomic mobile manipulators with unknown parameters and disturbances under uncertain holonomic constraints. The nonholonomic mobile manipulator is transformed into a reduced chained form, and then, robust adaptive force/motion control with hybrid variable signals is proposed to compensate for parametric uncertainties and suppress bounded disturbances. The control scheme guarantees that the outputs of the dynamic system track some bounded auxiliary signals, which subsequently drive the kinematic system to the desired trajectory/force. Simulation studies on the control of a wheeled mobile manipulator are used to show the effectiveness of the proposed scheme.     

Adaptive motion/force control of uncertain nonholonomic mobile manipulator with estimation of unknown external force

This paper describes a stable adaptive motion/force control of uncertain nonholonomic mobile manipulator with the consideration of external force. As it is well known, unexpected external force makes the motion of the system unstable since there are no fixed points in the stationary coordinate. Here, a novel adaptive control scheme is utilized to estimate and compensate the unknown external force exerted to the end-effector even if the parameters of the system are uncertain. The important advantages of this approach are to achieve estimation without the requirement of force-sensing feedback and the knowledge of the system dynamic model. The update laws for the force and the parameters are derived from a Lyapunov function to guarantee the control system stability. Furthermore, a unified operational space dynamic formulation is presented to solve the problem of redundancy. As a result, the desired end-effector and platform trajectories are simultaneously tracked with a perfect coordination between the two subsystems. Therefore, the proposed controller proves that it can not only guarantee the stability, but also the tracking performance of the system in the task space. The effectiveness of the proposed algorithm is evaluated through extensive simulations and they demonstrate the stability, tracking trajectories and feasibility in estimating the external force and the dynamic uncertainties.     

基于神经网络的不确定移动机器人鲁棒自适应跟踪控制

针对含运动学未知参数以及动力学模型不确定的非完整轮式移动机器人轨迹跟踪问题,基于Radical Basis Function(径向基函数)神经网络,提出了一种鲁棒自适应控制器.首先,考虑移动机器人运动学参数未知的情况,提出了一种含自适应参数的运动学控制器,用以补偿参数不确定性导致的系统误差;其次,利用神经网络控制技术,对于机器人在移动中动力学模型不确定问题,提出了一种具有鲁棒性的动力学控制器,使得移动机器人可以在不知道具体动力学模型的情况下跟踪到目标轨迹;最后利用Lyapunov稳定性理论证明了整个系统的稳定性.通过数值仿真验证了所设计的控制器的可行性.     

Hybrid function projective synchronization of chaotic systems with uncertain time-varying parameters via Fourier series expansion

In this paper, the hybrid function projective synchronization (HFPS) of different chaotic systems with uncertain periodically time-varying parameters is carried out by Fourier series expansion and adaptive bounding technique. Fourier series expansion is used to deal with uncertain periodically time-varying parameters. Adaptive bounding technique is used to compensate the bound of truncation errors. Using the Lyapunov stability theory, an adaptive control law and six parameter updating laws are constructed to make the states of two different chaotic systems asymptotically synchronized. The control strategy does not need to know the parameters thoroughly if the time-varying parameters are periodical functions. Finally, in order to verify the effectiveness of the proposed scheme, the HFPS between Lorenz system and Chen system is completed successfully by using this scheme.     

参数不确定的广义Lorenz系统的同步研究

针对参数不确定的广义Lorenz混沌系统,提出一种新的自适应反馈同步方法。利用Lyapunov稳定性理论,设计了参数不确定的广义Lorenz混沌系统的自适应反馈同步控制器,并给出了参数自适应律的解析式。理论上证明了所设计控制器的正确性,并通过Matlab进行仿真实验,成功地实现了系统状态同步和系统不确定参数的辨识。数值仿真结果验证了所提出方法的可行性和有效性。该方法同步建立时间短,同步精度高,对系统初值没有特殊要求。     

含有非线性不确定参数的电液系统滑模自适应控制

针对含有非线性不确定参数的电液控制系统, 提出了一种滑模自适应控制方法. 该控制方法主要是为了解决由于初始控制容积的不确定性而引起的, 非线性不确定参数自适应律设计的难题. 其主要特点为, 通过定义一个新型的特Lyapunov 函数, 进而构建系统的自适应控制器及参数自适应律, 并结合滑模控制方法及一种简单的鲁棒设计方法, 给出整个电液系统的滑模自适应控制器, 及所有不确定参数的自适应律. 试验结果表明, 采用该控制方法能够取得良好的性能, 尤其可以补偿非线性不确定参数对系统的影响.     

不确定分数阶多涡卷混沌系统自适应重复学习同步控制

研究了不确定分数阶多涡卷混沌系统的自适应重复学习同步控制问题.通过利用滞环函数,设计了一类参数可调的分数阶多涡卷混沌系统.针对这类分数阶多涡卷混沌系统,在考虑非参数化不确定性、周期时变参数化不确定性、常参数化不确定性和外部扰动情况下,提出了一种重复学习同步控制方案.利用自适应神经网络技术补偿了系统中的函数型不确定性,通过自适应重复学习控制技术处理了周期时变参数化不确定性,并利用自适应鲁棒学习项处理了神经网络逼近误差和干扰的影响,实现了主系统和从系统的完全同步.综合利用分数阶频率分布模型和类Lyapunov复合能量函数方法证明了同步误差的学习收敛性.数值仿真验证了所提方法的有效性.     

基于终端滑模机器人模糊自适应路径跟踪控制

对质心位置未知的移动机器人系统设计了基于快速终端滑模的模糊自适应路径跟踪控制方法。该方法采用模糊逻辑系统逼近控制器中的未知函数,基于李亚普诺夫稳定性分析方法对未知参数设计自适应律,并设计鲁棒控制器来补偿逼近误差。该方法不但可以保证闭环系统中的所有信号有界,而且可使跟踪误差在有限时间内收敛到原点的小邻域内。仿真结果验证了方法的有效性。     

Motion coordination and reactive control of autonomous multi-manipulator systems

The emerging field of service robots demands new systems with increased flexibility. The flexibility of a robot system can be increased in many different ways. Mobile manipulation—the coordinated use of manipulation capabilities and mobility—is an approach to increase robots flexibility with regard to their motion capabilities. Most mobile manipulators that are currently under development use a single arm on a mobile platform. The use of a two-arm manipulator system allows increased manipulation capabilities, especially when large, heavy, or non-rigid objects must be manipulated. This article is concerned with motion control for mobile two-arm systems. These systems require new schemes for motion coordination and control. A coordination scheme called transparent coordination is presented that allows for an arbitrary number of manipulators on a mobile platform. Furthermore, a reactive control scheme is proposed to enable the platform to support sensor-guided manipulator motion. Finally, this article introduces a collision avoidance scheme for mobile two-arm robots. This scheme surveys the vehicle motion to avoid platform collisions and arm collisions caused by self-motion of the robot. © 1996 John Wiley & Sons, Inc.     

Motion/force control scheme for electrically driven cooperative multiple mobile manipulators

This paper presents the motion and force control problem of rigid-link electrically driven cooperative mobile manipulators handling a rigid object. Although, the motion/force control problem of cooperative mobile manipulators has been enthusiastically studied. But there is little research on the motion/force control of electrically driven cooperative mobile manipulators. Due to the inclusion of the actuator dynamics with the manipulator’s dynamics, the controller exhibits some important characteristics. For the electromechanical system, we have designed a novel controller at the dynamic level as well as at the actuator level. In the proposed control scheme, at the dynamic level, uncertain non-linear mechanical dynamics is approximated with a hybrid controller containing model-based control scheme combined with model-free neural network based control scheme together with an adaptive bound. The adaptive bound is used to suppress the effects of external disturbances, friction terms, and reconstruction error of the neural network. At the actuator level, for the approximation of the unknown electrical dynamics, the model-free neural network is utilized. The developed control scheme provides that the position tracking errors, as well as the internal force, converge to the desired levels. Additionally, direct current motors are also controlled in such a way that the desired currents and torques can be attained. In order to make the overall system to be asymptotically stable, online learning of the weights and the parameter adaptation of the parameters is utilized in the Lyapunov function. The superiority of the developed control method is carried out with the numerical simulation results and its superior robustness is shown in a comparative manner.