基于计算转矩控制结构的机械手鲁棒神经网络补偿控制

提出了一种新的不确定性机器人跟踪控制策略,文中基于计算转矩控制结构,采用了函数链网络实现一个神经网络补偿器,并叠加一个鲁棒控制项,以补偿模型的不确定性部分,另外,还考虑了神经网络逼近误差非一致有界的情形,设计了自适应的鲁棒控制项,算法可保证跟踪误差及神经网络权估计最终一致有界,与其它有关基于计算转矩控制的方法相比,该算法既不需要测量关节角加速度,也不要求惯性矩阵已知,理论和仿真均证明了算法和可靠性和有效性。     

在线变结构模糊神经网络的自适应内模控制

本文提出一种在线变结构补偿模糊神经网络训练算法.该在线变结构算法,使得出的网络结构简单.并且由于该网络引入了补偿模糊神经元,使网络能对模糊规则进行在线的训练.将此算法应用到仿射非线性动态系统和大时滞线性动态系统的内模控制中.仿真结果表明,该方法能有效的控制动态过程,具有较好的自适应性和鲁棒性.     

基于神经网络的一类非线性连续系统的稳定自适应控制*

本文将神经网络作为非线性系统的模型，提出能够对一类非线性连续系统进行有效控制的自适应控制结构和算法，该控制方案不仅能经类非线性系统的跟踪控制问题，而且由于将变结构控制技术动用于其中，整个闭环控制系统还能克服许多神经网络控制系统中存在的稳定性问题。由稳定性理论可推证整个闭环控制系统渐近稳定和参数和渐近收敛的特性。     

机器人计算力矩不确定性的神经网络补偿控制*

提出一种由计算力矩控制器和神经网络补偿控制器相结合的控制方案,探讨了用神经网络补偿机器人计算力矩不确定性的方法,推导了网络权值的自适应调整律,并证明了系统的稳定性和误差的收敛性.该方案结构简单、鲁棒性强,且神经网络补偿器有较好的适应性,无须事先知道机器人动力学参数和结构的精确值.对机器人轨迹跟踪的仿真结果表明,所提方案具有很好的鲁棒性和抗干扰能力.     

时变大时滞系统参数辨识的仿真研究

研究了时变大时滞系统的参数辨识问题.大时滞系统大多采用补偿控制方法,但是补偿控制方法需要系统的精确数学模型,因而获得大时滞系统的数学模型成为了补偿控制的关键,时变特性使问题复杂化,从而影响了大时滞系统的控制精度.为解决上述问题,提出了一种神经网络的参数辨识策略,利用一个神经元对系统的时滞参数进行辨识,从而可以将时滞从系统模型中分离出来,可利用一个RBF神经网络模型辨识系统的其它参数,使神经元的输出作为RBF神经网络的一个输入,从而实现了串-并联结构的双神经网络拓扑.拓扑结构可以比串级的神经网络提高训练速度,因而也就更适合于实时控制.针对工业锅炉回水温度控制系统的仿真结果验证了所提辨识算法的正确性.     

基于神经网络补偿的机器人稳定自适应控制

针对建模不精确的机器人,提出了一种基于神经网络补偿的机器人轨迹跟踪稳定自适应控制方法,文中通过设计神经网络补偿器和自适应鲁棒控制项,有效地补偿了模型的不确定性部分和网络逼近误差.由于算法包含有补偿神经网络逼近误差的鲁棒控制项,实际应用中对神经网络规模的要求可以降低;而且神经网络连接权是在线调整的,不需要离线学习过程.理论表明算法能够保证跟踪误差及神经网络连接权估计最终一致有界,仿真结果也验证了算法的有效性.     

基于DSC后推法的非线性系统的鲁棒自适应NN控制

针对一类具有不确定系统函数和方向未知的不确定增益函数的非线性系统, 提出了一种鲁棒自适应神经网络控制算法. 本算法采用RBF神经网络(Radial based function neural network, RBF NN)逼近模型不确定性, 外界干扰和建模误差采用非线性阻尼项进行补偿, 将动态面控制(Dynamic surface control, DSC)与后推方法结合, 消除了反推法的计算膨胀问题, 降低了控制器的复杂性; 尤其是采用Nussbaum函数处理系统中方向未知的不确定虚拟控制增益函数, 不仅可以避免可能存在的控制器奇异值问题, 而且还能使得整个系统的在线学习参数显著减少, 与DSC方法优点结合, 使得控制算法的计算量大为减少, 便于计算机实现. 稳定性分析证明了所得闭环系统是半全局一致最终有界(Semi-global uniformly ultimately bounded, SGUUB)的, 并且跟踪误差可以收敛到原点的一个较小邻域. 最后, 计算机仿真结果表明了本文所提出控制器的有效性.     

配电静止同步补偿器模糊控制的优化设计

针对复杂配电静止同步补偿器模糊控制参数难以调整的问题,建立了基于直接电流控制和简化FBD检测法的配电静止同步补偿器数学模型,在传统模糊PI控制原理的基础上,提出了基于混沌粒子群算法的模糊控制参数优化设计方法。仿真和试验结果表明,该优化方法能精准控制直流侧电压并使其抗干扰能力增强,同时使补偿电流能够快速准确跟踪补偿电流指令值,大幅提高了控制效果。     

基于自抗扰迭代学习控制的开关磁阻电机转矩脉动抑制

针对开关磁阻电机双凸极结构和磁路饱和非线性导致开关磁阻电机转矩脉动大的问题, 本文基于一种新 型的数据驱动控制方法——自抗扰迭代学习控制, 将开关磁阻电机看成是空间重复运动对象, 设计空间迭代域补偿 机制用于抑制电机非线性特性所带来的换相转矩脉动. 提出了基于空间域扩张状态扰动补偿机制的转矩分配控制 策略, 在无法精确获取电机非线性模型的情形下, 设计了非线性转矩补偿器和电流控制器对各相电流进行精确补偿 和精确跟踪控制. 仿真研究表明, 基于自抗扰迭代学习的控制策略能显著快速地抑制开关磁阻电机的转矩脉动, 可 望在开关磁阻电机的实际应用中发挥作用.     

基于神经网络的解耦控制新方法及其应用

本提出两种基于神经网络的多变量解耦控制方法。方法１通过设计神经网络补偿装置，使得包括补偿神经网络在内的广义对象的Ｂｒｉｓｔｏｌ第一系数矩阵为对角阵；方法２首先定义了神经网络的串联，并联和反馈运算，然后在此基础上设计一个神经网络补偿装置，使得包括补偿神经网络在内的广义对象矩阵为对角阵。将其用于某二元精馏塔的塔顶和塔底组分控制，仿真结果证实了本方法的有效性。     

一类关于不确定性机器人的鲁棒控制策略

基于计算力矩结构,研究参数和结构不确定的机器人轨迹跟踪的鲁棒控制策略.其 特点是利用了机器人不确定动力学的集中包络函数,在该包络函数已知的情况下,设计的非 线性连续补偿控制律能够有效消除系统的不确定性影响,保证系统达到三种不同的稳定性结 果.另外,在该包络函数参数未知时,还没计了一个新颖的在线辨识器,可保证系统指数意义 下的渐近收敛或一致有界.     

Brief communication: Uniform ultimate boundedness of a fuzzy logic controlled industrial robot

This article addresses the proof of uniform ultimate boundedness of a fuzzy logic controller plus a computed torque control scheme applied to trajectory tracking control of robotic manipulators. Further improvement of the performance of this fuzzy logic control scheme is achieved through automatic tuning of a weight parameter α leading to a self‐tuning fuzzy logic compensator. Experimental results demonstrate the effectiveness of the computed torque and fuzzy compensation scheme, as well as the self‐tuning fuzzy logic controller, applied to an industrial CRS Robotics Corporation A460 robot during a trajectory tracking task. © 2001 John Wiley & Sons, Inc.     

A comparison of model-based path control algorithms for direct-drive SCARA robots

Various advanced control strategies are applied to a direct-drive SCARA robot and studied in computer simulations. Besides computed torque control and direct adaptive control, heuristic optimal control, a new path control scheme for robotic manipulators, is included in the comparison study. PD control, the traditional robot control method, is used for generating a comparing baseline. While all schemes are applied for the same tracking task, the effect of modelling errors and measurement noise is considered in robot performance evaluation. Simulation results show that (1) without model errors, all advanced control schemes can achieve higher tracking accuracy than PD control; (2) with a random measurement error of 1%, computed torque and direct adaptive control methods are inferior to PD control; (3) heuristic control proves to be the most robust control scheme in case of mixed model and measurement errors.     

Decoupling and tracking control for elastic joint robots with coupled joint structure

The paper deals with the modeling, identification, and control of a flexible joint robot developed for medical applications at the German Aerospace Center (DLR). In order to design anthropomorphic kinematics, the robot uses a coupled joint structure realized by a differential gearbox, which however leads to strong mechanical couplings inside the coupled joints and must be taken into account. Therefore, a regulation MIMO state feedback controller based on modal analysis is developed for each coupled joint pair, which consists of full state feedback (motor position, link side torque, as well as their derivatives). Furthermore, in order to improve position accuracy and simultaneously keep good dynamic behavior of the MIMO state feedback controller, a cascaded tracking control scheme is proposed, based on the MIMO state feedback controller with additional feedforward terms (desired motor velocity, desired motor acceleration, derivative of the desired torque), which are computed in a computed torque controller and take the whole rigid body dynamics into account. Stability analysis is shown for the complete controlled robot. Finally, experimental results with the DLR medical robot are presented to validate the practical efficiency of the approaches.     

不确定性机器人的神经网络补偿控制

本文研究具有不确定性的机器人的轨迹跟踪控制问题。提出了一种由计算力矩控制器和神经网络补偿控制器构成的控制方案。探讨了一种用神经网络估计机器人系统不确定性的途径。给出了神经补偿控制器的设计方法，并证明了闭环系统的收敛性。仿真结构表明所提方案具有很好的鲁棒性和抗干扰能力。     

Adaptive RBF neural network control of robot with actuator nonlinearities

In this paper, an adaptive neural network control scheme for robot manipulators with actuator nonlinearities is presented. The control scheme consists of an adaptive neural network controller and an actuator nonlinearities compensator. Since the actuator nonlinearities are usually included in the robot driving motor, a compensator using radial basis function (RBF) network is proposed to estimate the actuator nonlinearities and eliminate their effects. Subsequently, an adaptive neural network controller that neither requires the evaluation of inverse dynamical model nor the time-consuming training process is given. In addition, GL matrix and its product operator are introduced to help prove the stability of the closed control system. Considering the adaptive neural network controller and the RBF network compensator as the whole control scheme, the closed-loop system is proved to be uniformly ultimately bounded (UUB). The whole scheme provides a general procedure to control the robot manipulators with actuator nonlinearities. Simulation results verify the effectiveness of the designed scheme and the theoretical discussion.     

Smooth trajectory tracking of three-link robot: a self-organizingCMAC approach

A neuro fuzzy system which is embedded in the conventional control theory is proposed to tackle physical learning control problems. The control scheme is composed of two elements. The first element, the fuzzy sliding mode controller (FSMC), is used to drive the state variables to a specific switching hyperplane or a desired trajectory. The second one is developed based on the concept of the self organizing fuzzy cerebellar model articulation controller (FCMAC) and adaptive heuristic critic (AHC). Both compose a forward compensator to reduce the chattering effect or cancel the influence of system uncertainties. A geometrical explanation on how the FCMAC algorithm works is provided and some refined procedures of the AHC are presented as well. Simulations on smooth motion of a three-link robot is given to illustrate the performance and applicability of the proposed control scheme.     

Flatness Based Control of an Industrial Robot Joint Using Secondary Encoders

Due to their compliant structure, industrial robots without precision-enhancing measures are only to a limited extent suitable for machining applications. Apart from structural, thermal and bearing deformations, the main cause for compliant structure is backlash of transmission drives. This paper proposes a method to improve trajectory tracking accuracy by using secondary encoders and applying a feedback and a flatness based feed forward control strategy. For this purpose, a novel nonlinear, continuously differentiable dynamical model of a flexible robot joint is presented. The robot joint is modeled as a two-mass oscillator with pose-dependent inertia, nonlinear friction and nonlinear stiffness, including backlash. A flatness based feed forward control is designed to improve the guiding behaviour and a feedback controller, based on secondary encoders, is implemented for disturbance compensation. Using Automatic Differentiation, the nonlinear feed forward controller can be computed in a few microseconds online. Finally, the proposed algorithms are evaluated in simulations and experimentally on a real KUKA Quantec KR300 Ultra SE.