具有H_∞性能的机械手神经自适应跟踪控制

针对非线性不确定机械手系统的轨迹跟踪控制问题,提出一种具有H∞性能的神经自适应控制算法;该算法为机械手系统分别设计了主控制器和监督控制器;主控制器由神经网络控制为基础,基于滑模控制原理得到神经网络权值的自适应律;基于李亚普诺夫稳定性理论和鲁棒控制设计的监督控制器,补偿自适应神经网络对系统不确定项学习的误差,同时使系统具有H∞性能;通过二自由度机械手模型进行仿真实验,仿真结果验证了方法的有效性。     

基于观测器的机械手神经网络自适应控制

提出了一种基于观测器的机械手神经网络自适应轨迹跟随控制器设计方法,这里机 械手的动力学非线性假设是未知的,并且假设机械手仅有关节角位置测量.文中采用一个线 性观测器重构机械手的关节角速度,用神经网络逼近修正的机械手动力学非线性,改进系统 的跟随性能.基于观测器的神经网络自适应控制器能够保证机械手角跟随误差和观测误差的 一致终结有界性以及神经网络权值的有界性,最后给出了机械手神经网络自适应控制器-观 测器设计的主要理论结果,并通过数字仿真验证了所提方法的性能.     

不确定机器人的自适应神经网络控制与学习

针对具有未知动态的电驱动机器人,研究其自适应神经网络控制与学习问题.首先,设计了稳定的自适应神经网络控制器,径向基函数(RBF)神经网络被用来逼近电驱动机器人的未知闭环系统动态,并根据李雅普诺夫稳定性理论推导了神经网络权值更新律.在对回归轨迹实现跟踪控制的过程中,闭环系统内部信号的部分持续激励(PE)条件得到满足.随着PE条件的满足,设计的自适应神经网络控制器被证明在稳定的跟踪控制过程中实现了电驱动机器人未知闭环系统动态的准确逼近.接着,使用学过的知识设计了新颖的学习控制器,实现了闭环系统稳定、改进了控制性能.最后,通过数字仿真验证了所提控制方法的正确性和有效性.     

未知控制方向的迟滞非线性系统预设自适应控制

针对一类含有迟滞特性的未知控制方向严反馈非线性系统,设计了基于误差变换的反步自适应控制器.首先提出动态迟滞算子来扩展输入空间建立神经网络迟滞模型.然后利用径向基函数(RBF)神经网络逼近未知函数,并引入Nussbaum型函数来解决系统未知控制方向问题.最后采用误差变换将误差限定在预设的范围内,并利用反步法设计自适应控制器.该控制方案不仅能够保证跟踪精度,还可以提高系统暂态和稳态性能.仿真结果表明了控制方案的可行性.     

检测与修补移动机械手系统的协调控制技术研究

针对2自由度轮式移动载体与5自由度机械手组成的检测与修补移动机械手进行了运动分析及其控制技术的研究．首先分析了移动机械手的结构特点,描述了移动载体与机械手的动力耦合对机械手系统的性能影响．其次,根据移动机械手动力学的复杂性,提出了移动机械手的运动控制策略,建立了基于神经网络的移动机械手三层智能控制的体系结构,底层是执行层,中间层是处理层,顶层是决策层．处理层包括两个RBF神经网络子控制器,未知的移动载体和机械手的动力学参数分别由各自的RBF神经网络辨识和补偿,形成闭环逼近的控制．应用Matlab编程软件对所建立的RBF神经网络进行训练仿真,最后,在实验环境中对移动机械手的运动进行了相关的实验验证．实验结果验证了移动机械手完成相关任务的有效性和可靠性．     

含未知动态与扰动的非线性系统神经网络嵌入学习控制

针对带有不确定性与扰动的非线性系统的性能优化问题, 提出一种基于神经网络嵌入的学习控制方法. 对一类常见的 Lyapunov 函数导数形式, 将神经网络控制器集成到某种对系统稳定的基准控制器中, 其意义在于将原控制器改进为满足Lyapunov稳定的神经网络参数可调控制器, 从而能够利用先进的神经网络学习技术实现控制器的在线优化. 建立了跟踪误差的等效目标函数, 避免了对系统输入–输出的辨识问题. 建立了一种未知非线性与扰动等效值自适应方法, 并依此方法设计基准控制器. 以RBF (Radial basis function) 反步自适应控制、基于卷积神经网络的滑模控制和深度强化学习控制为对比方法, 对带有死区、饱和、三角函数等数值与物理非线性模型进行仿真分析以测试方法有效性, 并针对上肢康复机器人控制问题进行虚拟实验以验证该方法的实用性. 仿真与实验结果表明, 该方法能在Lyapunov 稳定条件下有效优化基础控制器性能, 对比结果证实了该方法的实用性与先进性.     

任意切换下高能随机系统的神经网络预设控制

针对一类不确定高能随机非线性系统,开展自适应神经网络backstepping控制研究,并保证在任意切换信号下的预设跟踪性能.该高能系统假定系统动态和任意切换信号未知.首先,利用预设性能控制,保证跟踪控制性能;其次,RBF神经网络用来克服未知系统动态,仅用到单一自适应更新参数,从而克服过参数问题;最后,基于公共的Lyapunov稳定性理论提出自适应神经网络控制策略,并减少了学习参数.最终结果表明所设计的公共控制器能保证所有闭环信号半全局最终一致有界,并能在任意切换下保证预设的跟踪性能.仿真结果进一步表明所提出方法的有效性.     

一种基于模糊径向基函数神经网络的自学习控制器

提出了一种新型的基于模糊径向基函数 (RBF)的神经网络学习控制器 ,并应用于电液伺服系统 .由于RBF网络和模糊推理系统具有函数等价性 ,采用模糊经验值方法选取网络中心值和基函数数目 .与一般的神经网络自学习控制器不同 ,以系统动态误差作为网络输入量 ,RBF神经网络控制器学习的是整个系统的动态逆过程 ,因而控制性能明显提高 .对电液位置伺服系统的仿真和实验结果表明 ,该控制方案可以有效提高系统的控制精度和自适应能力     

数据与未建模动态驱动的非线性PID切换控制

本文充分利用系统的数据信息和知识,把数据驱动控制、PID控制与一步超前最优控制策略相结合,提出了数据与未建模动态驱动的非线性PID切换控制方法.该方法首先利用被控对象往往运行在工作点附近的特点及系统丰富可测的数据信息,把被控对象表示成低阶控制器设计模型与高阶非线性项(未建模动态)和的形式.与以往方法的本质区别在于,所提的方法直接将未建模动态分解为前一拍数据与未知增量的和,并充分利用未建模动态可测数据信息补偿系统未知的非线性动态特性,设计非线性PID控制器,对未建模动态的未知增量采用自适应神经模糊推理系统(ANFIS)进行估计,从而设计带有未建模动态增量估计的非线性PID控制器.将控制器的跟踪误差引入切换指标,两个控制器通过切换机制协调控制系统,既保证系统的稳定,同时提高系统的性能.为解决PID控制器参数难以选择的问题,采用一步超前最优控制策略进行参数设计,从理论上给出了PID控制器参数选择的一般原则和方法,推导了保证闭环系统输入输出稳定性的条件;最后,通过数值仿真实验以及在水箱液位控制系统的物理对比实验,实验结果验证了所提算法的有效性和实用性.     

RBFNN结合自适应边界的机器人手臂轨迹跟踪控制系统设计

针对机器人手臂动态模型中存在动态不确定性问题,提出一种结合径向基函数神经网络(RBFNN)和自适应边界控制的机械臂轨迹跟踪方法;利用RBF神经网络在线学习系统中现有的结构化和非结构化不确定性,近似补偿未知动态部分;利用自适应边界来估计非结构化不确定性上的未知边界和神经网络重建误差;通过加权矩阵产生的李雅普诺夫函数证明了该系统具有渐进稳定性;利用三自由度机械臂进行实验,结果表明,相比FFNN控制器,提出的控制器的跟踪误差改进了3～7倍,稳态误差改进了100～1 000倍.     

Intelligent complementary sliding-mode control with dead-zone parameter modification

This paper proposes an intelligent complementary sliding-mode control (ICSMC) system which is composed of a computed controller and a robust controller. The computed controller includes a neural dynamics estimator and the robust compensator is designed to prove a finite L₂-gain property. The neural dynamics estimator uses a recurrent neural fuzzy inference network (RNFIN) to approximate the unknown system term in the sense of the Lyapunov function. In traditional neural network learning process, an over-trained neural network would force the parameters to drift and the system may become unstable eventually. To resolve this problem, a dead-zone parameter modification is proposed for the parameter tuning process to stop when tracking performance index is smaller than performance threshold. To investigate the capabilities of the proposed ICSMC approach, the ICSMC system is applied to a one-link robotic manipulator and a DC motor driver. The simulation and experimental results show that favorable control performance can be achieved in the sense of the L₂-gain robust control approach by the proposed ICSMC scheme.     

An adaptive neural network switching control approach of robotic manipulators for trajectory tracking

In this paper, an adaptive neural network (NN) switching control strategy is proposed for the trajectory tracking problem of robotic manipulators. The proposed system comprises an adaptive switching neural controller and the associated robust compensation control law. Based on the Lyapunov stability theorem and average dwell-time approach, it is shown that the proposed control scheme can guarantee tracking performance of the robotic manipulators system, in the sense that all variables of the closed-loop system are bounded and the effect due to the external disturbance and approximate error of radical basis function (RBF) NNs on the tracking error can be converged to zero in an infinite time. Finally, simulation results on a two-link robotic manipulator show the feasibility and validity of the proposed control scheme.     

Identification and decentralized adaptive control using dynamicalneural networks with application to robotic manipulators

Efficient implementation of a neural network-based strategy for the online adaptive control of complex dynamical systems characterized by an interconnection of several subsystems (possibly nonlinear) centers on the rapidity of the convergence of the training scheme used for learning the system dynamics. For illustration, in order to achieve a satisfactory control of a multijointed robotic manipulator during the execution of high speed trajectory tracking tasks, the highly nonlinear and coupled dynamics together with the variations in the parameters necessitate a fast updating of the control actions. For facilitating this requirement, a multilayer neural network structure that includes dynamical nodes in the hidden layer is proposed, and a supervised learning scheme that employs a simple distributed updating rule is used for the online identification and decentralized adaptive control. Important characteristic features of the resulting control scheme are discussed and a quantitative evaluation of its performance in the above illustrative example is given.     

Control of robotic manipulators—a neural network approach

A neural-network-based scheme is used for the control of a robotic manipulator. The main idea is that, by using a neural network to learn the characteristics of the robot system (or specifically its inverse dynamics), accurate trajectory following and good performance results are obtained. However, the traditional back-propagation algorithm commonly used for control and identification of nonlinear systems suffers from a slow rate of convergence. We investigate the effect of adusting the slope of the activation function (the node nonlinearity) on the performance of a back-propagation algorithm. It is shown that learning speed is increased significantly by making the slope of non-linearity adaptive. The results demonstrate that the proposed method gives better error minimization and faster convergence. The suggested method is applied to a two-link robotic manipulator. The resulting controller is sufficiently robust with respect to the changing conditions.     

A Neural Net Predictive Control for Telerobots with Time Delay

This paper extends the Smith Predictor feedback control structure to unknown robotic systems in a rigorous fashion. A new recurrent neural net predictive control (RNNPC) strategy is proposed to deal with input and feedback time delays in telerobotic systems. The proposed control structure consists of a local linearized subsystem and a remote predictive controller. In the local linearized subsystem, a recurrent neural network (RNN) with on-line weight tuning algorithm is employed to approximate the dynamics of the time-delay-free nonlinear plant. The remote controller is a modified Smith predictor for the local linearized subsystem which provides prediction and maintains the desirable tracking performance. Stability analysis is given in the sense of Lyapunov. The result is an adaptive compensation scheme for unknown telerobotic systems with time delays, uncertainties, and external disturbances. A simulation of a two-link robotic manipulator is provided to illustrate the effectiveness of the proposed control strategy.     

Adaptive neural control for an uncertain robotic manipulator with joint space constraints

In this paper, adaptive neural tracking control is proposed for a robotic manipulator with uncertainties in both manipulator dynamics and joint actuator dynamics. The manipulator joints are subject to inequality constraints, i.e., the joint angles are required to remain in some compact sets. Integral barrier Lyapunov functionals (iBLFs) are employed to address the joint space constraints directly without performing an additional mapping to the error space. Neural networks (NNs) are utilised to compensate for the unknown robot dynamics and external force. Adapting parameters are developed to estimate the unknown bounds on NN approximations. By the Lyapunov synthesis, the proposed control can guarantee the semi-global uniform ultimate boundedness of the closed-loop system, and the practical tracking of joint reference trajectory is achieved without the violation of predefined joint space constraints. Simulation results are given to validate the effectiveness of the proposed control scheme.     

Analysis and Verification of Repetitive Motion Planning and Feedback Control for Omnidirectional Mobile Manipulator Robotic Systems

Mobile manipulator robotic systems (MMRSs) composed of a manipulator and a mobile platform are investigated in this paper. In order for the mobile manipulator robotic system (MMRS) to return to its initial state when the manipulator’s end-effector is requested to execute cyclical tasks, a quadratic program (QP) based repetitive motion planning and feedback control (RMPFC) scheme is proposed and analyzed. Such an RMPFC scheme can not only mix motion planning and reactive control, but also consider the physical limits of the robotic system. Mathematically, the efficacy of the RMPFC scheme is verified via gradient dynamics analysis. To further demonstrate the effectiveness of the RMPFC scheme, a kinematically redundant MMRS composed of a three degrees-of-freedom (DOF) planar manipulator and an omnidirectional mobile platform is designed, modeled and analyzed. Then, repetitive motion planning and feedback control for the designed omnidirectional MMRS is studied. Besides, a numerical algorithm is developed and presented to solve the QP and resolve the redundancy of the robotic system. Moreover, computer simulations are comparatively performed on such an omnidirectional MMRS, and simulation results substantiate the effectiveness, accuracy and superiority of the proposed RMPFC scheme.     

Robust tracking control of a unicycle-type wheeled mobile manipulator using a hybrid sliding mode fuzzy neural network

This article presents a robust tracking controller for an uncertain mobile manipulator system. A rigid robotic arm is mounted on a wheeled mobile platform whose motion is subject to nonholonomic constraints. The sliding mode control (SMC) method is associated with the fuzzy neural network (FNN) to constitute a robust control scheme to cope with three types of system uncertainties; namely, external disturbances, modelling errors, and strong couplings in between the mobile platform and the onboard arm subsystems. All parameter adjustment rules for the proposed controller are derived from the Lyapunov theory such that the tracking error dynamics and the FNN weighting updates are ensured to be stable with uniform ultimate boundedness (UUB).     

Neural network-based robust finite-time control for robotic manipulators considering actuator dynamics

A novel neural network-based robust finite-time control strategy is proposed for the trajectory tracking of robotic manipulators with structured and unstructured uncertainties, in which the actuator dynamics is fully considered. The controller, which possesses finite-time convergence and strong robustness, consists of two parts, namely a neural network for approximating the nonlinear uncertainty function and a modified variable structure term for eliminating the approximate error and guaranteeing the finite-time convergence. According to the analysis based on the Lyapunov theory and the relative finite-time stability theory, the neural network is asymptotically convergent and the controlled robotic system is finite time stable. The proposed controller is then verified on a two-link robotic manipulator by simulations and experiments, with satisfactory control performance being obtained even in the presence of various uncertainties and external disturbances.     

Neural-network-based two-loop control of robotic manipulators including actuator dynamics in task space

A neural-network-based motion controller in task space is presented in this paper. The proposed controller is addressed as a two-loop cascade control scheme. The outer loop is given by kinematic control in the task space. It provides a joint velocity reference signal to the inner one. The inner loop implements a velocity servo loop at the robot joint level. A radial basis function network （RBFN） is integrated with proportional-integral （PI） control to construct a velocity tracking control scheme for the inner loop. Finally, a prototype technology based control system is designed for a robotic manipulator. The proposed control scheme is applied to the robotic manipulator. Experimental results confirm the validity of the proposed control scheme by comparing it with other control strategies.