Output feedback tracking control of robot manipulators with model uncertainty via adaptive fuzzy logic

Many robot controllers require not only joint position measurements but also joint velocity measurements; however, most robotic systems are only equipped with joint position measurement devices. In this paper, a new output feedback tracking control approach is developed for the robot manipulators with model uncertainty. The approach suggested herein does not require velocity measurements and employs the adaptive fuzzy logic. The adaptive fuzzy logic allows us to approximate uncertain and nonlinear robot dynamics. Only one fuzzy system is used to implement the observer-controller structure of the output feedback robot system. It is shown in a rigorous manner that all the signals in a closed loop composed of a robot, an observer, and a controller are uniformly ultimately bounded. Finally, computer simulation results on three-link robot manipulators are presented to show the results which indicate good position tracking performance and robustness against payload uncertainty and external disturbances.     

Adaptive Output Feedback Control of Flexible-Joint Robots Using Neural Networks: Dynamic Surface Design Approach

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.     

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.     

基于神经网络观测器的船舶轨迹跟踪递归滑模 动态面输出反馈控制

针对三自由度全驱动船舶速度向量不可测问题,考虑船舶模型参数和外部环境扰动均未知的情况,提出一种基于神经网络观测器的船舶轨迹跟踪递归滑模动态面输出反馈控制方法.该方法设计神经网络自适应观测器估计船舶速度向量,且利用神经网络逼近模型参数不确定项,综合考虑船舶位置和速度误差之间关系构造递归滑模面,再采用动态面控制技术设计轨迹跟踪控制律和参数自适应律,并引入低频增益学习方法消除外界扰动导致的高频振荡控制信号.选取李雅普诺夫函数证明了该控制律能够保证轨迹跟踪闭环系统内所有信号的一致最终有界性.最后,基于一艘供给船进行仿真验证,结果表明,船舶轨迹跟踪响应速度快,所设计控制器对系统模型参数摄动及外界扰动具有较强的鲁棒性.     

Neural network payload estimation for adaptive robot control

A concept is proposed for utilizing artificial neural networks to enhance the high-speed tracking accuracy of robotic manipulators. Tracking accuracy is a function of the controller's ability to compensate for disturbances produced by dynamical interactions between the links. A model-based control algorithm uses a nominal model of those dynamical interactions to reduce the disturbances. The problem is how to provide accurate dynamics information to the controller in the presence of payload uncertainty and modeling error. Neural network payload estimation uses a series of artificial neural networks to recognize the payload variation associated with a degradation in tracking performance. The network outputs are combined with a knowledge of nominal dynamics to produce a computationally efficient direct form of adaptive control. The concept is validated through experimentation and analysis on the first three links of a PUMA-560 manipulator. A multilayer perceptron architecture with two hidden layers is used. Integration of the principles of neural network pattern recognition and model-based control produces a tracking algorithm with enhanced robustness to incomplete dynamic information. Tracking efficacy and applicability to robust control algorithms are discussed.     

Adaptive position/force control of robot manipulators withoutvelocity measurements: theory and experimentation

In this paper, we design an adaptive position/force controller for robot manipulators during constrained motion. The proposed controller can compensate for parametric uncertainty while only requiring measurements of link position and end-effector force. A filtering technique is utilized to produce a pseudo-velocity error signal and thus, eliminate the need for link velocity measurements. The control strategy provides semiglobal asymptotic tracking performance for the end-effector position and the interaction force between the constraint and the end-effector. An experimental implementation of the proposed controller on a two-link planar robot is also presented.     

A pure neural network controller for double‐pendulum crane anti‐sway control: Based on Lyapunov stability theory

Crane systems have been widely applied in logistics due to their efficiency of transportation. The parameters of a crane system may vary from each transport, therefore the anti‐sway controller should be designed to be insensitive to the variation of system parameters. In this paper, we focus on pure neural network adaptive tracking controller design issue that does not require the parameters of crane systems, i.e. the trolley mass, the payload mass, the cable lengths, and etc. The proposed neural network controller only requires the output feedback signals of the trolley, i.e. the position and the velocity, which means no sway measuring equipment is needed. The Lyapunov method is utilized to design the weights update law of neural network, and the robustness of the proposed controller is proved by the Lyapunov stability theory. The results of numerical simulations show that the proposed neural network controller has excellent performance of trolley position tracking and payload anti‐sway controlling.     

机器人自适应PID饱和输出反馈控制

针对机器人系统在仅有位置传感、驱动器饱和、存在建模不确定性及干扰等条件下的轨迹跟踪控制问题,提出了一种新的自适应PID控制方案。采用高精度滤波器估计机器人关节速度,采用带饱和函数的控制器限制输出力矩,采用自适应PID控制器补偿建模不确定性和干扰。通过Lyapunov直接法,证明系统的稳定性。最后以两关节机器人为例,给出仿真实验结果,验证了算法的有效性。     

Robust adaptive control of a robotic manipulator including motor dynamics

An adaptive nonlinear control law that incorporates the manipulator dynamics as well as dynamics of the actuator is developed in this article. The proposed adaptive robust tracking controller requires position measurements only. The controller consists of two parts: a linear observer that generates an estimated error from the error on the joint position, together with a linear feedback controller that utilizes the estimated states. The second part is an adaptive controller that utilizes the feedback states from the linear observer to generate a control effort that takes into consideration the dynamic parameters variation of the robot and actuator. The closed loop system is locally stable in the Lyapunov sense. © 1998 John Wiley & Sons, Inc.     

An adaptive partial state-feedback controller for RLED robotmanipulators

An adaptive partial state-feedback controller is designed for rigid-link electrically driven (RLED) robot manipulators. The controller is based on structural knowledge of the electromechanical dynamics of the RLED robot and measurements of link position and electrical winding current in each of the brushed DC link actuators. The proposed controller is designed to adapt for parametric uncertainty in the electromechanical dynamics while utilizing a dynamic filter to generate link velocity tracking error information. The controller, adaptation laws, and the pseudovelocity filter are designed via a Lyapunov-like approach, the benefit of which is that at the end of the design procedure the controller can be mathematically shown to produce semiglobal asymptotic link position tracking. The basic design approach can be extended to many types of multiphase motors     

基于自适应神经网络的AUVs路径跟踪控制

为解决由于随时间变化水动力阻尼引起的参数变化和不确定性的问题,提出了基于径向基函数神经网络的未知评估算法,引入自适应算法以保证神经网络权值的最优评估.基于Lyapunov稳定性理论,设计一种自适应神经网络控制器以保证路径跟踪系统中所有误差状态都趋于稳定.为了验证该控制器的可行性,对系统施加如位置误差、方向误差等虚拟干扰,证明该控制器可将误差消减为零.另一方面,机器人在以恒定的速度行驶时,每个航点被指定一个适合半径的圆弧可以保证其有较高的精度.为了评估路径跟踪控制器的性能,提出直线型和直线加圆弧型路径方案.仿真结果表明,该控制器可以有效地消除机器人非线性和模型不确定性造成的干扰.     

Adaptive neural controller for space robot system with an attitude controlled base

In this paper, an adaptive neural network-based controller is proposed for a space robot system with an attitude controlled base without joint acceleration measurements and in the presence of parametric uncertainties and external disturbances. Based on the dynamic model, a neural network-based controller is proposed that achieves the required tracking effectively. A feedforward neural network is employed to learn the existing unknown dynamics of robot system. The uniform ultimate boundedness of all signals in the closed-loop system is guaranteed by the Lyapunov approach. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary off learning. Finally, simulation study has been performed to evaluate the controller performance.     

非完整移动机器人的神经网络鲁棒自适应控制

在非完整移动机器人轨迹跟踪问题中,针对机器人运动学与动力学模型的参数和非参数不确定性,提出了一种混合神经网络鲁棒自适应轨迹跟踪控制器,该控制器由运动学控制器和动力学控制器两部分组成;其中,采用了参数自适应的径向基神经网络对运动学模型的未知部分进行了建模,并采用权值在线调整的单层神经网络和自适应鲁棒控制项构成了动力学控制器;基于Lyapunov方法的设计过程保证了系统的稳定性和收敛性,仿真结果证明了算法的有效性。     

神经网络自适应滑模控制的不确定机器人轨迹跟踪控制

提出一种针对机器人跟踪控制的神经网络自适应滑模控制策略。该控制方案将神经网络的非线性映射能力与滑模变结构和自适应控制相结合。对于机器人中不确定项,通过RBF网络分别进行自适应补偿,并通过滑模变结构控制器和自适应控制器消除逼近误差。同时基于Lyapunov理论保证机器手轨迹跟踪误差渐进收敛于零。仿真结果表明了该方法的优越性和有效性。     

A hybrid learning/adaptive partial state feedback controller for RLED robot manipulators

In this paper, we present an adaptive partial state-feedback repetitive learning control algorithm for a rigid-link electrically-driven (RLED) robot manipulator actuated by brushed DC (BDC) motors. The proposed controller is designed to compensate for repeatable mechanical uncertainty via a learning control term while an adaptive control loop is used to compensate for parametric uncertainty in the electrical dynamics. The proposed controller guarantees semi-global asymptotic link position tracking while only requiring measurements of link position and electrical winding current (e.g. measurements of link velocity are not required).     

Nonlinear adaptive control of robotic manipulators - hyperstability approach

A nonlinear model reference adaptive controller based on hyperstability approach, is presented for the control of robot manipulators. Use of hyperstability approach simplifies the stability proof of the adaptive system. The unknown parameters of the system, as well as its variable payload, are estimated on line and are adaptive to their actual values; tending to reduce the system error. In addition, any sudden change in the system parameters or payload is detected by the proposed intelligent controller. Robot path tracking, with unknown parameter values and variable payload, is simulated to show the effectiveness of the proposed adaptive control algorithm. Both system output error and parameter estimation error vanish under the proposed parameter adaptation algorithm.     

基于神经网络的自由漂浮空间机器人关节空间鲁棒跟踪控制

针对存在不确定性以及干扰的自由漂浮空间机器人关节空间轨迹跟踪问题,提出了一种基于鲁棒控制思想的神经网络鲁棒控制方法.对于控制器中由系统惯性参数不确定性引起的非线性不确定项,利用径向基函数(RBF)神经网络进行逼近,并且利用鲁棒控制器使系统镇定并保证从干扰到跟踪误差的增益小于或等于给定的指标.最后,对本文提出的控制方案进...     

基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应控制

针对三自由度全驱动船舶存在模型不确定和未知外部环境扰动的情况,设计出一种基于非线性增益递归滑模的船舶轨迹跟踪动态面自适应神经网络控制方法.该方法综合考虑船舶位置和速度误差之间关系设计递归滑模面,引入神经网络对船舶模型不确定部分进行逼近,设计带σ-修正泄露项的自适应律对神经网络逼近误差与外界环境扰动总和的界进行估计,并应用一种非线性增益函数构造动态面控制律,选取李雅普诺夫函数证明了该控制律能够保证轨迹跟踪闭环系统内所有信号的一致最终有界性.最后,基于一艘供给船进行仿真验证,结果表明,船舶轨迹跟踪响应速度快、精度高,所设计控制器对系统模型参数摄动及外界扰动具有较强的鲁棒性.     

Neural network-based adaptive controller design of roboticmanipulators with an observer

A neural network (NN)-based adaptive controller with an observer is proposed for the trajectory tracking of robotic manipulators with unknown dynamics nonlinearities. It is assumed that the robotic manipulator has only joint angle position measurements. A linear observer is used to estimate the robot joint angle velocity, while NNs are employed to further improve the control performance of the controlled system through approximating the modified robot dynamics function. The adaptive controller for robots with an observer can guarantee the uniform ultimate bounds of the tracking errors and the observer errors as well as the bounds of the NN weights. For performance comparisons, the conventional adaptive algorithm with an observer using linearity in parameters of the robot dynamics is also developed in the same control framework as the NN approach for online approximating unknown nonlinearities of the robot dynamics. Main theoretical results for designing such an observer-based adaptive controller with the NN approach using multilayer NNs with sigmoidal activation functions, as well as with the conventional adaptive approach using linearity in parameters of the robot dynamics are given. The performance comparisons between the NN approach and the conventional adaptation approach with an observer is carried out to show the advantages of the proposed control approaches through simulation studies     

变结构神经网络自适应鲁棒控制

针对一类不确定非线性系统, 提出一种变结构神经网络自适应鲁棒控制(Variable structure neural network adaptive robust control, VSNNARC)方法. 其中变结构神经网络用于在线辨识系统未知非线性函数, 该网络利用节点激活与催眠技术进行动态调节, 减小网络规模与计算量; 自适应鲁棒控制用于网络权值学习与系统建模误差及外部扰动补偿. 采用Lyapunov稳定性分析法, 给出网络权值自适应律的形式以及鲁棒控制项的设计方法. 该方法不仅能保证系统的稳定性, 也能保证系统具有很好的瞬态性能. 将该方法应用到转台伺服系统的位置跟踪控制中, 实际运行结果表明, 该方法使系统具有很强的鲁棒性及良好的跟踪效果.