Stable Adaptive Fuzzy Control with TSK Fuzzy Friction Estimation for Linear Drive Systems

This paper considers the control of a linear drive system with friction and disturbance compensation. A stable adaptive controller integrated with fuzzy model-based friction estimation and switching-based disturbance compensation is proposed via Lyapunov stability theory. A TSK fuzzy model with local linear friction models is suggested for real-time estimation of its consequent local parameters. The parameters update law is derived based on linear parameterization. In order to compensate for the effects resulting from estimation error and disturbance, a robust switching law is incorporated in the overall stable adaptive control system. Extensive computer simulation results show that the proposed stable adaptive fuzzy control system has very good performances, and is potential for precision positioning and trajectory tracking control of linear drive systems.     

Robust adaptive tracking control for servo mechanisms with continuous friction compensation

In this paper, a robust adaptive tracking control scheme is developed for servo mechanisms with nonlinear friction dynamics. A continuously differentiable friction model is used to capture the friction behaviors (e.g. Stribeck effect, Coulombic friction and Viscous friction). The robust integral of the sign of the error (RISE) feedback term is employed to design an innovative adaptive controller to compensate nonlinear friction and bounded disturbances. To reduce the effect of noise pollution, the desired trajectory is employed to replace the output signal in controller design. The developed adaptive controller can guarantee the asymptotic tracking performance for nonlinear servo mechanisms in the presence of nonlinear friction and bounded disturbances. Comparative experimental results are used to validate the effectiveness of the developed control algorithm.     

Adaptive integral backstepping sliding mode control for opto-electronic tracking system based on modified LuGre friction model

In order to improve the control accuracy and stability of opto-electronic tracking system fixed on reef or airport under friction and external disturbance conditions, adaptive integral backstepping sliding mode control approach with friction compensation is developed to achieve accurate and stable tracking for fast moving target. The nonlinear observer and slide mode controller based on modified LuGre model with friction compensation can effectively reduce the influence of nonlinear friction and disturbance of this servo system. The stability of the closed-loop system is guaranteed by Lyapunov theory. The steady-state error of the system is eliminated by integral action. The adaptive integral backstepping sliding mode controller and its performance are validated by a nonlinear modified LuGre dynamic model of the opto-electronic tracking system in simulation and practical experiments. The experiment results demonstrate that the proposed controller can effectively realise the accuracy and stability control of opto-electronic tracking system.     

Adaptive control of a nonlinear dc motor drive using recurrent neural networks

A model-following adaptive control structure is proposed for the speed control of a nonlinear motor drive system and the compensation of the nonlinearities. A recurrent artificial neural network is used for the online modeling and control of the nonlinear motor drive system with high static and Coulomb friction. The neural network is first trained off-line to learn the inverse dynamics of the motor drive system using a modified form of the decoupled extended Kalman filter algorithm. It is shown that the recurrent neural network structure combined with the inverse model control approach allows an effective direct adaptive control of the motor drive system. The performance of this method is validated experimentally on a dc motor drive system using a standard personal computer. The results obtained confirm the excellent disturbance rejection and tracking performance properties of the system.     

Neural-network adaptive controller for nonlinear systems and its application in pneumatic servo systems

In this paper, a novel control law is presented, which uses neural-network techniques to approximate the affine class nonlinear system having unknown or uncertain dynamics and noise disturbances. It adopts an adaptive control law to adjust the network parameters online and adds another control component according to H-infinity control theory to attenuate the disturbance. This control law is applied to the position tracking control of pneumatic servo systems. Simulation and experimental results show that the tracking precision and convergence speed is obviously superior to the results by using the basic BP-network controller and self-tuning adaptive controller.     

TRACKING CONTROL IN THE PRESENCE OF NONLINEAR DYNAMIC FRICTIONAL EFFECTS: ROBOT EXTENSION

In this paper, we extend the observer/control strategies previously published in [25] to an n-link, serially connected, direct drive, rigid link, revolute robot operating in the presence of nonlinear friction effects modeled by the Lu-Gre model. In addition, we also present a new adaptive control technique for compensating for the nonlinear parameterizable Stribeck effects. Specifically, an adaptive observer/controller scheme is developed which contains a feedforward approximation of the Stribeck effects. This feedforward approximation is used in a composite controller/observer strategy which forces the average square integral of the position tracking error to an arbitrarily small value. Experimental results are included to illustrate the performance of the proposed controllers.     

Research on nonlinear friction compensation of harmonic drive in gimbal servo-system of DGCMG

The purpose of this paper is to present our results in overcoming the influence of the nonlinear friction afforded by harmonic drive to the gimbal servo-system of double-gimbal control momentum gyro (DGCMG). The existence of compliance and oscillation inherent in harmonic drive systems, and the lack of any technical information on the internal dynamics of the transmission, make the development of friction compensation in harmonic drive system extremely challenging. In this paper, the modeling of nonlinear friction in harmonic drive gear transmission in gimbal servo-system of the DGCMG is proposed. The relationship among the nonlinear friction, the angular velocity and the angular position with an improved Coulomb-Viscous model is derived, and the experiments to identify the various parameters of the improved model are given. At last a feed-forward compensation controller based on the improved model is designed to carry out the friction compensation study.     

基于强化学习神经网络的车速跟踪控制

提出一种用于汽车排放试验中驾驶机器人对车速跟踪控制的新方法.该控制方法基于神经网络并结合强化学习的自适应能力,通过神经网络的在线学习对车速进行跟踪控制.利用试验汽车所获得的数据,首先开发出用于车速控制的神经网络模型.然后基于强化学习神经网络结构设计神经网络控制器以取得车速跟踪的自适应控制.在仿真研究中,使用神经网络车速控制模型替代实际汽车来训练初始控制器,并用开发与训练好的自学习神经网络控制器用于汽车车速跟踪控制.结果表明,所开发的神经网络控制器具有良好的车速跟踪性能,控制效果明显.     

Intelligent position tracking control for LCM drive using stable online self-constructing recurrent neural network controller with bound architecture

In this paper, an intelligent position tracking control (IPTC) is developed for a linear ceramic motor (LCM) drive system. The IPTC system is comprised of a neural controller and a robust controller. The neural controller utilizes a self-constructing recurrent neural network (SCRNN) to mimic an ideal computation controller, and the robust controller is designed to achieve L₂ tracking performance with a desired attenuation level. If the approximation performance of SCRNN is insufficient, SCRNN can create new hidden neurons to increase the learning ability. If the hidden neuron of SCRNN is insignificant, it should be removed to reduce the computation load; otherwise, if the hidden neuron of SCRNN is significant, it should be retained. Moreover, the adaptive laws of controller parameters are derived in the sense of Lyapunov, so system stability can be guaranteed. Finally, the experimental results of the LCM drive system show a perfect tracking response can be achieved using the self-constructing mechanism and the on-line learning algorithm.     

Adaptive Dynamic Surface control of An electrohydraulic Actuator with Friction Compensation

In this paper, an adaptive nonlinear control scheme with a friction observer for position control of an electrohydraulic actuator is proposed. The observer based on the LuGre friction model is employed to compensate for the friction. Adaptation laws are used to handle parameter uncertainties in the actuator and friction model. The control law including dynamics of the observer is developed through a backstepping‐like dynamic surface control (DSC) technique. Experimental results have illustrated the success of the control scheme. The results also show that the adaptive DSC controller has better tracking performance than an adaptive backstepping and conventional PI controllers.     

Adaptive feedback linearizing control of nonholonomic wheeled mobile robots in presence of parametric and nonparametric uncertainties

In this paper, the integrated kinematic and dynamic trajectory tracking control problem of wheeled mobile robots (WMRs) is addressed. An adaptive robust tracking controller for WMRs is proposed to cope with both parametric and nonparametric uncertainties in the robot model. At first, an adaptive nonlinear control law is designed based on input–output feedback linearization technique to get asymptotically exact cancellation of the parametric uncertainty in the WMR parameters. The designed adaptive feedback linearizing controller is modified by two methods to increase the robustness of the controller: (1) a leakage modification is applied to modify the integral action of the adaptation law and (2) the second modification is an adaptive robust controller, which is included to the linear control law in the outer loop of the adaptive feedback linearizing controller. The adaptive robust controller is designed such that it estimates the unknown constants of an upper bounding function of the uncertainty due to friction, disturbances and unmodeled dynamics. Finally, the proposed controller is developed for a type (2, 0) WMR and simulations are carried out to illustrate the robustness and tracking performance of the controller.     

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。     

基于自适应模糊补偿的机械臂复合控制策略

针对刚性机械臂存在摩擦和扰动等不确定因素给轨迹跟踪控制带来的困难,本文基于李亚普诺夫稳定性理论,给出了一种机械臂的自适应控制方案．该方案针对机械臂的标称部分,采用计算力矩的方法设计相应的控制量,在此基础上,构造模糊系统逼近摩擦得到补偿控制量,并针对随机扰动的上界设计反馈控制率,以克服扰动带来的影响,保证系统的稳定性．仿真结果表明,该复合控制对于具有不确定性摩擦以及扰动的机械臂轨迹跟踪问题效果良好．     

一种新的自适应模糊滑模控制器设计方法

对一类非线性系统提出一种新的自适应模糊滑模控制器设计方法。将自适应模糊控制与滑模控制有效地结合在一起,先用滑模控制使跟踪误差进入边界层内,然后启动自适应模糊控制器。该控制器可消除滑模控制器中出现的抖振,并可在存在模糊逻辑系统逼近误差情况下使系统跟踪误差小于预先给定的任意常数。仿真算例验证了所提出方法的有效性。     

Desired Compensation Adaptive Robust Control of Electrical-optical Gyro-stabilized Platform with Continuous Friction Compensation Using Modified LuGre Model

Continuous friction compensation along with other modeling uncertainties is concerned in this paper, to result in a continuous control input, which is more suitable for controller implementation. To accomplish this control task, a novel continuously differentiable nonlinear friction model is synthesized by modifying the traditional piecewise continuous LuGre model, then a desired compensation version of the adaptive robust controller is proposed for precise tracking control of electrical-optical gyro-stabilized platform systems. As a result, the adaptive compensation and the regressor in the proposed controller will depend on the desired trajectory and on-line parameter estimates only. Hence, the effect of measurement noise can be reduced and then high control performance can be expected. Furthermore, the proposed controller theoretically guarantees an asymptotic output tracking performance even in the presence of modeling uncertainties. Extensively comparative experimental results are obtained to verify the effectiveness of the proposed control strategy.

     

考虑摩擦力影响精密伺服系统的鲁棒自适应控制

针对具有摩擦力扰动的精密伺服系统提出了一种鲁棒自适应控制方法.首先,对基于 bristle模型的动态摩擦力模型进行了线性参数化,该线性参数化过程包含了对stribeck效应的 线性参数化处理;然后,基于构造的Lyapunov函数设计全局渐近稳定自适应控制律,并对闭环 系统的跟踪性能进行了严格的理论分析.仿真实验验证了算法的有效性.     

Improving the tracking performance of mechanical systems by adaptive extended friction compensation

The paper discusses a tracking control system and shows with simulation and experimental results that extended friction models can be successfully incorporated in a computed-torque-like adaptive control scheme. The friction model used includes Coulomb, viscous, and periodic friction with sense of direction dependent parameters. To get small tracking errors, adaptation of the friction model parameters is necessary. The tracking performance is an order of magnitude better than with PD control. The robustness of the scheme for parameter inaccuracies is sufficient, owing to the adaptation, but the controller gains are limited due to stability problems caused by unmodeled dynamics.     

含有驱动器模型的移动机器人自适应跟踪控制

本文针对包含驱动器模型的移动机器人, 考虑到其在粗糙表面上运动过程中所受的摩擦力以及不可建模的动态的影响, 使用反步设计法(Backstepping)给出了一种自适应跟踪控制策略.其中对于不可建模的动态, 本文使用一种非线性函数对其影响进行抵消,使得机器人的路径跟踪对不确定具有鲁棒性; 对于摩擦力项, 使用径向基神经网络(RBFNN)对其进行逼近, 在控制器中能够根据逼近值给予相应的摩擦力补偿量, 从而使移动机器人比较适合在粗糙度大的路面(如沙地)上进行路径跟踪. 仿真结果验证了该控制方法的有效性.     

基于神经网络的水下机器人三维航迹跟踪控制

本文研究了水下机器人三维航迹跟踪控制问题.在充分考虑了模型中不确定水动力系数和外界海流干扰的基础上,提出了基于神经网络的自适应输出反馈控制方法.控制器由3部分组成:基于动态补偿器的输出反馈控制项、神经网络自适应控制项和鲁棒控制项.神经网络所需的自适应学习信号由线性观测器提供.基于Lyapunov稳定性理论证明了控制系统的稳定性.最后针对某AUV进行了空间三维航迹跟踪控制仿真实验,结果表明设计的控制器可以较好地克服时变非线性水动力阻尼对系统的影响,并对外界海流干扰有较好的抑制作用,可以实现三维航迹的精确跟踪.     

不确定非线性系统的模糊鲁棒跟踪控制

提出了一种基于T-S模糊型的鲁捧自适应跟踪控制方法.整个控制方案在结合所有 的局部线性状态反馈控制器的基础上,引入了基于自适应神经网络的鲁棒控制器.所提出的 模糊自适应鲁棒控制器设计方法不需要求取李亚普诺夫方程的公共解,不要求系统的不确定 性项满足任何匹配条件或约束条件所提出的带有补偿项的完全自适应RBF神经网络,通过 在线自适应调整RBF神经网络的权重、函数中心和宽度,提高了神经网络的学习能力,可以 有效地对消系统的未知不确定性的影响.同时通过自适应补偿项来在线估计神经网络的近似 误差边界,弥补了神经网络的不足.所提出的方案保证了闭环系统的稳定性,有效地提高了 系统的鲁棒性和跟踪性能.仿真实例表明了所提出方法的有效性.