Adaptive Robust Control with Leakage-Type Control Law for Trajectory Tracking of Exoskeleton Robots

This paper investigates the trajectory following problem of exoskeleton robots with numerous constraints. However, as a typical nonlinear system with variability and parameter uncertainty, it is difficult to accurately achieve the trajectory tracking control for exoskeletons. In this paper, we present a robust control of trajectory tracking control based on servo constraints. Firstly, we consider the uncertainties (e.g., modelling errors, initial condition deviations, structural vibrations, and other unknown external disturbances) in the exoskeleton system, which are time-varying and bounded. Secondly, we establish the dynamic model and formulate a close-loop connection between the dynamic model and the real world. Then, the trajectory tracking issue is regarded as a servo constraint problem, and an adaptive robust control with leakage-type adaptive law is proposed with the guaranteed Lyapunov stability. Finally, we conduct numerical simulations to verify the performance of the proposed controller.

Adaptive Robust Control for a Lower Limbs Rehabilitation Robot Running Under Passive Training Mode

This paper focuses on the problem of the adaptive robust control of a lower limbs rehabilitation robot(LLRR) that is a nonlinear system running under passive training mode. In reality, uncertainties including modeling error, initial condition deviation, friction force and other unknown external disturbances always exist in a LLRR system. So, it is necessary to consider the uncertainties in the unilateral man-machine dynamical model of the LLRR we described. In the dynamical model, uncertainties are(possibly fast) time-varying and bounded. However, the bounds are unknown. Based on the dynamical model, we design an adaptive robust control with an adaptive law that is leakagetype based and on the framework of Udwadia-Kalaba theory to compensate for the uncertainties and to realize tracking control of the LLRR. Furthermore, the effectiveness of designed control is shown with numerical simulations.

A New Noise-Tolerant Dual-Neural-Network Scheme for Robust Kinematic Control of Robotic Arms With Unknown Models

Taking advantage of their inherent dexterity,robotic arms are competent in completing many tasks efficiently.As a result of the modeling complexity and kinematic uncertainty of robotic arms,model-free control paradigm has been proposed and investigated extensively.However,robust model-free control of robotic arms in the presence of noise interference remains a problem worth studying.In this paper,we first propose a new kind of zeroing neural network(ZNN),i.e.,integration-enhanced noise-tolerant ZNN(IENT-ZNN)with integration-enhanced noisetolerant capability.Then,a unified dual IENT-ZNN scheme based on the proposed IENT-ZNN is presented for the kinematic control problem of both rigid-link and continuum robotic arms,which improves the performance of robotic arms with the disturbance of noise,without knowing the structural parameters of the robotic arms.The finite-time convergence and robustness of the proposed control scheme are proven by theoretical analysis.Finally,simulation studies and experimental demonstrations verify that the proposed control scheme is feasible in the kinematic control of different robotic arms and can achieve better results in terms of accuracy and robustness.

Adaptive fuzzy compensation based composite nonlinear feedback controller design for robot manipulators

In order to suppress the influence of uncertain factors on robot system and enable an uncertain robot system to track the reference input accurately,a strategy of combining composite nonlinear feedback(CNF)control and adaptive fuzzy control is studied,and a robot CNF controller based on adaptive fuzzy compensation is proposed.The key of this strategy is to use adaptive fuzzy control to approach the uncertainty of the system online,as the compensation term of the CNF controller,and make full use of the advantages of the two control methods to reduce the influence of uncertain factors on the performance of the system.The convergence of the closed-loop system is proved by feedback linearization and Lyapunov theory.The final simulation results confirm the effectiveness of this plan.

Robust Control of Robotic Manipulators in the Task-Space Using an Adaptive Observer Based on Chebyshev Polynomials

In this paper,an adaptive observer for robust control of robotic manipulators is proposed.The lumped uncertainty is estimated using Chebyshev polynomials.Usually,the uncertainty upper bound is required in designing observer-controller structures.However,obtaining this bound is a challenging task.To solve this problem,many uncertainty estimation techniques have been proposed in the literature based on neuro-fuzzy systems.As an alternative,in this paper,Chebyshev polynomials have been applied to uncertainty estimation due to their simpler structure and less computational load.Based on strictly-positive-rea Lyapunov theory,the stability of the closed-loop system can be verified.The Chebyshev coefficients are tuned based on the adaptation rules obtained in the stability analysis.Also,to compensate the truncation error of the Chebyshev polynomials,a continuous robust control term is designed while in previous related works,usually a discontinuous term is used.An SCARA manipulator actuated by permanent magnet DC motors is used for computer simulations.Simulation results reveal the superiority of the designed method.

Modeling and Robust Discrete LQ Repetitive Control of Electrically Driven Robots

Discrete linear quadratic control has been efciently applied to linear systems as an optimal control.However,a robotic system is highly nonlinear,heavily coupled and uncertain.To overcome the problem,the robotic system can be modeled as a linear discrete-time time-varying system in performing repetitive tasks.This modeling motivates us to develop an optimal repetitive control.The contribution of this paper is twofold.For the frst time,it presents discrete linear quadratic repetitive control for electrically driven robots using the mentioned model.The proposed control approach is based on the voltage control strategy.Second,uncertainty is efectively compensated by employing a robust time-delay controller.The uncertainty can include parametric uncertainty,unmodeled dynamics and external disturbances.To highlight its ability in overcoming the uncertainty,the dynamic equation of an articulated robot is introduced and used for the simulation,modeling and control purposes.Stability analysis verifes the proposed control approach and simulation results show its efectiveness.

A Robust Tracking Controller for Electrically Driven Robot Manipulators: Stability Analysis and Experiment

In this paper, a robust controller for electrically driven robotic systems is developed. The controller is designed in a backstepping manner. The main features of the controller are: 1) Control strategy is developed at the voltage level and can deal with both mechanical and electrical uncertainties. 2) The proposed control law removes the restriction of previous robust methods on the upper bound of system uncertainties. 3) It also benefits from global asymptotic stability in the Lyapunov sense. It is worth to mention that the proposed controller can be utilized for constrained and nonconstrained robotic systems. The effectiveness of the proposed controller is verified by simulations for a two link robot manipulator and a four-bar linkage. In addition to simulation results,experimental results on a two link serial manipulator are included to demonstrate the performance of the proposed controller in tracking a given trajectory.

Application of μ Theory in Compliant Force Control

The application of μ theory in compliant force control system is studied. A compliant force control strategy is developed based on the inner loop position control of 6-DOF parallel robot in order to simulate the push and pull process of forcible alignment in space docking, Considering uncertainties such as parameter perturbations, model perturbations and external disturbances, etc., a robust force controller is designed using μ synthesis theory. The robust stability and robust performance are compared by analysis between the designed robust force controller and the classical force controller. The experiment results of the designed robust force controller and the classical force controller are shown. The results indicate that the designed robust force controller is of efficiency and superiority.

Reduced-Order GPIO Based Dynamic Event-Triggered Tracking Control of a Networked One-DOF Link Manipulator Without Velocity Measurement

In networked robot manipulators that deeply integrate control, communication and computation, the controller design needs to take into consideration the limited or costly system resources and the presence of disturbances/uncertainties. To cope with these requirements, this paper proposes a novel dynamic event-triggered robust tracking control method for a onedegree of freedom(DOF) link manipulator with external disturbance and system uncertainties via a reduced-order generalized proportional-integral observer(GPIO). By only using the sampled-data position signal, a new sampled-data robust output feedback tracking controller is proposed based on a reduced-order GPIO to attenuate the undesirable influence of the external disturbance and the system uncertainties. To save the communication resources, we propose a discrete-time dynamic event-triggering mechanism(DETM), where the estimates and the control signal are transmitted and computed only when the proposed discrete-time DETM is violated. It is shown that with the proposed control method, both tracking control properties and communication properties can be significantly improved. Finally, simulation results are shown to demonstrate the feasibility and efficacy of the proposed control approach.

基于自适应神经网络的工业机器人双臂协同鲁棒控制

为了克服机械摩擦、外界干扰和模型误差等不确定性对工业机器人双臂运动轨迹控制精度的影响,设计了一种基于自适应神经网络的工业机器人双臂协同鲁棒控制方法。首先,建立了带有各类不确定性的工业机器人双臂动力学模型;然后,通过构造障碍Lyapunov函数设计了带有不确定性的协同控制律,并设计了自适应神经网络对系统中的不确定性进行估计,从而得到工业机器人双臂协同鲁棒控制律;最后,利用Lyapunov稳定性理论证明了设计的协同鲁棒控制律能够将工业机器人双臂的轨迹跟踪误差、速度跟踪误差和不确定性估计误差约束在一个任意小的邻域内。仿真结果表明,设计的自适应神经网络可准确估计出工业机器人双臂系统中的不确定性,最大估计误差仅为0.04 N·m,提出的协同鲁棒控制律能够稳定、准确地跟踪轨迹控制指令,最大轨迹跟踪误差仅为1.3 mm,从而验证了设计方法的合理性。在三维空间固定坐标定位测试中,提出的协同鲁棒控制律与其他几种方法相比具有更高的控制精度,平均定位误差和最大定位误差分别仅为1.1 mm和1.4 mm,表现出了更强的鲁棒性和更优的工程适用性。

基于辅助变量的终端阻抗滑模六足机器人足端控制

针对复杂环境下六足机器人足端力/位跟踪控制问题,提出一种基于辅助变量的终端阻抗滑模六足机器人足端控制方法来实现复杂环境下六足机器人的稳定运动。首先,该方法引入广义阻抗模型实现复杂环境下六足机器人足端位置和力的动态调节;其次,引入终端滑模控制方法提高系统的鲁棒性以及足端力/位跟踪误差收敛性能;然后,设计动态补偿器以及新型的辅助变量,构建桥梁,从而建立了结合终端滑模控制以及广义阻抗控制的复合控制框架;之后,通过Lyapunov理论证明了控制器的稳定性;最后,在六足机器人三自由度机械腿模型上与滑膜阻抗控制方法(SMIC)进行了对比仿真验证,仿真结果证明了与结合线性滑模面的滑模阻抗控制方法相比,所提控制方法具有更好的足端力/位跟踪精度以及更快的跟踪误差收敛速度。

不确定性机器人的鲁棒控制

本文研究不确定性刚性机器人的鲁棒控制问题。提出了一种新的鲁棒控制方案。控制器由两部分组成：第一部分为基于标称模型设计的计算力矩控制器；第二部分为基于Ｌｙａｐｕｎｏｖ方法设计的鲁棒补偿控制器，其作用是消除不确定性对跟踪性能的影响．本文证明了闭环系统的全局收敛性，仿真结果表明本方法对于存在外扰和模型不确定性的机器人系统是十分有效的。

机器人鲁棒控制研究进展

本文综述了近年来机器人鲁棒控制方法的发展情况，介绍并比较了各种机器人鲁棒控制方法的优缺点。

基于神经网络的鲁棒自适应控制

考虑摩擦及外界干扰的情况下 ,针对具有不确定性参数的机器人系统 ,提出一种基于神经网络动态补偿的鲁棒自适应控制策略 ,采用神经网络在线补偿控制器以克服系统的外部扰动、未建模动力学部分等非参数不定性带来的影响 ,从而提高了系统的动态性能和稳态精度 ,并对闭环系统稳定性进行了证明。仿真结果表明 ,所提方法具有良好的跟踪性能和较强的鲁棒性。

高阶滑模控制在非完整移动机器人鲁棒输出跟踪中的应用

给出了移动机器人鲁棒输出跟踪的高阶滑模控制器 ,它不仅可以削弱滑模控制系统的抖振问题 ,还对系统存在的不确定性具有良好的鲁棒性 .

改进自适应微分进化算法求解全局优化问题

针对微分进化(differential evolution,DE)算法在进化后期收敛速度慢、收敛精度低、易陷入局部最优解等缺点。通过改进DE的变异方程,并引入一种新的控制参数自适应策略,提出了一种改进自适应微分进化(improved adaptive differential evolution,IADE)算法。进化过程中IADE将根据个体适应值与父代平均适应值之间的关系动态地调整控制参数。同时,采用10个常用于优化算法比较的标准函数对IADE和其他改进DE算法进行对比实验。实验结果表明,IADE算法不仅能够显著地提高收敛速度和收敛精度,而且具有非常好的鲁棒性,从而使得该算法能够满足过程优化的实时性、准确性以及稳定性要求。

3-RPS并联机器人动力学分析及控制

运用牛顿—欧拉方法建立3-RPS并联机器人机构的逆动力学模型。通过使用Simmechanics将并联机器人物理模型导入Matlab/Simulink中,利用Simulink对机构进行动力学仿真以及验证机构的逆动力学模型。针对并联机器人的建模不确定性,提出一种基于不确定性系统的鲁棒控制方案,即分别通过基于标称模型设计系统的计算力矩控制器,来镇定标称系统;通过构建Lyapunov函数来构建系统的鲁棒补偿控制器,来消除由于建模不确定性引起的跟踪误差。通过将控制模型导入Simulink中对其控制效果进行验证,其具有较低的稳态误差精度,效果优于计算力矩控制策略。

机器人轨迹跟踪的一种自适应神经鲁棒控制

针对不确定机器人轨迹跟踪问题 ,提出了一种基于神经网络的自适应鲁棒控制 .该控制方案由一个PD反馈和一个神经动态补偿器组成 ,其特点是不需要系统不确定性上界的先验知识 ,而且避免了求解惯性矩阵逆 .通过利用一个RBF神经网络自适应学习系统不确定性的未知上界 ,从而可以有效克服系统不确定性的影响 ,保证机器人系统的输出跟踪误差渐近收敛于 0 .

输入力矩受限的机器人鲁棒自适应跟踪控制

在输入力矩受限的情况下,提出一种全新的简单鲁棒自适应跟踪控制算法,当参数的估计范围包含其真实值时,证明了闭环系统的渐近稳定跟踪;当有干扰存在,常规参数估计自适应控制算法不能实现稳定控制时,本算法仍然使系统稳定,在本算法中,所估计的参数在跟踪控制律前馈项中表现为非线性,这是区别于常规参数估计自适应算法的一个最重要特征。因此本算法控制器的设计更有灵活性,另一方面获得更好的控制品质和鲁棒性,特别是对参数域估计误差即参数范围估计错误的强鲁棒性,均为仿真算例所验证。