Iterative learning control for robotic manipulators: A bounded‐error algorithm

This paper presents a model‐based nonlinear iterative learning control (NILC) for nonlinear multiple‐input and multiple‐output mechanical systems of robotic manipulators. An algorithm of a new strategy for the NILC implementation is proposed. This algorithm ensures that trajectory‐tracking errors of the proposed NILC, when implemented, are bounded by a given error norm bound. Both standard and bounded‐error learning control laws with feedback controllers attached are considered. The NILC synthesis is based on a dynamic model of a six degrees of freedom robotic manipulator. The dynamic model includes viscous and Coulomb friction and input generalized torques are bounded. With respect to the bounded‐error and standard learning processes applied to a virtual PUMA 560 robot (Unimation, Inc. Danburry, CT, USA), simulation results are presented in order to verify maximal tracking errors, convergence and applicability of the proposed learning control. Copyright © 2013 John Wiley & Sons, Ltd.     

A new decentralized model reference adaptive control for a class of interconnected dynamic systems using variable structure design

This paper presents a new decentralized model reference adaptive control for a class of large-scale interconnected dynamic systems. Interconnections among subsystems may be time-invariant or time-varying and linear or non-linear. The scheme proposed here only takes input and output measurements from each subsystem for input synthesis. Using a variable structure design concept, we show that the tracking errors will converge to zero in finite time despite the interconnections with any possible strengths.     

基于迭代学习与FIR滤波器的PMLSM高精密控制

针对永磁直线同步电机(PMLSM)运行时易受端部效应、摩擦力、负载扰动、参数变化等不确定性因素的影响而难以达到高精度跟踪控制的问题,提出一种基于迭代学习与有限冲击响应(FIR)滤波器的控制方案。PMLSM伺服系统执行重复任务时,迭代学习控制(ILC)可有效地抑制重复性扰动,具有很高的控制精度,但执行非重复性任务时很难获得较高的控制精度。为了进一步改善基于ILC的PMLSM伺服系统运行迭代1次的跟踪精度,利用ILC的输出信息来设计FIR滤波器,进而用FIR滤波器来代替ILC,使控制系统达到最优的ILC,以提高系统的跟踪精度。采用滑模控制(SMC)对FIR滤波器进行补充,使位置误差快速收敛到一定的界限内,以提高系统的抗扰能力。实验结果表明,所提出的控制方案使系统具有很高的位置跟踪精度和很强的鲁棒性。     

Prescribed performance bound‐based adaptive path‐following control of uncertain nonholonomic mobile robots

In this paper, we consider the transient performance of path‐following control of nonholonomic mobile robots with parametric uncertainties. By incorporating prescribed performance bound (PPB) technique, the transient performances on position and orientational tracking errors will be guaranteed with convergence rates no less than certain pre‐specified values and sufficiently small maximum overshoots. We also extend the proposed scheme to solve the formation control problem for a group of N unicycle‐type mobile robots without inter‐robot communications. It is ensured that all the robots can track their references with arbitrarily small errors and no collision will occur between any two robots. Simulation studies verify the established theoretical results. Copyright © 2016 John Wiley & Sons, Ltd.     

多关节机器人的反演自适应模糊滑模控制

针对二关节机器人轨迹跟踪问题,设计了一种新的反演自适应模糊滑模控制器.该方法设计了反演滑模控制器和自适应模糊控制器,通过设计合适的自适应律,采用模糊控制器在线估计不确定性上界值,实现了对建模误差和干扰的自动跟踪,削弱了抖振.利用李亚普诺夫定理证明了系统的稳定性.仿真结果表明该方法的有效性.     

迭代学习控制在GTAW焊接过程中的应用研究

针对相同工件的批量焊接,并且焊接轨迹相同的情况下,焊接过程具有极高的重复性。提出了基于迭代学习控制(ILC)的脉冲气体钨极氩弧焊(GTAW)焊接过程跟踪控制方法。根据GTAW焊接的动态过程模型,设计了GTAW焊接过程控制的ILC算法,并对算法的收敛性进行了证明。研究结果表明,ILC可以有效地利用焊接过程中的重复信息,经过60次迭代学习后,焊接系统输出可以较好的达到期望轨迹,并获得较高的控制精度,验证了方法的有效性。与PID控制相比ILC控制器不但可以获得较好的跟踪效果,而且还能有效抑制外部扰动的作用,具有较强的鲁棒性。     

一种非线性连续预测变结构控制方案

提出了一种非线性连续预测变结构控制方案,将非线性连续预测控制与滑模变结构控制结合起来。其控制器由针对对象标称非线性模型设计的连续预测控制器和针对对象不确定性设计的滑模变结构控制器组成。所提出的方案既具有变结构控制鲁棒性强的优点,又由于连续模型预测控制的引入使得变结构控制的“抖振”大大减小。应用于二自由度机械手轨迹跟踪控制,仿真结果表明了所提控制方案的有效性。     

输入受限下非完整轮式移动机器人迭代学习跟踪控制

为了解决输入受限下非完整轮式移动机器人的跟踪控制问题,考虑迭代学习控制方法,设计了一种迭代学习控制律,这里所设计的迭代学习控制律结合了系统的跟踪误差和约束下的上一代控制律.通过应用范数分析理论,对跟踪误差的收敛性进行了理论分析,验证了设计的控制律的有效性.最后,给出了一个仿真实例以证明理论分析结果的正确性,仿真结果表明,在设计的迭代学习控制律作用下,具有输入受限的非完整轮式移动机器人能够获得很好的跟踪控制性能,跟踪误差最终收敛于零的很小邻域内.     

Global adaptive learning control of robotic manipulators by output error feedback

This paper addresses the problem of designing a global, output error feedback based, adaptive learning control for robotic manipulators with revolute joints and uncertain dynamics. The reference signals to be tracked are assumed to be smooth and periodic with known period. By developing in Fourier series expansion the input reference signals of every joint, an adaptive, output error feedback, learning control is designed, which ‘learns’ the input reference signals by identifying their Fourier coefficients: global asymptotic and local exponential stability of the tracking error dynamics are obtained when the Fourier series expansion of each input reference signal is finite, while arbitrary small tracking errors are achieved otherwise. The resulting control is not model based and depends only on the period of the reference signals and on some constant bounds on the robot dynamics. Copyright © 2008 John Wiley & Sons, Ltd.     

三相PWM整流器的滑模和非线性最优预测控制

结合滑模变结构控制和非线性最优预测控制的优点,提出了一种新型电压电流双环控制方法,该方法电压环采用滑模变结构控制,充分利用滑模变结构控制的鲁棒性强、对负载的扰动具有很好的适应性的优点,生成指令电流;电流环采用非线性最优预测控制,该方法可以在一个到几个周期内消除跟踪误差,具有响应速度快、跟踪精度高等优点,非常适合数字控制系统.仿真和实验的结果证明了所提方法的有效性和优越性.     

Adaptive fuzzy dual-performance fault-tolerant control for interconnected nonlinear systems

This article focuses on the decentralized adaptive fuzzy fixed-time fault-tolerant control issue for the error-constrained interconnected nonlinear systems with unknown actuator faults possessing dead zone. The unknown nonlinear functions can be modeled via fuzzy logic systems. By utilizing the parameter estimation method, the effect of unknown actuator faults possessing dead zone can be compensated. To guarantee the predefined dynamic performance of state tracking errors, the barrier Lyapunov functions and prescribed performance functions are introduced. Then, a dual-performance fault-tolerant control method that can guarantee fast transient performance and predefined performance of state tracking errors is proposed via using the decentralized backstepping technique. In addition, on the basis of the Lyapunov stability theory and the fixed-time criterion, it is proved that the predefined performance of full-state errors and the stability of closed-loop systems can be guaranteed. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed control scheme.     

Optimal control of robot manipulators with a new two-level gradient-based approach

In this paper, a new approach for optimal control of robot manipulators is presented. For this purpose, the system is considered as a two-level large-scale system where a gradient-based coordination strategy is used to coordinate the overall system. This is achieved within a decomposition–coordination framework, where the robot manipulator is first decomposed into several sub-systems at the first level, and then, the coordination is done at the second level. The solution is based on Interaction Prediction Principle, where in the first level, the optimization is done by a gradient method and in the second level, the coordination is done by a new method based on the gradient of interaction errors. This approach provides a new scheme for hierarchical control of robot manipulators with high degree of freedom.The results fairly illustrate the effectiveness of the proposed coordination strategy.     

基于在线学习RBF神经网络的汽门开度自适应补偿控制方法

汽门控制对于提高电力系统暂态稳定具有重要作用。为了提高汽门系统的控制性能,提出了基于在线学习RBF神经网络的汽门开度自适应补偿控制方法。首先,根据逆系统方法分析了被控汽门系统的可逆性、推导了被控汽门系统输出的α阶导数和伪控制量之间的误差,并设计了用于补偿此误差的在线学习RBF神经网络。然后,基于Lyapunov稳定性理论设计了RBF神经网络的在线学习算法,证明了闭环系统跟踪误差和RBF神经网络权值估计误差的一致最终有界性。所提出的控制方法仅需被控汽门系统很少的先验知识,而无需其精确数学模型,并且用于自适应补偿控制的RBF神经网络无需离线训练过程。最后,针对典型的单机无穷大汽门控制系统进行了数值仿真。仿真结果表明,所提出的控制方法较传统的非线性最优控制方法能明显提升电力系统的暂态控制性能。     

基于 actor-critic 框架的在线积分强化学习算法研究

针对轮式移动机器人动力学系统难以实现无模型的最优跟踪控制问题,提出了一种基于actor-critic框架的在线积分强化学习控制算法。首先,构建RBF评价神经网络并基于近似贝尔曼误差设计该网络的权值更新律,以拟合二次型跟踪控制性能指标函数。其次,构建RBF行为神经网络并以最小化性能指标函数为目标设计权值更新律,补偿动力学系统中的未知项。最后,通过Lyapunov理论证明了所提出的积分强化学习控制算法可以使得价值函数,行为神经网络权值误差与评价神经网络权值误差一致最终有界。仿真和实验结果表明,该算法不仅可以实现对恒定速度以及时变速度的跟踪,还可以在嵌入式平台上进行实现。     

A self-tuning controller for mimo non-linear systems

A new self-tuning control (STC) strategy for non-linear, multi-input/multi-output control problems is proposed. This strategy is applicable to a broad class of non-linear systems, which can include any nonlinear functions of the old inputs and outputs as well as the products of these functions and any powers of the most recent inputs. Simulation results for a two-link robot manipulator demonstrate that the new non-linear STC strategy provides significant improvement over conventional STCs based on linear models.     

玻璃基板码垛机械手位置的迭代学习跟踪控制

码垛机械手在玻璃基板生产过程中发挥着至关重要的作用。针对玻璃基板码垛机械手码垛过程具有较强重复性的特点,提出迭代学习的码垛机械手位置跟踪控制方法。考虑到运行过程中出现的非重复性干扰设计了鲁棒迭代学习控制器。同时采用Lyapunov函数分析了码垛机械手的收敛性。理论结果表明,鲁棒迭代学习控制算法可以保证码垛机械手的位置跟踪误差收敛到0。仿真结果表明,所提出的鲁棒迭代学习控制算法,较好地抑制了非重复扰动的影响,有效提高码垛机械手的位置跟踪性能,经过20次的迭代,控制精度能达到0．01mm。在实验测试中,相邻两个玻璃基板间的码垛误差最大不超过0．1mm,取得了良好的控制效果。     

Robust adaptive iterative learning control for discrete‐time nonlinear systems with both parametric and nonparametric uncertainties

A new robust adaptive iterative learning control approach is proposed for discrete‐time nonlinear systems with both parametric and nonparametric uncertainties. By virtue of a well‐designed dead‐zone function, the learning of the parametric and nonparametric uncertainties can be performed concurrently. Rigorous Lyapunov function‐based analysis ensures that the effect of system uncertainties can be fully compensated, and the tracking error will converge to zero asymptotically in the iteration domain, even under random initial conditions and iteration‐varying reference trajectories. The efficacy of the proposed controller is demonstrated by simulating a single‐link robot manipulator with unknown frictions. Copyright © 2015 John Wiley & Sons, Ltd.     

基于迭代学习的矿井提升系统跟踪控制

矿井提升机在煤矿开采和生产过程中发挥着至关重要的作用。针对矿料提升过程具有较强重复性的特点,提出迭代学习的提升机速度和位置跟踪控制方法。设计了提升机的D型迭代学习控制器,同时考虑运行过程中出现的非重复性干扰设计了带有滤波器型迭代学习控制器。并采用λ范数证明了系统的收敛性,理论结果表明矿井提升机位置与速度跟踪误差可以收敛到0。同时仿真结果表明,经过30次运行后,跟踪误差几乎收敛到0,迭代学习控制算法可利用矿井作业的重复运行特性可以有效提高提升机的跟踪性能,带有滤波器的迭代学习控制算法可较好地抑制了非重复扰动的影响。     

永磁直线同步电动机的自适应学习控制

由于没有传动机构,使永磁直线交流同步电机（PMLSM）控制器设计较为复杂.PMLSM对模型不确定性和外扰更加敏感;推力波动等非线性因素对运动精度影响很大.针对上述问题,用自适应学习方法改善PMLSM的轨迹跟踪性能,并对迭代模式和单次运行模式下算法的收敛性进行了证明,通过实验进行了算法验证.该控制方法基于迭代学习,控制器分为两个部分,通过执行重复任务自适应学习项补偿系统的非线性;另一项用于增强系统的鲁棒性,保证系统在单次运动模式下稳定.实验结果表明,这种控制方法可以有效提高PMLSM轨迹跟踪精度.     

Finite-time adaptive control for nonlinear systems with uncertain parameters based on the command filters

The trajectory tracking control problem for a class of nonlinear systems with uncertain parameters is considered in this article. A new adaptive finite-time tracking control is designed based on the adaptive backstepping method via the command filters. The command filter mechanism can avoid the calculation of partial derivatives and solve the “explosion of complexity” in the backstepping design. The compensation signals are introduced to eliminate errors produced by the command filters. The proposed adaptive backstepping control can guarantee the tracking error remains in a small neighborhood of the origin in finite time, while the practical finite-time stability of the control systems with uncertain parameters is proven by the stability criterion. The effectiveness of the proposed scheme is verified by some simulation results.