模型不确定的下肢康复机器人轨迹跟踪自适应控制

为了实现康复训练过程中高精度的轨迹跟踪控制,针对下肢康复机器人的模型参数和外界干扰等不确定性因素对其轨迹跟踪造成严重影响,提出一种模型不确定的下肢康复机器人轨迹跟踪自适应控制方法。根据所提方案,设计了相应的轨迹跟踪自适应控制器;并进行了轨迹跟踪控制仿真实验对比分析,结果表明,计算力矩控制方法在系统模型不确定时,膝关节的最大角度跟踪误差高达11.3°,髋关节最大稳态误差4.6°;而轨迹跟踪自适应控制方法在模型不确定的情况下,髋关节和膝关节的角度跟踪稳态误差均收敛于零;轨迹跟踪自适应控制方法可以显著提高下肢康复机器人轨迹跟踪的精度。     

变电站巡检机器人数据驱动无模型自适应控制

针对变电站巡检机器人动力学建模及参数辨识复杂等问题,文中提出了一种基于双闭环的巡检机器人数据驱动无模型自适应控制方法。首先,为了便于理解和仿真需要,对变电站巡检机器人的模型进行了简单的介绍。其次,根据机器人的位姿跟踪误差进行外环的设计,为内环提供虚拟参考输入。然后,根据无模型自适应控制方法进行内环控制器的设计,对机器人的虚拟参考速度进行跟踪。值得注意的是,文中所提方法只用到了输入输出数据,没有用到任何模型信息,因此是完全无模型的。最后,通过MATLAB进行仿真,验证了所提方法的有效性。     

A class of adaptive regulators for robot manipulators

In this paper we propose a framework to adaptive regulator design of robot manipulators. A class of global regulators for robot is characterized for which we provide guidelines to derive adaptive versions. The class of regulators is described by control laws comprising the gradient of an artificial potential energy depending in a linear manner on the robot and payload unknown parameters plus a linear velocity feedback. We provide explicit sufficient conditions on the artificial potential energy which allow to obtain an adaptive regulator which yields a stable closed-loop system and global positioning. Using this framework we revise two previously proposed adaptive regulators and we suggest two new ones. © 1998 John Wiley & Sons, Ltd.     

基于改进自适应遗传算法的配电网络重构

提出了一种用于配电系统网络重构的改进型自适应遗传算法。给出了网络重构问题的数学模型及改进的自适应遗传算法。在应用遗传算法时结合配电网自身的特点,提出以环路开关号为基因、系统环路数为染色体长度的编码方法,在优化过程中采用自适应调整的交叉率和变异率,结合一定的禁忌规则．较好地提高了算法在网络重构方面的效率。在IEEE16节点、33节点、69节点3个不同规模的算例系统上进行了测试,计算结果表明,所提出的方法缩短了染色体长度,较好地抑制了不可行解的产生．无论是在收敛性、稳定性还是在计算效率上都取得了比较满意的结果。     

计及可靠性含M-V-U的配电网动态重构

大量新能源及新型负荷的接入,传统基于时间断面的配电网静态重构将难以适用于新型配电网络。提出了含电动汽车充电设施微电网(M-V-U)的新型配电网可靠性最优化方法。首先,建立了含电动汽车充电设施的微电网结构模型,并对其运营策略进行分析。选取了系统平均停电频率指标、系统供电不足率指标和系统有功网损指标,并建立各指标的满意度评价函数,使各指标量纲统一。在此基础上,建立了含M-V-U的配电网可靠性最优化模型,并在四种典型场景下对模型进行优化,仿真结果验证了文章所提方法及模型的有效性及正确性。     

A framework for adaptive motion control of autonomous sociable guide robot

We present a framework by which the motion of an autonomous mobile guide robot is adaptively controlled. A sociable robot should adapt its speed and path to suit the users' activities, without restricting the user movement. By generating adaptive artificial potential fields for the users and the subgoal separately, and integrating them with the basic potential fields generated from obstacles, our robot can adapt to the users' activities and provide sociable tour‐guide services. The robot predicts a user's moving speed and adapts to it to maintain the social distance. Moreover, with the proposed framework, users can deviate from the guided path temporarily and return to the original task afterward. Instead of waiting for the users and taking the risk of losing them, the robot deviates from its original path to follow the users and also prepares for returning to the guiding task. The robot restarts the guiding task at that place, which ensures the least cost to reach the goal. Simulation and experimental results show that our framework can automatically generate suitable motion patterns to control the robot adaptively, making it sociable while providing tour guide services. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

感应电机的神经网络鲁棒自适应动态面控制

针对感应电机的高阶、强耦合的非线性,设计一种具有自适应性和鲁棒性的动态面控制律,以实现对感应电机的控制。首先通过变量代换,将五阶感应电机模型变为块三角形式;接着采用神经网络逼近,将模型转化为含有结构不确定性和参数不确定性的参数化模型。对于感应电机系统中的参数不确定性,在动态面控制方法的基础上设计参数自适应律,在线估计其未知参数;对于感应电机系统中的结构不确定性,设计鲁棒控制律,使得电机控制系统具有鲁棒性。最后通过仿真实现,以验证所提出的控制方法的有效性与实用性。     

Indirect model reference adaptive control with dynamic adjustment of parameters

The paper discusses in detail a new method for indirect model reference adaptive control (MRAC) of linear time-invariant continuous-time plants with unknown parameters. The method involves not only dynamic adjustment of plant parameter estimates but also those of the controller parameters. Hence the overall system can be described by a set of non-linear differential equations as in the case of direct control. Many of the difficulties encountered in the conventional indirect approach, where an algebraic equation is solved to determine the control parameters, are consequently bypassed in this method. The proof of stability of the equilibrium state of the overall system is found to be different from that used in direct control. Using Lyapunov's theory, it is first shown that the parameter errors between the parameter estimates of the identifier and the true parameters of the plant, as well as those between the actual parameters of the controller and their desired values, are bounded. Following this, using growth rates of signals in the adaptive loop as well as order arguments, it is shown that the error equations are globally uniformly stable and that the tracking (control) error tends to zero asymptotically. This in turn establishes the fact that both direct and indirect model reference adaptive schemes require the same amount of prior information to achieve stable adaptive control.     

A generalized global adaptive tracking control scheme for robot manipulators with bounded inputs

In this work, a generalized adaptive scheme for the global motion control of robot manipulators with constrained inputs is proposed. It gives rise to various families of bounded adaptive controllers defined through a general class of saturation functions. Compared with adaptive tracking control algorithms previously developed in a bounded input context, the proposed adaptive approach guarantees the motion control objective for any initial condition, avoiding discontinuities throughout the scheme, preventing the inputs to reach their natural saturation bounds, and permitting innovation on the saturating structure through its generalized form, giving a wide range of possibilities for performance improvement. Experimental results corroborate the efficiency of the proposed scheme. Copyright © 2014 John Wiley & Sons, Ltd.     

自适应遗传算法在变压器局部放电超高频模式识别中的应用

采用自适应遗传算法(AGA)作为神经网络的学习算法，对实验室中变压器局部放电超高频自动识别系统检测到的5种放电类型进行了模式识别。实验结果表明，AGA神经网络解决了BP神经网络对初始权值敏感、收敛速度慢和容易局部收敛的问题，具有较高的识别率和较强的推广能力，可以很好地应用于变压器局部放电的超高频模式识别中。     

自适应遗传算法在变压器局部放电高频模式识别的应用

采用自适应遗传算法(AGA)作为神经网络的学习算法,对实验室中变压器局部放电超高频自动识别系统检测到的5种放电类型进行了模式识别.实验结果表明,AGA神经网络解决了BP神经网络对初始权值敏感、收敛速度慢和容易局部收敛的问题,具有较高的识别率和较强的推广能力,可以很好地应用于变压器局部放电的超高频模式识别中.     

HGA-AWNN模型在明胶浓度软测量中的应用

有效的测量明胶浓度是研究明胶生产工艺中一项非常重要的课题,如何准确、快速地测量出明胶的浓度是一项重要的任务。目前明胶浓度的检测手段多为离线人工检测,不能实现胶液浓度的在线实时测量。在研究自适应小波神经网络(AWNN)的基础上,从提高明胶浓度软测量模型的实时性和鲁棒性着手,采用混合递阶遗传算法(HGA)对模型的参数进行优化。仿真结果得到的训练精度和预测精度分别为0.45和0.31,满足精度要求,算法在实现明胶浓度的在线测量上具有一定的实用性。     

机器人的神经网络鲁棒轨迹跟踪控制

考虑了一类具有外界扰动和参数不确定性机器人系统的轨迹跟踪鲁棒控制问题。提出了两种控制方法：第一种应用输入输出线性化方法以及Lyapunov函数法,推导出鲁棒输出跟踪控制器。所获得的控制器可确保系统输出按指数规律跟踪期望输出,同时相应闭环系统的状态一致最终有界。第二种方法在第一种控制方法的基础上,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,有效的克服了系统不确定性的影响,提高了控制精度。     

Functional adaptive controller for multivariable stochastic systems with dynamic structure of neural network

The article deals with a challenging problem of adaptive control design for multivariable stochastic systems with a functional uncertainty. Model of the system is based on multi‐layered perceptron neural networks where both the unknown parameters and the structure are found in real time without a necessity of any off‐line training process. The unknown parameters are estimated by a global estimation method, the Gaussian sum filter, and the structure of the neural network model is optimized by a proposed pruning method. The control law is based on a bicriterial approach to the suboptimal dual control. Two individual criteria are designed and used to introduce conflicting efforts between the estimation and control; probing and caution. A comparison of the proposed dual control and its alternative with an implementation of the pruning algorithm is shown in a numerical example. Copyright © 2011 John Wiley & Sons, Ltd.     

基于Hopfield神经网络的直流传动系统模型参考自适应控制

本文将Hopfield神经网络引入直流传动系统的模型参考自适应控制,通过神经网络模型参考自适应控制器来给出常规双闭环调速系统的速度控制器外环动态输出,使速度控制具有对某些参数变化的一定程度的鲁棒性     

动态输电网络规划的组合编码遗传算法

针对动态输电网络规划过程中需要考虑时间决策量的问题,提出了组合编码方式。组合编码方式将多阶段输电网络规划中的时间决策量隐含在编码中,从而使得多阶段的动态输电网络规划问题能够转换成静态规划问题进行求解。该编码方式满足表现型和基因型的1对1映射,及表现型空间与基因型空间距离上的一致性,从而保证了遗传算法的搜索效率。此外,针对动态输电网络规划的特点,对遗传算法的交叉算子、变异方式、适应度函数、惩罚系数等方面进行了改进,进一步改善了遗传算法求解多阶段输电网络规划问题的性能。以19节点系统为例对算法进行了验证,结果表明能够在较短的进化代数内得到问题的最优解。     

基于自适应变异蝙蝠优化BP神经网络的短期风电功率预测

风电规模化并网技术的大力发展,进一步增大了对电力系统规划与运行的影响.现今,风电机组出力面临着波动的随机性以及不确定性的技术性问题,为了提高短期风电功率预测的精度,文中提出了一种结合基于群体适应度方差自适应变异的蝙蝠优化算法(AMBA)与BP神经网络算法,就短期风电功率进行精准预测.该模型根据群体适应度方差以及当前最优...     

参数在线跟踪的直流传动双神经网络模型参考自适应控制

针对在“基于Hopfield神经网络的直流传动系统模型参考自适应控制“^[8]一文中指出的所提神经网络控制器在负载转矩未知和变化情况下的缺陷,将作者先前所提出的交流传动系统参数自动跟踪神经网络^[2]引入直流传动系统,构成具有参数在线跟踪功能的直流传动双神经网络模型参考自适应控制模式,进一步提高了系统的控制性能。     

一种新型遗传算法与人工神经网络相结合的模型在电力系统短期负荷预测中的应用研究

利用神经网络和遗传算法的优点,建立了一种新型的神经网络和遗传算法相结合的模型来进行电力系统的短期负荷预测。在对负荷变化规律分析的基础上提出了按日期类型分开建模的24小时预测模型,并对天气因素进行了有效处理。建立了具有灵活友好的用户界面和完善功能的负荷预测软件。实例表明人工神经网络和遗传算法相结合的模型在实际应用中有令人满意的预测效果。     

基于AGA优化RBF神经网络的矿井通风机故障诊断

针对径向基神经网络(RBF)用于故障诊断时存在收敛速度慢、诊断结果准确率低等问题,提出了一种基于自适应遗传算法(AGA)优化RBF神经网络的矿井通风机故障诊断方法.采用AGA对RBF神经网络的隐含层节点数、隐层基函数的中心和宽度进行优化,以此提高RBF网络的泛化能力.通过大量收集和整理工作形成样本集,使用训练样本训练RBF网络,根据网络输出结果对通风机故障进行诊断.仿真结果表明,相较于RBF神经网络,AGA优化的RBF神经网络收敛速度更快,迭代次数更少,能够有效识别通风机故障类型,诊断结果准确率更高.