机器人自适应控制—计算力矩法

基于李雅普诺夫直接法理论本文提出了一种新的机器人操作器自适应控制方法——计算力矩法.这种控制方法既考虑被传统计算力矩法所忽略了的机器人操作器真实参数与估计值之间的误差,又考虑机器人操作器高速运动时系统参数的快速变化.这种控制方法具有自适应性,因而不必事先知道系统参数;在闭环系统中,能保证机器人操作器动力学系统和参考模型之间的误差渐近趋近于零.最后,举出一例验证此法.     

基于计算力矩的微创手术机器人控制

针对微创手术机器人通常采用的独立PD控制或基于重力补偿的PD控制,都必须预先设定好相应的PD常数,不能随刀具所受有效外载荷而进行自适应调节,从而影响了机器人的定位精度的问题.提出了一种基于计算力矩的控制模型,在拉格朗日动力学模型基础上引进控制量使机器人系统线性化,并且将静力学分析结果作为反馈信号,完成外载荷作用下的轨迹...     

机器人计算力矩不确定性的神经网络补偿控制

提出一种由计算力矩控制器和神经网络补偿控制器相结合的控制方案,探讨了用神经网络补偿机器人计算力矩不确定性的方法,推导了网络权值的自适应调整律,并证明了系统的稳定性和误差的收敛性.该方案结构简单、鲁棒性强,且神经网络补偿器有较好的适应性,无须事先知道机器人动力学参数和结构的精确值.对机器人轨迹跟踪的仿真结果表明,所提方案具有很好的鲁棒性和抗干扰能力.     

机器人控制的现状与问题

机器人学是一个多学科交叉的领域,它关联着传感技术,计算机科学,控制理论与方法,以及人工智能,近年来已经发表了大量有关机器人控制的研究成果。本文的目的是对机器人控制的现状作一评述并指出若干有待解决的问题。     

灵长类仿生机器人悬臂运动仿生控制综述

灵长类仿生机器人是通过智能机械手段模仿灵长类运动的一类机器人,针对其悬臂运动仿生的控制研究是该领域的热点.综述了目前灵长类仿生机器人悬臂运动仿生控制的研究方法,给出了悬臂运动仿生控制的一般方法与基于"动态伺服"理论的悬臂运动仿生控制策略,提出了悬臂运动仿生控制中亟待解决的若干问题,并对今后灵长类仿生机器人悬臂运动仿生控...     

柔性机器人的控制

柔性机器人的控制周其节，徐建闽（华南理工大学自动控制系·广州，５１０６４１）一些应用领域要求机器人耗能低、快速和重量小，因而机构要设计得轻巧．如果对机器人运动的性能要求较高，则在设计控制器时必须考虑机械在运动中的弹性变形．近年来柔性机器人的控制问题已...     

基于无线控制的仿生六足步进机器人的设计

本文描绘了一种无线控制的仿生六足步进机器人的控制方法,行进原理与结构。这种机器人两组足交替行走,用于支撑机器人的重心落在一个固定三角形的区域内,使得机器人在行进过程中没有侧翻的可能性。用步进电机控制可以用来实现这种机器人的精确定位,六足所具有的高自由度可以使得机器人的运动非常灵活,可广泛应用于管道、地质勘察、探险、搜救等工作中。     

不确定机器人的鲁棒计算力矩控制

本文研究了存在扰动和参数不确定因素时机器人的轨迹跟踪控制问题.给出了一种鲁棒计算力矩控制方案,它由改进力矩发生器和鲁棒补偿器组成,该方案具有下述特点:①不依赖精确机器人模型;②容忍一定程度的计算误差。这给模型简化、减轻控制计算机运算负担、缩短运算时间提供了一条途径;③保证被控机器人具有渐近跟踪理想轨迹的能力.这些结论通过仿真实验已初步得到证实.     

仿生自然计算研究综述

介绍了仿生自然计算这一新兴交叉学科的含义及研究范围,讨论了仿生计算与其它自然计算分枝的关系。将几种常见种仿生自然计算模型,按照人类社会、生物群体、个体、组织器官、细胞、分子等不同的层次进行了分类综述,并介绍了各种算法的最新研究进展。     

Models of computation for reactive control of autonomous mobile robots

The paper studies computation models for tasks performed by autonomous mobile robots. Such tasks can be accomplished by reactive control algorithms. Reactive control systems can be described using different models of computation which have as distinguishing feature the abstraction level of time. Thus, three computation models are defined: the untimed model, the synchronous model and the timed model. It is shown that the clocked-synchronous model of computation is more appropriate for describing the controller for a parallel parking task.     

Multiagent control of self-reconfigurable robots

We demonstrate how multiagent systems provide useful control techniques for modular self-reconfigurable (metamorphic) robots. Such robots consist of many modules that can move relative to each other, thereby changing the overall shape of the robot to suit different tasks. Multiagent control is particularly well-suited for tasks involving uncertain and changing environments. We illustrate this approach through simulation experiments of Proteo, a metamorphic robot system currently under development.     

Model reference adaptive control algorithms for industrial robots

In this paper some problems concerning the control of multifunctional manipulators (industrial robots) with high speed continuous movements are investigated. Although deterministic approaches to the control of robots, whose model are highly interconnected and non-linear, are known alternative approaches based on the Model Reference Adaptive System (MRAS) method of control are possible and useful. In the paper it is proved that a generalized MRAS control assures the convergence to a suitable reference model for a class of processes: the manipulator is shown to belong to such a class. The paper is completed by some applications evaluated by simulation.     

Decentralized and adaptive control of multiple nonholonomic robots for sensing coverage

This work deals with decentralized control of multiple nonholonomic mobile sensors for optimal coverage of a given area for sensing purposes. We assume a density function over the region to be covered, which can be viewed as a probability density of the phenomena to be sensed. The density function is unknown but assumed to be linearly parameterized with unknown parameter weights. We consider a second‐order dynamic model for the mobile agents and derive decentralized adaptive control laws to achieve optimal coverage of the region. We then consider the case where the dynamic model of the agents are not fully known, and then develop parameter adaptation laws to achieve the optimal coverage objective. We test the derived algorithms using simulations and compare our proposed controllers with kinematics‐based controllers. We find that the feedback control design based on the dynamic model performs significantly better than controllers solely relying on kinematic models. Furthermore, for the unknown dynamics case, our controller outperforms the nonadaptive controller with poor initial parameter estimates.     

Contribution to the indirect decentralized adaptive control of manipulation robots

In this paper, efficient approaches to the synthesis of indirect decentralized adaptive control for manipulation robots are presented. The first part of control synthesis consists of the estimation of unknown dynamic robot parameters using the methods of recursive identification and fast dynamic as well as identification models in a symbolic form. The second part of synthesis includes the self-tuning control strategy which is a basis for adaptive control synthesis according to the estimates of the unknown dynamic parameters. Using the theory of decentralized systems, a new robust algorithm for adaptive control with the ability of adaptation in the feedforward or feedback loop are proposed. A complete stability and convergence analysis is presented. A special part of the paper represents an analysis of practical implementation of the proposed control algorithms on modern microprocessor-based robot controllers. Based on this analysis, an efficient application of indirect adaptive algorithms in real time with high-quality system performance is shown. Adaptive algorithms are verified through simulation of trajectory tracking for an industrial robot with unknown dynamic parameters of payload.     

A hybrid control approach to action coordination for mobile robots

In this paper, the problem concerning how to coordinate the contributions from concurrent controllers, when controlling mobile robots, is investigated. It is shown how a behavior based control system for autonomous robots can be modeled as a hybrid automaton, where each node corresponds to a distinct robot behavior. This type of construction gives rise to chattering executions, but it is shown how regularized automata can be used to solve this problem. As an illustration, the obstacle-negotiation problem is solved by using a combination of a robust path-following behavior and a reactive obstacle-avoidance behavior that move the robot around a given obstacle at a predefined safety distance.     

Fractional-order dynamics and adaptive dynamic surface control of flexible-joint robots

This paper establishes a novel fractional-order model for n-links flexible-joint (FJ) robots and proposes an adaptive dynamic surface control (DSC) scheme to address the tracking control problem. The fractional-order FJ model is built by fractional-order viscoelastic dynamics model to have a more concise form. An adaptive DSC strategy is proposed to address the tracking control problem based on backstepping method. By selecting the appropriate orders for fractional filters, the controller could solve the “explosion of complexity” problem. The unknown nonlinearities of FJ robot systems are approximated by Radial basis function (RBF) neural networks (NNs). Based on the Lyapunov stability theory, the bounds of all signals in the closed-loop system are achieved. The simulation results confirm the effectiveness of the presented control scheme.     

基于免疫计算的抗蠕虫病毒Web系统

为了高精度地检测、识别和消除Web系统中的蠕虫病毒,为了实现Web系统的故障自修复,提出了Web系统的正常模型和免疫计算方法。Web系统的正常模型是由各个组件的时空属性表示的,存储在自体数据库中,系统中所有组件的时空属性唯一确定了该软件系统的正常状态。免疫计算方法包括自体/异体的检测算法、已知蠕虫病毒的识别算法、未知蠕虫病毒的识别算法、蠕虫病毒的消除算法和受损系统的修复算法。通过数学逻辑论证,正常模型能在理论上实现对自体和软件故障的100%检测率。“人工智能”网络课程Web原型上的抗蠕虫病毒实验表明,正常模型和免疫计算对实现Web系统的抗蠕虫病毒功能是有效的和必要的,能提高蠕虫病毒检测的精度和系统修复的效率。     

A brief review of dynamics and control of underactuated biped robots

Humans as bipeds enjoy certain advantages over other terrestrial systems, which motivate us to study and develop biped robots. Underactuated biped robots adopt the energy efficient gait of the biological counterparts and passive walkers. However, the control design for such robots is challenging due to lesser controllable joints, non-linear hybrid system dynamics and the goal of utilizing the natural dynamics. This paper summarizes various designs, models and control strategies used to enable stable walking and running for the underactuated biped robots. It gives a brief about how the mechanism of such bipeds evolved to incorporate the design variations which significantly improved the system performance. The few basic mathematical models which are used to simulate, analyze and predict the system dynamics and test control designs, are described, highlighting the difference in walking and running models. An introduction to the various stability criteria and control methods, successful in enabling stable walking for the robots on flat or uneven terrains, is provided. This paper gives a brief of the significant achievements in this field and ends with the highlights of the abilities inherent to humans but lacking in underactuated bipeds, and adopting or improving which should be the focus of the future research.     

Decoupling force and motion control in industrial robots

A theoretical approach to force control design for industrial robots involved in surface-following tasks is described in this paper, assuming an infinitely stiff environment. Independent Joint Control techniques, based on standard (PID) algorithms, are adopted for position control. Force control acts as an outer loop, by adding a bias to the position set points in the joint space. A simple model and compensation of the joint flexibility effects, that play an important role in determining the dynamic behavior of the system, are also presented, leading to a force control decoupled from motion control. Some experimental results are discussed, with reference to the industrial robot SMART.