采摘机械臂自适应输入整形控制研究

采摘机械臂在夹住柔性果茎后运输果实时,执行器末端的加减速运动使得果实在移动过程中产生摆动,易引发掉落,进而导致采摘失败.本文以单个西红柿作为负载,将果茎近似为柔性连杆.由于每一个果实的质量是不同的,因此,针对机械臂抓取可变柔性负载移动过程中的振动抑制问题,提出了自适应输入整形控制方法.当系统模型由于负载的不确定性发生变化后,传统的输入整形算法无法抑制柔性连杆移动过程中产生的振动.因此采用自适应输入整形算法,实时计算脉冲的幅值和时间.构造二次性能指标函数,通过对机械臂移动的加速度和负载的摆角实时数据进行迭代运算,达到零残余振动的目的.仿真实验结果表明,在变负载情况下,自适应输入整形算法有良好的末端振动抑制能力,获得满意的控制效果.     

基于Leap Motion和S曲线的飞行机器人机械臂控制研究

针对带有多自由度机械臂的飞行机器人,提出基于Leap Motion的控制方法以实现机械臂跟随人体手掌位置姿态运动的功能。采用DH方法建立了机械臂数学模型,给出了将Leap Motion获取的人手运动映射到机械臂末端的推导过程。利用7段S型曲线调速方法近似实现舵机角加速度连续没有突变,减轻了舵机快速响应给飞行器带来的冲击问题。设计制作了实物样机对控制方法的可实现性进行验证测试,在飞行测试中,成功地利用Leap Motion控制远端的机械臂抓取到地面目标。     

Assistant force compensation for hand movement of patients by using exogenous signals and/or neurophysiological signals

Because functional diseases of the brain can cause disabilities related to human movement control, a compensation method was developed for improving the performance of hand movements. The compensation for human hand movements can be carried out by adding an assistant force that is generated from artificial equipment attached to a human arm. From the experiment on visual target tracking, it was found that the tracking trajectory was adequately represented by a dynamic model of the motion of an articulated industrial robot arm, and the different abilities for movement control among healthy people and patients were classified by different model parameters as position loop gain, velocity loop gain, and response delay. Dynamic force compensation was approached by considering the different control features of the patients. The effectiveness of the proposed compensation method was verified in a simulation study on an actual industrial robot arm. A human-machine interface, e.g., a brain-computer interface (BCI), for realizing the control of artificial equipment to compensate for human hand movements is also presented and discussed.     

面向人臂三角形动作基元的拟人臂运动学

对拟人臂的运动规划问题进行了研究.通过引入动作基元的概念,将动作基元空间作为连接任务空间与关节空间的桥梁,构建了一个"关节空间-动作基元空间-任务空间"的三级运动规划框架.这种规划方式既保证了对拟人臂运动过程的控制,又简化了复杂操作任务的运动规划.在抽象的动作基元基础之上提出了一个具体的人臂三角形模型,用以描述拟人臂的运动状态.通过引入拟人臂工作平面的概念,利用坐标变换与几何分析相结合的方法详细推导并建立了人臂三角形空间与任务空间和关节空间之间的正逆运动学,为基于人臂三角形的动作基元设计奠定了基础.最后通过仿真算例验证了运动学算法的有效性与可行性.     

一种基于状态空间分析法的人工情感模型

从人类情感的特征出发,分析了情感的变化规律,引入转移系数矩阵、情感强度的衰减因子以及情感的灵敏因子描述不同个体的情感特征,提出一种基于马尔可夫链和状态空间分析方法的情感模型构建方法,分析了该模型的稳定性,并使用Matlab软件对情感的自发转移和控制输入时的情感变化以及无输入时情感的变化趋势进行了仿真,仿真结果说明了模型所表示的情感过程接近于人类的情感过程。     

A multi-link system control strategy based on biological reaching movement

In the multi-link arm control process, the problems of trajectory planning and trajectory realization have been recognized as being of key importance. We developed a technique by which to realize a reaching movement control of the multi-link arm system, which was inspired by experimental results for reaching movements of macaques or human beings. The proposed method can treat the effect of the dynamics of the multi-link system and the trajectory planning of the end-effector, which has a bell-shaped speed profile, as well as the difficulties of redundancies of multi-link systems. Two-link arm reaching movement experiments revealed the same features, as demonstrated by the results of biological experiments on humans and macaques. In addition, the results obtained using a two-dimensional four-link model in a standing-up movement control experiment agreed well with 'standing-up from a chair' movement of human beings. Since the proposed method has a simple structure and its implementation process is simple, the proposed method will be effective for use in a multi-link system control strategy.     

Hybrid control of a pneumatic artificial muscle (PAM) robot arm using an inverse NARX fuzzy model

We investigated the possibility of applying a hybrid feed-forward inverse nonlinear autoregressive with exogenous input (NARX) fuzzy model-PID controller to a nonlinear pneumatic artificial muscle (PAM) robot arm to improve its joint angle position output performance. The proposed hybrid inverse NARX fuzzy-PID controller is implemented to control a PAM robot arm that is subjected to nonlinear systematic features and load variations in real time. First the inverse NARX fuzzy model is modeled and identified by a modified genetic algorithm (MGA) based on input/output training data gathered experimentally from the PAM system. Second the performance of the optimized inverse NARX fuzzy model is experimentally demonstrated in a novel hybrid inverse NARX fuzzy-PID position controller of the PAM robot arm. The results of these experiments demonstrate the feasibility and benefits of the proposed control approach compared to traditional PID control strategies. Consequently, the good performance of the MGA-based inverse NARX fuzzy model in the proposed hybrid inverse NARX fuzzy-PID position control of the PAM robot arm is demonstrated. These results are also applied to model and to control other highly nonlinear systems.     

无奇异间接迭代学习控制及其在机器人运动模仿中的应用

针对相当广泛的一类非线性系统有限时间轨迹跟踪问题,提出了间接迭代学习方案. 采用最小二乘算法,根据重复跟踪历史辨识非线性系统的线性化模型.利用一个分段学习方案 可保证学习控制总在有效线性近似区域内进行.探讨了如何在学习过程中避免控制奇异问题, 提出了一种高效的参数修正方法,保证输入耦合矩阵的估计行列式不为零.本文将这一控制方 案应用于未知机器人及摄像机模型下的机器人运动模仿中,而不面临任何奇异问题.这是一个 采用摄像机替代传统程序编写的新的机器人编程方法.     

A Novel Scheme for Human-Friendly and Time-Delays Robust Neuropredictive Teleoperation

A novel Neuropredictive Teleoperation (NPT) Scheme is presented. The design results from two key ideas: the exploitation of the measured or estimated neural input to the human arm or its electromyograph (EMG) as the system input and the employment of a predictor of the arm movement, based on this neural signal and an arm model, to compensate for time delays in the system. Although a multitude of such models, as well as measuring devices for the neural signals and the EMG, have been proposed, current telemanipulator research has only been considering highly simplified arm models. In the present design, the bilateral constraint that the master and slave are simultaneously compliant to each other's state (equal positions and forces) is abandoned, thus obtaining a simple to analyze succession of only locally controlled modules, and a robustness to time delays of up to 500 ms. The proposed designs were inspired by well established physiological evidence that the brain, rather than controlling the movement on-line, programs the arm with an action plan of a complete movement, which is then executed largely in open loop, regulated only by local reflex loops. As a model of the human arm the well-established Stark model is employed, whose mathematical representation is modified to make it suitable for an engineering application. The proposed scheme is however valid for any arm model. BIBO-stability and passivity results for a variety of local control laws are reported. Simulation results and comparisons with traditional designs also highlight the advantages of the proposed design.     

具有量化输入和边界扰动的柔性臂边界振动滑模控制

本文针对量化输入和有界扰动下柔性臂系统的振动抑制和边界滑模控制器设计问题开展研究. 柔性臂的动态特性由偏微分方程表示的分布参数模型描述. 对于具有未知有界干扰的柔性臂系统, 其主要控制目标是减小干扰的影响, 使柔性臂到达期望角度并同时抑制系统的振动. 首先, 利用边界输出信号构造滑模函数和滑模面. 其次, 结合所构造的滑模面, 设计一种边界滑模控制器, 并利用算子半群理论证明了闭环系统的适定性. 所提出的边界滑模控制策略保证了系统状态能够在有限时间内到达滑模面, 并且系统状态在滑模面上是指数收敛的. 最后, 通过物理实验验证了所提出控制策略的有效性.     

Non-linear phase different control for precise output force of bi-articularly actuated manipulators

Bi-articular actuators – actuator spanning two joints – play fundamental role in robot arms designed under the human musculoskeletal actuation paradigm. Unlike kinematic redundancy, actuator redundancy resulting from bi-articular actuation brings advantages such as increasing stability, reducing link's inertia, and decreasing non-linearity of the end-effector force with respect to the force direction. The traditional phase different control (PDC) resolves actuator redundancy on the basis of a linearized model derived from measured human muscle activity. Such linear model produces a non-zero error in calculation between a desired output force and necessary inputs. In this paper, the non-linear phase different control (NLPDC) is proposed to resolve actuator redundancy with no error. The maximum end-effector force of BiWi, bi-articularly actuated, and wire-driven arm, is measured using both PDC and NLPDC. When the robot arm moves towards singular configurations, the measured error in output force remains within the modeling error if using NLPDC, while such error increases significantly for PDC. Furthermore, unlike PDC, the proposed NLPDC allows design of joint stiffness and torque independently, reduction of necessary total muscle input force, and precise calculation of maximum output force.     

Input-output linearizing control of constrained nonlinear processes

An input-output linearization strategy for constrained nonlinear processes is proposed. The system may have constraints on both the manipulated input and the controlled output. The nonlinear control system is comprised of: (i) an input-output linearizing controller that compensates for processes nonlinearities; (ii) a constraint mapping algorithm that transforms the original input constraints into constraints on the manipulated input of the feedback linearized system; (iii) a linear model predictive controller that regulates the resulting constrained linear system; and (iv) a disturbance model that ensures offset-free setpoint tracking. As a result of these features, the approach combines the computational simplicity of input output linearization and the constraint handling capability of model predictive control. Simulation results for a continuous stirred tank reactor demonstrate the superior performance of the proposed strategy as compared to conventional input-output linearizing control and model predictive control techniques.     

A self-organized model for the control,planning and learning of nonlinear multi-dimensional systems using a sensory feedback

A new approach is presented to deal with the problem of modelling and simulating the control mechanisms underlying planned-arm-movements. We adopt a synergetic view in which we assume that the movement patterns are not explicitly programmed but rather are emergent properties of a dynamic system constrained by physical laws in space and time. The model automatically translates a high-level command specification into a complete movement trajectory. This is an inverse problem, since the dynamic variables controlling the current state of the system have to be calculated from movement outcomes such as the position of the arm endpoint. The proposed method is based on an optimization strategy: the dynamic system evolves towards a stable equilibrium position according to the minimization of a potential function. This system, which could well be described as a feedback control loop, obeys a set of non-linear differential equations. The gradient descent provides a solution to the problem which proves to be both numerically stable and computationally efficient. Moreover, the addition into the control loop of elements whose structure and parameters have a pertinent biological meaning allows for the synthesis of gestural signals whose global patterns keep the main invariants of human gestures. The model can be exploited to handle more complex gestures involving planning strategies of movement. Finally, the extension of the approach to the learning and control of non-linear biological systems is discussed.     

宏观经济运行的控制与决策模型

根据系统论和控制论原理，模拟宏观经济的运行机理,获得了宏观经济系统的传递函数和时域形式的数学模型；然后根据经济规划离散性特征,得出了符合实际情况的更替循环控制与决策模型，以及离散逐次投入控制与决策模型。     

Semi-global sampled-data output feedback disturbance rejection control for a class of uncertain nonlinear systems

This paper investigates the semi-global output feedback disturbance rejection control problem for a class of uncertain nonlinear systems with additive disturbances using linear sampled-data control. Aiming to reject the adverse effects caused by the uncertainties and unknown nonlinear perturbations which may not satisfy the strict feedback or feedforward structure, a new generalised discrete-time extended state observer is proposed to estimate the disturbance at sampling points. An output feedback disturbance rejection control law is then constructed in a sampled-data form which facilitates digital implementations. By selecting adequate control gains and a sufficiently small sampling period to restrain the state growth under a zero-order-hold input, the semi-global asymptotic stability of the hybrid closed-loop system and the disturbance rejection ability are proved. Both numerical example and an application of a single-link robot arm system demonstrate the feasibility and efficacy of the proposed method.     

Seated leg/foot ballistic and visually-controlled movements

An experiment with 52 participants investigated the relationship between movement time of the leg/foot for seated persons when moving in the transverse and sagittal planes. Four amplitudes of movement and 11 values of Fitts' Index of Difficulty (ID) were used to determine conditions under which ballistic movements could be made along with the need for visual control at higher ID values. Vision of the foot was available in all movements. As with arm movements (Gan and Hoffmann, 1988) there was a critical ID value below which it was possible to use ballistic movements and where movement times were approximately linear with the square-root of movement amplitudes. Above these ID values, Fitts' law applied, with gradients dependent on the amplitude of movement, suggesting that the muscle torque applied to the leg varied with movement amplitude. The critical ID varied with the amplitude of movement as previously found for arm movements.Relevance to industryThere is increasing use of the foot/leg for input to various controlling devices. Consequently it is necessary to have detailed information on the capacity of the leg/foot system to provide accurate input to a machine via a control pedal or other device. The present research provides such information over a wide range of control sizes and spacings.     

Adaptive input shaping for single-link flexible manipulators using an algebraic identification

This work proposes an adaptive control scheme applied to single link-flexible manipulators, which combines a feedback controller of the joint angle with an adaptive input shaper updated by an algebraic non-asymptotic identification. The feedback controller is designed to guarantee trajectory tracking of the joint angle, simplifying thus the input shaper, which can be designed for the arm dynamics only. The input shaper is updated by an algebraic identification of the natural frequency corresponding to the first vibration mode of the arm. In addition, the influence of the assumptions adopted to derive the algebraic identification on the performance of the estimation is studied. Finally, the proposed adaptive control strategy is implemented in practice.     

An optimal control model of a neuromuscular system in human arm movements and its control characteristics

The joint torque which sets human limbs into motion is generated by a separate group of muscles provided for each joint. As the activation of each muscle is determined by a neural input, a neuromuscular system controlling all muscles has to be considered in order to understand human movements. In this study, an optimal control model of a neuromuscular system is investigated, and its control characteristics are examined. First, the dynamic and mechanical properties of a muscle are examined, and a neuromuscular system is formulated mathematically. Second, a performance criterion for the optimal control model is defined in order to characterize the dynamic behavior of the neuromuscular system, and a mathematical procedure for producing optimal trajectories is represented. Third, optimal trajectories in human arm movements are produced under various conditions of movement, and these trajectories are compared with experimentally observed ones. It is then verified that the optimal trajectories demonstrate human arm movements well. Finally, the behavior of individual muscles in various movements is examined quantitatively by means of simulation results, and the control characteristics of the human neuromuscular system are investigated. This work was presented in part at the Sixth International Symposium on Artificial Life and Robotics, Tokyo, January 15–17, 2001.     

一类约束条件下的跟踪保性能控制

本文研究了一类时变非线性系统在输入有位置约束条件下的跟踪保性能控制问题.首先对非线性对象选择合适的特征运行状况,采用瞬时线性化技术得到多个线性化控制模型,将之看作是一个线性不确定系统,提出应用基于线性矩阵不等式的跟踪保性能控制设计控制器,然后经过推导得到了满足控制约束的充分条件,以定理的形式给出了约束条件下跟踪保性能控制器存在的充分条件.最后给出了仿真算例,仿真结果表明了所提方法的有效性和可行性.     

面向智能假肢手臂的生机接口系统与类神经协同控制

针对肢体残障患者的假肢控制问题,搭建了一种基于sEMG（表面肌电信号）的智能假肢手臂系统,实现手臂残障程度较高患者的手－肘协调控制。首先,基于肌肉协同理论,使用非负矩阵分解（NMF）方法提取肌肉协同作用,并进行手部动作识别以及肘关节的连续运动估计。其次,基于意图识别结果构建“前馈－反馈”控制框架,对受试者进行前馈监督与反馈检测;根据前馈－反馈结果调整期望的控制输入,提高假肢系统的舒适性与鲁棒性。然后,针对手部动作,构建一种自适应调整抓握力度的框架,通过力、位信息交替控制,实现不同刚度、不同形状物体的自适应抓握;对于肘部运动,设计一种基于识别结果的阻抗控制算法,实现手－肘一体化假肢的稳定的人机交互控制。最后,由6名健康受试者、1名手臂残障受试者对以上控制策略进行实验验证,对手臂整体运动实现了较为准确的意图识别,同时也完成了稳定的肘部屈伸以及手部抓取,做到了手－肘的一体化协调控制。最终该套系统在北京2022年冬残奥会实现了应用展示。