上肢康复机器人实时安全控制

针对上肢辅助康复机器人临床使用中的安全性和平稳性问题,提出基于模糊逻辑的实时在线安全监测控制方法.机器人对患肢进行康复训练时,患肢状态对控制效果会产生影响;通过设计智能安全监控模糊控制器(SSFC)改善系统运动平稳性以及突发情况下的安全性.首先提取相关运动特征评估受训患肢状态稳定情况,安全监控模糊控制器智能实现正常扰动情况下的控制期望力调节以及突发情况下的紧急响应.其次通过基于位置的阻抗控制策略实现患肢与机器人末端的柔顺性.实验结果验证了该控制方法能够有效地实现康复机器人的安全性和平稳性.     

基于外骨骼的可穿戴式上肢康复机器人设计与研究

为辅助对患肢进行高强度标准化康复训练工作,研制了一种基于外骨骼原理的可穿戴式4自由度上肢康复机器人.该康复机器人可实现肩关节水平方向外展/内收、竖直方向上摆/下摆和旋转运动以及肘关节的屈/伸运动.首先根据康复医学原理确定出人手臂各关节的运动角度范围,并在确保驱动力矩最小的原则下进行结构设计.然后,对各结构进行了运动学与动力学仿真分析,并据此对结构进行优化.最后,设计了康复机器人在连续被动康复运动(CPM)模式下的控制系统.实验结果表明,此结构方便穿戴于人体,机器人的运动自由度与人体运动自由度同轴,能有效地对患肢前、后臂各部位进行支撑和牵引,精确地施加牵引力于上肢的各关节.     

基于IPC和PMAC的喷涂机器人控制系统

以工业级计算机IPC和DSP作为喷涂机器人的控制器,设计了一种基于可编程多轴控制器PMAC(Programmable Multi-Axis Controller)的开放式机器人控制系统.系统采用双微机分级控制方式和模块化结构软件设计,上级IPC负责系统管理和路径规划,下级PMAC则实现对各个关节的位置伺服控制和多个关节的协调控制,满足了喷涂机器人实时性控制的要求.系统采用电流、速度为内环.位置为外环的三层半闭环的关节运动伺服控制方法,保证了关节的位置精度.     

基于模糊逻辑的上肢康复机器人阻抗控制实验研究

针对机器人辅助患肢主动康复训练过程中辅助力／阻力不能随患肢病情实时调整的问题,提出了一种 新的模糊自适应阻抗力控制方法．该方法实时检测患肢与机器人之间的相互作用力,并进一步运用辨识算法实时估 计出患肢的病情状态;然后运用模糊阻抗控制器对两者之间的相互作用力进行实时调整,使得在患肢主动能力不 足时提供一定的辅助,而在其有能力完成动作时,实时调整阻力实现肌力训练．实验结果表明了该控制方法的有效 性．     

基于LMI方法的机器人LPV鲁棒H_∞控制器设计

对平面两关节直接驱动机器人 ,提出一种同时将闭环极点配置到满足动态响应区域内的变增益 L PV鲁棒 H∞ 控制器设计新方法。利用 L PV的凸分解方法 ,将机器人模型化为具有凸多面体结构的L PV模型 ,然后利用 L MI技术对凸多面体各顶点分别设计满足 H∞ 性能和闭环极点配置的反馈增益 ,再利用各顶点设计的反馈控制器综合得到具有凸多面体结构的 L PV控制器。仿真结果验证了该控制器可使机器人随关节位置变化始终具有良好的控制性能。     

基于LM I方法的机器人L PV 鲁棒H ∞控制器设计

对平面两关节直接驱动机器人,提出一种同时将闭环极点配置到满足动态响应区域内的变增益LPV鲁棒H∞控制器设计新方法,利用LPV的凸分解方法,将机器人模型化为具有凸多面体结构的LPV模型,然后利用LMI技术对凸多面体各顶点分别设计满足H∞性能和闭环极点配置的反馈增益,再利用各顶点设计的反馈控制器综合得到具有凸多面体结构的LPV控制器,仿真结果验证了该控制器可使机器人随关节位置变化始终具有良好的控制性能。     

两自由度Dobot机器人控制仿真分析

机器人是代替和解放人类劳动力的重要产品,它对运动位置和速度控制要求较高。针对DOBOT机器人的双闭环控制方法进行研究。首先,分别利用DH方法和Kane方程建立了Dobot机器人运动学和动力学模型,并建立了这个系统的动力学模型。然后,根据某企业对Dobot机器人的要求和机器人自身的特点,建立系统的PI速度内环和PD位置外环的双闭环控制模型。最后,根据实际参数,在MATLAB中搭建了机器人的半物理仿真模型,设置相关运动参数和控制参数,对关节控制器的阶跃信号的响应和正弦输入信号响应进行仿真,并对双闭环控制器对机器人末端位置的控制性能进行了控制仿真试验。研究结果表明,关节控制器具有较好的响应能力,双闭环控制器控制的末端运动误差小于设计方案的要求,且控制效果很好。该研究为机器人的实际应用奠定了基础。     

髋关节康复机器人的运动学分析与仿真

康复机器人能够按照指定轨迹稳定运行是康复过程安全性和有效性的重要保证,因此机器人末端位姿与人体患肢位姿应保持高度重合,同时康复机器人应具备适应不同人体患肢长度的能力。为此提出了机器人与人体患肢运动学模型关联的计算方法。对所设计的平卧式五自由度髋关节康复串联机器人建立了运动学数学模型。将人体下肢简化为四自由度两关节连杆,建立人体的运动学模型,根据人体下肢参数计算患肢末端位姿,并将其作为输入条件代入机器人运动学模型求解,得到机器人的关节变量对关节转动进行控制。以屈髋和内旋动作为例,应用SimMechanics进行仿真,得到的各关节角度与目标设定值一致,且机器人关节角度范围满足人体髋关节活动度的康复要求。分析了下肢长度测量误差对髋关节康复角度及位置的影响。结果表明当大腿长度占比测量偏差为0.5%,位置偏差小于6 mm时,关节角度偏差小于1°。     

欠驱动冗余度空间机器人优化控制

欠驱动控制是空间技术中容错技术的重要方面.本文研究了被动关节中有制动器的欠驱动冗余度空间机器人系统的运动优化控制问题.从系统动力学方程出发,分析了欠驱动冗余度空间机器人的优化能力和控制方法;给出了主、被动关节间的耦合度指标;提出了欠驱动冗余度空间机器人系统的“虚拟模型引导控制”方法,在这种方法中采用与欠驱动机器人机构等价的全驱动机器人作为模型来规划机器人的运动,使欠驱动系统在关节空间中逼近给出的规划轨迹,实现了机器人末端运动的连续轨迹运动优化控制;通过末关节为被动关节的平面三连杆机器人进行了仿真,仿真的结果证明了提出算法的有效性.     

仿袋鼠跳跃机器人用关节运动控制器设计

设计了一种基于TI公司高性能浮点DSP TMS320F28335和双全桥PWM电机驱动器DRV8402的仿袋鼠跳跃机器人关节运动控制器.控制器实时采集机器人3个关节相对运动角度,利用积分分离的PID控制算法实现关节驱动电机位置闭环控制.设计了基于LabWindows/CVI的上位机测控软件,制作了控制器样机并进行系统联...     

Adaptive Impedance Control for Upper-Limb Rehabilitation Robot Using Evolutionary Dynamic Recurrent Fuzzy Neural Network

Control system implementation is one of the major difficulties in rehabilitation robot design. A newly developed adaptive impedance controller based on evolutionary dynamic fuzzy neural network (EDRFNN) is presented, where the desired impedance between robot and impaired limb can be regulated in real time according to the impaired limb??s physical recovery condition. Firstly, the impaired limb??s damping and stiffness parameters for evaluating its physical recovery condition are online estimated by using a slide average least squares (SALS)identification algorithm. Then, hybrid learning algorithms for EDRFNN impedance controller are proposed, which comprise genetic algorithm (GA), hybrid evolutionary programming (HEP) and dynamic back-propagation (BP) learning algorithm. GA and HEP are used to off-line optimize DRFNN parameters so as to get suboptimal impedance control parameters. Dynamic BP learning algorithm is further online fine-tuned based on the error gradient descent method. Moreover, the convergence of a closed loop system is proven using the discrete-type Lyapunov function to guarantee the global convergence of tracking error. Finally, simulation results show that the proposed controller provides good dynamic control performance and robustness with regard to the change of the impaired limb??s physical condition.     

Control System Design for an Upper-Limb Rehabilitation Robot

Control system implementation is one of the major difficulties in rehabilitation robot design. The purpose of our study is to present newly developed control strategies for an upper-limb rehabilitation robot. The Barrett WAM Arm manipulator is used as the main hardware platform for the functional recovery training of the past-stroke patient. Passive and active recovery training have been implemented on the WAM Arm. A fuzzy-based PD position control strategy is proposed for the passive recovery exercise to control the WAM Arm stably and smoothly to stretch the impaired limb to move along predefined trajectories. An adaptive impedance force controller is employed in the active motion mode in which a fuzzy logic regulator is used to adjust the desired impedance between the robot and impaired limb to generate adaptive force in agreement with the change of the impaired limb's muscle strength. In order to evaluate the change of the impaired limb's muscle power, the impaired limb's mechanical impedance parameters as an objective evaluation index is estimated online by using a recursive least-squares algorithm with an adaptive forgetting factor. Experimental results demonstrate the effectiveness and potential of the proposed control strategies.     

Passivity-based non-fragile control for Markovian jump systems with aperiodic sampling

In this paper, the problem of non-fragile passive control for Markovian jump systems with aperiodic sampling is investigated. The considered controller is assumed to have either additive or multiplicative norm-bounded uncertainties. A time-dependent Lyapunov functional capturing the available information of the sampling pattern is constructed to derive a sufficient condition for non-fragile stochastic passivity of the resultant closed-loop system. Based on the condition, a mode-independent state feedback sampled-data controller is designed such that for all admissible uncertainties the closed-loop system is robustly stochastically passive. Two illustrative examples are included to demonstrate the effectiveness and merits of the proposed techniques.     

非线性系统的直接自适应输出反馈监督模糊控制

针对一类单输入单输出非线性不确定系统，提出一种稳定的直接自适应模糊输出反馈监督控制算法，该算法不需要系统的状态完全可测的假设条件，监督控制不仅迫使系统的状态在指定的集合内，而且当模糊自适应控制处于良好的工作状态时，监督控制可以关闭，证明了整个模糊自适应输出反馈控制算法可以保证闭环系统稳定。     

非线性系统的间接自适应模糊输出反馈监督控制

In this paper, an indirect adaptive fuzzy output feedback controller with supervisory mode for a class of unknown nonlinear systems is developed. The proposed approach does not need the availability of the state variables, moreover, a supervisory controller is appended to the adaptive fuzzy controller to force the state to be within the constraint set. Therefore, if the adaptive fuzzy controller cannot maintain the stability, the supervisory controller starts to work to guarantee stability. On the other hand, if the adaptive fuzzy controller works well, the supervisory controller will be deactivated. The overall adaptive fuzzy control scheme guarantees the stability of the whole closed-loop systems. The simulation results confirm the effectiveness of the proposed method.     

Indirect Adaptive Fuzzy Output Feedback Control with Supervisory Controller for Uncertain Nonlinear Systems

In this paper, an indirect adaptive fuzzy output feedback controller with supervisory mode for a class of unknown nonlinear systems is developed. The proposed approach does not need the availability of the state variables, moreover, a supervisory controller is appended to the adaptive fuzzy controller to force the state to be within the constraint set. Therefore, if the adaptive fuzzy controller cannot maintain the stability, the supervisory controller starts to work to guarantee stability. On the other hand, if the adaptive fuzzy controller works well, the supervisory controller will be de-activated. The overall adaptive fuzzy control scheme guarantees the stability of the whole closed-loop systems. The simulation results confirm the effectiveness of the proposed method.     

A Control Approach for Actuated Dynamic Walking in Biped Robots

This paper presents an approach for the closed-loop control of a fully actuated biped robot that leverages its natural dynamics when walking. Rather than prescribing kinematic trajectories, the approach proposes a set of state-dependent torques, each of which can be constructed from a combination of low-gain spring–damper couples. Accordingly, the limb motion is determined by interaction of the passive control elements and the natural dynamics of the biped, rather than being dictated by a reference trajectory. In order to implement the proposed approach, the authors develop a model-based transformation from the control torques that are defined in a mixed reference frame to the actuator joint torques. The proposed approach is implemented in simulation on an anthropomorphic biped. The simulated biped is shown to converge to a stable, natural-looking walk from a variety of initial configurations. Based on these simulations, the mechanical cost of transport is computed and shown to be significantly lower than that of trajectory-tracking approaches to biped control, thus validating the ability of the proposed idea to provide efficient dynamic walking. Simulations further demonstrate walking at varying speeds and on varying ground slopes. Finally, controller robustness is demonstrated with respect to forward and backward push-type disturbances and with respect to uncertainty in model parameters.      

Optimal impedance via model predictive control for robot-aided rehabilitation

This paper proposes an optimal impedance controller for robot-aided rehabilitation of walking, aiming to increase the patient’s activity during the therapy. In an online procedure, the joint torques produced by the patient during the gait is estimated using the generalized momenta-based disturbance observer and the Extended Kalman filter algorithm. At the same time, a model predictive control is performed to obtain the instantaneous optimal stiffness parameters of the robot’s impedance controller, trying to maximize the patient’s active participation by increasing his/her joint torques. In this feasibility study, experiments with a healthy subject, considering a modular lower limb exoskeleton and a set of user’s behaviors, are performed to evaluate the proposed controller. The results show the robot stiffness converges to a value which increases the user’s active participation.