On the Passivity-Based Impedance Control of Flexible Joint Robots

In this paper, a novel type of impedance controllers for flexible joint robots is proposed. As a target impedance, a desired stiffness and damping are considered without inertia shaping. For this problem, two controllers of different complexity are proposed. Both have a cascaded structure with an inner torque feedback loop and an outer impedance controller. For the torque feedback, a physical interpretation as a scaling of the motor inertia is given, which allows to incorporate the torque feedback into a passivity-based analysis. The outer impedance control law is then designed differently for the two controllers. In the first approach, the stiffness and damping terms and the gravity compensation term are designed separately. This outer control loop uses only the motor position and velocity, but no noncollocated feedback of the joint torques or link side positions. In combination with the physical interpretation of torque feedback, this allows us to give a proof of the asymptotic stability of the closed-loop system based on the passivity properties of the system. The second control law is a refinement of this approach, in which the gravity compensation and the stiffness implementation are designed in a combined way. Thereby, a desired static stiffness relationship is obtained exactly. Additionally, some extensions of the controller to viscoelastic joints and to Cartesian impedance control are given. Finally, some experiments with the German Aerospace Center (DLR) lightweight robots verify the developed controllers and show the efficiency of the proposed control approach.     

Complementary Stability and Loop Shaping for Improved Human–Robot Interaction

Robots intended for high-force interaction with humans face particular challenges to achieve performance and stability. They require low and tunable endpoint impedance as well as high force capacity, and demand actuators with low intrinsic impedance, the ability to exhibit high impedance (relative to the human subject), and a high ratio of force to weight. Force-feedback control can be used to improve actuator performance, but causes well-known interaction stability problems. This paper presents a novel method to design actuator controllers for physically interactive machines. A loop-shaping design method is developed from a study of fundamental differences between interaction control and the more common servo problem. This approach addresses the interaction problem by redefining stability and performance, using a computational approach to search parameter spaces and displaying variations in performance as control parameters are adjusted. A measure of complementary stability is introduced, and the coupled stability problem is transformed to a robust stability problem using limited knowledge of the environment dynamics (in this case, the human). Design examples show that this new measure improves performance beyond the current best-practice stability constraint (passivity). The controller was implemented on an interactive robot, verifying stability and performance. Testing showed that the new controller out-performed a state-of-the-art controller on the same system     

Control of bidirectional physical human–robot interaction based on the human intention

This paper presents a control strategy for human–robot interaction with physical contact, recognizing the human intention to control the movement of a non-holonomic mobile robot. The human intention is modeled by mechanical impedance, sensing the human-desired force intensity and the human-desired force direction to guide the robot through unstructured environments. Robot dynamics is included to improve the interaction performance. Stability analysis of the proposed control system is proved by using Lyapunov theory. Real experiments of the human–robot interaction show the performance of the proposed controllers.     

Optimal impedance control based on body inertia for a hydraulically driven hexapod robot walking on uneven and extremely soft terrain

This paper presents the implementation of impedance control for a hydraulically driven hexapod robot named COMET‐IV, which can walk on uneven and extremely soft terrain. To achieve the dynamic behavior of the hexapod robot, changes in center of mass and body attitude must be taken into consideration during the walking periods. Indirect force control via impedance control is used to address these issues. Two different impedance control schemes are developed and implemented: single‐leg impedance control and center of mass‐‐based impedance control. In the case of single‐leg impedance control, we derive the necessary impedance and adjust parameters (mass, damping, and stiffness) according to the robot legs' configuration. For center of mass–based impedance control, we use the sum of the forces of the support legs as a control input (represented by the body's current center of mass) for the derived impedance control and adjust parameters based on the robot body's configuration. The virtual forces from the robot body's moment of inertia are adapted to achieve optimal control via a linear quadratic regulator method for the proposed indirect attitude control. In addition, a compliant switching mechanism is designed to ensure that the implementation of the controller is applicable to the tripod sequences of force‐based walking modules. Evaluation and verification tests were conducted in the laboratory and the actual field with uneven terrain and extremely soft surfaces. © 2011 Wiley Periodicals, Inc.     

Variable stiffness control for SEAs in rehabilitation training

ABSTRACT

Series Elastic Actuator (SEA) with both security and high performance is used extensively for rehabilitation robots with physical interaction. Human joints applied for motion therapy show variable stiffness properties during the process of rehabilitation training. When using robot to do motion therapy, impedance control is one of the most popular methods for rehabilitation works. However, impedance control with constant stiffness usually produces rigidity in the body due to natural changes of muscle tension. It may seriously restrict the achievement of excellent training effect and may even cause harm to patients. In this study, a novel real-time parallel variable stiffness control method is proposed based on cascade impedance controller. First, an SEA joint is analyzed and the limit factor of the impedance frequency is discussed. Subsequently, cascade impedance controller scheme with stiffness adjustment regulator is utilized to achieve the stiffness and the passivity of the controller is proved. Based on the scheme, a novel stiffness self-adjustment algorithm is presented which can regulate the stiffness by impedance approximation. Finally, simulation and experimental results are provided to validate the stiffness adjustment method during the rehabilitation process.     

Robot impedance control and passivity analysis with inner torque and velocity feedback loops

Impedance control is a well-established technique to control interaction forces in robotics. However, real implementations of impedance control with an inner loop may suffer from several limitations. In particular, the viable range of stable stiffness and damping values can be strongly affected by the bandwidth of the inner control loops (e.g., a torque loop) as well as by the filtering and sampling frequency. This paper provides an extensive analysis on how these aspects influence the stability region of impedance parameters as well as the passivity of the system. This will be supported by both simulations and experimental data. Moreover, a methodology for designing joint impedance controllers based on an inner torque loop and a positive velocity feedback loop will be presented. The goal of the velocity feedback is to increase (given the constraints to preserve stability) the bandwidth of the torque loop without the need of a complex controller.     

Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

In this paper, we present a novel data-driven design method for the human-robot interaction (HRI) system, where a given task is achieved by cooperation between the human and the robot. The presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller design. The task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop, while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the inner-loop. Data-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance parameters. In the inner-loop, a velocity-free filter is designed to avoid the requirement of end-effector velocity measurement. On this basis, an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task space. The simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.      

Model reference adaptive impedance control in Cartesian coordinates for physical human–robot interaction

In this paper, a nonlinear model reference adaptive impedance controller is proposed and tested. The controller provides asymptotic tracking of a reference impedance model for the robot end-effector in Cartesian coordinates applicable to rehabilitation robotics or any other human–robot interactions such as haptic systems. The controller uses the parameters of a desired stable reference model which is the target impedance for the robot’s end-effector. It also considers uncertainties in the model parameters of the robot. The asymptotic tracking is proven using Lyapunov stability theorem. Moreover, the adaptation law is proposed in joint space for reducing the complexity of its calculations; however, the controller and the stability proof are all presented in Cartesian coordinates. Using simulations and experiments on a two DOFs robot, the effectiveness of the proposed controller is investigated.     

On the enhanced passivity of pneumatically actuated impedance-type haptic interfaces

The stable simulation of high-stiffness surfaces remains a challenge in impedance-type haptic simulations of mechanical environments. In this paper, the authors propose an approach to achieving a stable, high-stiffness surface in a haptic interface by leveraging the open-loop properties of pneumatic actuators. By using the open-loop component of the actuator stiffness as a primary component of stiffness simulation in a haptic interface, the system requires a comparatively small component of simulated stiffness from the closed-loop control of the actuator. A passivity analysis is presented describing how the presence of an open-loop stiffness enhances the range of passivity of haptically simulated high-stiffness surfaces. Experimental results both with and without a human operator are presented that demonstrate the effectiveness of the approach and its enhanced passivity relative to motor-actuated devices.     

Adaptive visual gesture recognition for human–robot interaction using a knowledge-based software platform

In human–human communication we can adapt or learn new gestures or new users using intelligence and contextual information. Achieving natural gesture-based interaction between humans and robots, the system should be adaptable to new users, gestures and robot behaviors. This paper presents an adaptive visual gesture recognition method for human–robot interaction using a knowledge-based software platform. The system is capable of recognizing users, static gestures comprised of the face and hand poses, and dynamic gestures of face in motion. The system learns new users, poses using multi-cluster approach, and combines computer vision and knowledge-based approaches in order to adapt to new users, gestures and robot behaviors. In the proposed method, a frame-based knowledge model is defined for the person-centric gesture interpretation and human–robot interaction. It is implemented using the frame-based Software Platform for Agent and Knowledge Management (SPAK). The effectiveness of this method has been demonstrated by an experimental human–robot interaction system using a humanoid robot ‘Robovie’.     

Experimental study of a momentum-based method for identifying the inertia barycentric parameters of a human body

Physical modeling, simulation and analysis of an individual human body require inertia properties of the body segments of the human. Such subject-specific inertia data can be obtained only by measuring the individual human body as opposed to be derived from statistically generated anthropometric database. This paper presents experimental validation of a momentum-based approach for identifying the barycentric parameters of an individual human body which fully describes the inertia properties of the human. The identification algorithm is derived from the impulse–momentum equations of the human body which is assumed to be a multibody system with tree-type topology. Since the impulse–momentum equations are linear in terms of the unknown barycentric parameters, these parameters can be solved from the equations using a least-squares method. The approach does not require measuring or estimating accelerations and joint forces/torques because they do not appear in the impulse–momentum equations, and thus, the resulting identification procedure is less demanding on measurement data than the methods derived from the equations of motion. In this paper the test results of the identification method are validated by comparing the identified inertia parameters against the statistically established anthropometric data. Additionally, the identification results are also confirmed by comparing the contact forces using inverse dynamics to those obtained by forces plates.     

Force feedback control design for nonideal teleoperators

Achieving force feedback for a nonideal teleoperator is challenging, due to complications such as friction, force sensor noise, non-backdriveability and structural resonances. Furthermore, non-collocation of the force sensors and the point of interaction results in shunt dynamics that degrade the interaction force estimation. In this paper, a method is presented to model, identify and compensate for the influence of shunt dynamics. Furthermore, a recently developed two-layer approach that enforces passivity in the time domain is implemented and evaluated in a practical setup that is dedicated for application in surgery. Experiments demonstrate that using a combination of these techniques with an impedance reflecting controller, stable bilateral interaction with both soft and hard environments is achieved, for a nonideal system. A teleoperated robot for minimally invasive surgery is used as a representative example of a nonideal surgical system.     

Learning impedance control for physical robot–environment interaction

In this article, learning impedance control is proposed for physical robot–environment interaction. Learning mechanism is developed such that the knowledge of the robot structure is not required. With the developed method, the dynamics of the robot arm is governed to follow a target impedance model and the interaction control objective is achieved. The control performance is discussed through the rigorous analysis. The validity of the proposed method is verified by simulation studies.     

Enhanced teleoperation performance using hybrid control and virtual fixture

To develop secure, natural and effective teleoperation, the perception of the slave plays a key role for the interaction of a human operator with the environment. By sensing slave information, the human operator can choose the correct operation in a process during the human–robot interaction. This paper develops an integrated scheme based on a hybrid control and virtual fixture approach for the telerobot. The human operator can sense the slave interaction condition and adjust the master device via the surface electromyographic signal. This hybrid control method integrates the proportional-derivative control and the variable stiffness control, and involves the muscle activation at the same time. It is proposed to quantitatively analyse the human operator's control demand to enhance the control performance of the teleoperation system. In addition, due to unskilful operation and muscle physiological tremor of the human operator, a virtual fixture method is developed to ensure accuracy of operation and to reduce the operation pressure on the human operator. Experimental results demonstrated the effectiveness of the proposed method for the teleoperated robot.     

A real-time impedance-based singularity and joint-limits avoidance approach for manual guidance of industrial robots

This paper studies real-time manual guidance considering singularity and joint-limits avoidance using impedance control in an industrial scenario. The operator is responsible for keeping the end-effector (EE) away from the robot’s singularity and joint-limits. The proposed approach detects the singularity and joint-limits in real-time. Then, virtual stiffness and damping are added to target stiffness and damping as the robot is getting close to the singularity or joint-limit. A criterion is presented for detection of singularity by combining manipulability ellipsoid and condition number. Also a new joint to Cartesian space transformation is formulated in order to convert joint stiffness and damping to Cartesian stiffness and damping for joint-limits avoidance method. The presented approach is applied on a SCARA robot. An experiment is performed in this paper to investigate singularity and joint-limits avoidance separately as well as together. Increase in stiffness and damping warn the operator of the possibility of singularity or joint-limits allowing the operator to changes the EE path. The proposed approach, eliminates the need for a robotics expert by allowing any operator with no knowledge about robot singularity and joint-limits to interact and teach the robot in a safe, real-time and time-saving manner.     

基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法

设计了一种基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法.在机器人与人体上肢接触面安装力传感器采集人机交互力矩信息作为量化的主动运动意图,设计了一种无模型自适应滤波算法使交互力矩变得平滑而连贯;以人机交互力矩为输入,综合考虑机器人末端点与参考轨迹的相对位置和补偿力的信息,设计了人机交互阻抗控制器,用于调节各关节的给定目标速度;设计了将无模型自适应与离散滑模趋近律相结合的速度控制器完成机器人各关节对目标速度的跟踪.仿真结果表明,该控制方法可以实现外骨骼式上肢康复机器人辅助患者完成主动交互训练的功能.通过调节人机交互阻抗控制器的相应参数,机器人可以按照患者的运动意图完成不同的主动交互训练任务,并在运动出现偏差时予以矫正.控制器在设计实现过程中不要求复杂准确的动力学建模和参数识别,并有一定的抗干扰性和通用性.     

A Generalized Vision-based Stiffness Controller for Robot Manipulators with Bounded Inputs

Generally, stiffness and impedance control schemes require knowledge of the location of any object with which a robot interacts within its workspace; therefore, the integration of a computer vision system within the control loop allows us to know the location of the robot end effector and the object (target) simultaneously. In this paper, a generalized and saturating vision-based stiffness controller with adaptive gravity compensation is presented. The proposed control algorithm is designed to regulate robot-environment interaction in task-space, where the contact force is modeled as a vector of generalized bounded spring-like forces. In order to control nonredundant robots, the proposed controller has a nonlinear proportional-derivative structure with static model-based compensation of gravitational forces, as it includes a regressor-based adaptive term. To support the proposal, the Lyapunov stability analysis of the closed-loop equilibrium vector is presented. Finally, the suitable performance of the proposed scheme was verified by numerical simulations and experimental tests.

     

基于特征深度学习的机器人协调操作感知控制

为了在操作者与机器人之间实现更为稳定的负载运动状态,提出一种基于特征深度学习的机器人协调操作感知控制方法,分析机器人的协调操作原理,根据虚拟阻尼、虚拟惯量与虚拟刚度系数,构建人机协调操作模型,利用特征深度学习对模型内的神经网络进行网络信息的交互拓扑,优化机器人协调操作的感知性能.同时,减少内存占比,对人力交互信息滤波去...     

Passivity-based tracking control of an omnidirectional mobile robot using only one geometrical parameter

This paper presents an output feedback tracking control scheme for a three-wheeled omnidirectional mobile robot, based on passivity property and a modified generalized proportional integral (GPI) observer. The proposed control approach is attractive from an implementation point of view, since only one robot geometrical parameter (i.e., contact radius) is required. Firstly, a nominal dynamic model is given and the passivity property is analyzed. Then the controller is designed based on passivity property and a modified GPI observer. The controller design objective is to preserve the passivity property of the robot system in the closed-loop system, which is conceptually different from the traditional model-based control methodology. Particularly, the designed control system takes full advantage of the robot natural damping. Therefore, only considerably small or non differential feedback is needed. In addition, theoretical analysis is given to show the closed-loop stability behavior. Finally, experiments are conducted to validate the effectiveness of the proposed control system design in both tracking and robustness performance.     

Augmented Virtual Stiffness Rendering of a Cable-driven SEA for Human-Robot Interaction

Human-robot interaction (HRI) is fundamental for human-centered robotics, and has been attracting intensive research for more than a decade. The series elastic actuator (SEA) provides inherent compliance, safety and further benefits for HRI, but the introduced elastic element also brings control difficulties. In this paper, we address the stiffness rendering problem for a cable-driven SEA system, to achieve either low stiffness for good transparency or high stiffness bigger than the physical spring constant, and to assess the rendering accuracy with quantified metrics. By taking a velocity-sourced model of the motor, a cascaded velocity-torque-impedance control structure is established. To achieve high fidelity torque control, the 2-DOF (degree of freedom) stabilizing control method together with a compensator has been used to handle the competing requirements on tracking performance, noise and disturbance rejection, and energy optimization in the cable-driven SEA system. The conventional passivity requirement for HRI usually leads to a conservative design of the impedance controller, and the rendered stiffness cannot go higher than the physical spring constant. By adding a phase-lead compensator into the impedance controller, the stiffness rendering capability was augmented with guaranteed relaxed passivity. Extensive simulations and experiments have been performed, and the virtual stiffness has been rendered in the extended range of 0.1 to 2.0 times of the physical spring constant with guaranteed relaxed passivity for physical humanrobot interaction below 5 Hz. Quantified metrics also verified good rendering accuracy.