Modification of the Leuven integrated friction model structure

This note presents a modification of the integrated friction model structure proposed by Swevers et al. (2000), called the Leuven model. The Leuven model structure allows accurate modeling both in the presliding and the sliding regimes without the use of a switching function. The model incorporates a hysteresis function with nonlocal memory and arbitrary transition curves. This note presents two modifications of the Leuven model. A first modification overcomes a recently detected shortcoming of the original Leuven model: a discontinuity in the friction force which occurs during certain transitions in presliding. A second modification, using the general Maxwell slip model to implement the hysteresis force, eliminates the problem of stack overflow, which can occur with the implementation of the hysteresis force     

Single state elastoplastic friction models

For control applications involving small displacements and velocities, friction modeling and compensation can be very important. In particular, the modeling of presliding displacement (motion prior to fully developed slip) can play a pivotal role. In this paper, it is shown that existing single-state friction models exhibit a nonphysical drift phenomenon which results from modeling presliding as a combination of elastic and plastic displacement. A new class of single state models is defined in which presliding is elastoplastic: under loading, frictional displacement is first purely elastic and then transitions to plastic. The new model class is demonstrated to substantially reduce drift while preserving the favorable properties of existing models (e.g., dissipativity) and to provide a comparable match to experimental data     

Sensorless force estimation for industrial robots using disturbance observer and neural learning of friction approximation

Contact force estimation enables robots to physically interact with unknown environments and to work with human operators in a shared workspace. Most heavy-duty industrial robots without built-in force/torque sensors rely on the inverse dynamics for the sensorless force estimation. However, this scheme suffers from the serious model uncertainty induced by the nonnegligible noise in the estimation process. This paper proposes a sensorless scheme to estimate the unknown contact force induced by the physical interaction with robots. The model-based identification scheme is initially used to obtain dynamic parameters. Then, neural learning of friction approximation is designed to enhance estimation performance for robotic systems subject with the model uncertainty. The external force exerted on the robot is estimated by a disturbance observer which models the external disturbance. A momentum observer is modified to develop a disturbance Kalman filter-based approach for estimating the contact force. The neural network-based model uncertainty and measurement noise level are analysed to guarantee the robustness of the Kalman filter-based force observer. The proposed scheme is verified by the measurement data from a heavy-duty industrial robot with 6 degrees of freedom (KUKA AUGLIS six). The experimental results are used to demonstrate the estimation performance of the proposed approach by the comparison with the existing schemes.     

摩擦非线性伺服系统辨识建模方法研究

针对伺服控制设计时系统精确建模的重要性,提出了一种主要针对受摩擦非线性影响的系统建模方法,即通过在辨识输入信号中叠加摩擦补偿来抵消摩擦的影响,从而实现对非线性系统的线性化处理的建模方法。并通过模型验证实验,证明经过这种处理方法得到的模型准确化程度很高。     

Redundancy modelling and resolution for robotic mobile manipulators: a general approach

In this work the topic of kinematic redundancy modelling and resolution for robotic mobile manipulators is considered. A set of redundancy parameters is introduced to define a general inverse kinematic procedure for mobile manipulators. Then, redundancy is treated as a non-linear optimization problem with the purpose of finding robot configurations that maximize the designed metric measures. Some strategies to design the optimization objective function are introduced in order to achieve desirable redundant behaviours, such as obstacles avoidance, mobile base motions reductions and dexterity optimization. Moreover, the robot controller has been developed following an object-oriented software architecture principle that allows to keep it general and robot independent. As a prove of reliability and generality of our approach, the same controller has been used to control several different mobile manipulators in a simulation environment, as well as a real KUKA youBot robot.     

Rolling Contact Motion Generation and Control of Robotic Fingers

Dexterity in human hand is connected with the fingertip rolling ability. In this work we consider rolling motion of spherical robotic fingertips as one of the control objectives together with the set point position control and force trajectory tracking. The generation of a rolling motion trajectory is proposed and a control solution is designed which achieves prescribed transient and steady state tracking behavior. The proposed control law is structurally and computationally simple and does not utilize the dynamics of the robot model or its approximation. A simulation of a five degrees of freedom robot show excellent contact rolling performance even at cases of adverse friction conditions while alternative controllers lead to contact sliding. Experiments with a KUKA LWR4 + are performed to validate the proposed method.     

Modeling of static friction in closed-loop kinematic chains—Uniqueness and parametric sensitivity problems

Problems with uniqueness and high parametric sensitivity of the solution of equations of motion, encountered in the static friction regime, are addressed. Friction in joints of a multibody system with closed-loop kinematic chains is discussed. Three different models of friction are studied: the discontinuous Coulomb model with stiction regime represented in terms of additional constraints; the approximate Coulomb model, smoothed in the vicinity of zero relative velocity; and the LuGre model with presliding displacements represented in terms of auxiliary state variables. Firstly, a rigid body model is investigated. It is shown that in the case of constraint addition approach, problems with uniqueness of solution emerge in the static friction regime. In the case of continuous models of friction, the solution in the stiction regime and its vicinity is highly sensitive to some hardly measurable or arbitrarily chosen parameters of the model of friction. Origins of nonuniqueness and high sensitivity are investigated, and the questionable credibility of the stiction regime simulation results is discussed. Secondly, a simplified model of body and joint elasticity is introduced to investigate the impact of flexibility on the mechanism frictional behavior. It is shown that taking the flexibility into consideration may eliminate the uniqueness and sensitivity problems. Moreover, the quantities that represent flexibility may be regarded as the key factors influencing the results of stiction regime simulation. Five examples are provided to illustrate the presented considerations.     

Robot calibration using the CPC error model

The complete and parametrically continuous (CPC) robot kinematic modeling convention has no model singularities and allows the modeling of the robot base and tool in the same manner by which the internal links are modeled. These two properties can be utilized to construct robot kinematic error models employing the minimum number of kinematic error parameters. These error parameters are independent and span the entire geometric error space. The BASE and TOOL error models are derived as special cases of the regular CPC error model. The CPC error model is useful for both kinematic identification and kinematic compensation. This paper focuses on the derivation of the CPC error models and their use in the experimental implementation of robot calibration.     

KUKA机器人运动学算法的研究

如今传统的工业机器人示教编程方式已很难满足生产要求。解决此问题的有效途径之一,就是采用离线编程技术。离线编程技术需要机器人仿真系统的支持。逆解分析是机器人运动学分析的一个重要组成部分,是进行机器人控制和轨迹规划的前提和基础。针对KR系列KUKA运动学进行了分析与研究。其中运动学模型的建立主要采用Denavit.Hartenberg(D-H)参数法。D-H参数法相对成熟,在机器人运动学分析中得到广泛应用。最后给出了KUKA机器人运动学逆解的显式求解结果,以KUKA KR-16机器人为例,验证了算法的正确性,并在仿真环境中进行了测试。     

含有摩擦补偿的全方位移动机器人自抗扰控制

本文针对全方位移动机器人轨迹追踪中的摩擦补偿问题,提出了一种改进的非线性自抗扰控制器.首先建立了含有经典静态摩擦模型的全方位移动机器人动力学模型.其次,基于该模型设计非线性控制器和线性扩张状态观测器并给出了系统的稳定性分析.通过将模型已知项加入线性扩张状态观测器中得到摩擦力的估计值,并将估计值用于非线性控制器中摩擦补偿部分.为减小摩擦力对机器人低速运动轨迹追踪控制的影响,非线性控制器采用变增益控制器进行轨迹追踪控制.最后通过仿真结果验证本文提出控制器的有效性.     

Sparse identification for ball-screw drives considering position-dependent dynamics and nonlinear friction

Establishing accurate dynamic models in a form that is suitable for integration with model-based control methods, is of great significance for further improving the dynamic motion control precision of ball-screw drives. However, due to the nonlinear time-varying factors such as position-dependent dynamics and nonlinear friction disturbance, it is difficult to model the dynamic characteristics of ball-screw drives accurately, concisely and efficiently. To overcome this challenge, a sparse identification method for ball-screw drives is proposed. Ball-screw drives are modeled as discrete-time linear parameter-varying systems under nonlinear friction disturbance, and two types of dictionary function libraries are designed to represent the position-dependent dynamics and nonlinear friction respectively. After constructing the regression form of the system model, a stepwise sparse regression policy is proposed to solve all the coefficients of dictionary functions. The proposed method is verified in both simulation and real environments. The results both show that by the proposed method, an accurate and linearizable dynamic model of ball-screw drives can be identified only using the data from only one global random excitation experiment covering the working stroke.     

An adaptive friction compensator for global tracking in robot manipulators

A novel adaptive friction compensator based on a dynamic model recently proposed in the literature is presented in this paper. The compensator ensures global position tracking when applied to an n degree of freedom robot manipulator perturbed by friction forces with only measurements of position and velocity, and all the system parameters (robot and friction model) unknown. Instrumental for the solution of the problem is the observation that friction compensation can be recasted as a disturbance rejection problem. The control signal is then designed in two steps, first a classical adaptive robot controller that (strictly) passifies the system, and then a relay-based outer-loop that rejects the disturbance.     

Flatness Based Control of an Industrial Robot Joint Using Secondary Encoders

Due to their compliant structure, industrial robots without precision-enhancing measures are only to a limited extent suitable for machining applications. Apart from structural, thermal and bearing deformations, the main cause for compliant structure is backlash of transmission drives. This paper proposes a method to improve trajectory tracking accuracy by using secondary encoders and applying a feedback and a flatness based feed forward control strategy. For this purpose, a novel nonlinear, continuously differentiable dynamical model of a flexible robot joint is presented. The robot joint is modeled as a two-mass oscillator with pose-dependent inertia, nonlinear friction and nonlinear stiffness, including backlash. A flatness based feed forward control is designed to improve the guiding behaviour and a feedback controller, based on secondary encoders, is implemented for disturbance compensation. Using Automatic Differentiation, the nonlinear feed forward controller can be computed in a few microseconds online. Finally, the proposed algorithms are evaluated in simulations and experimentally on a real KUKA Quantec KR300 Ultra SE.     

A discrete model of a stochastic friction system

To investigate a random friction system must cost a large of time on computer. So appropriate modeling of this system is of significance in practice. In this paper, an average model on simple random system with friction is firstly developed which is a discrete model by a two-dimensional mean Poincaré map. It is applied to describe random stick–slip motion. The numerical example shows that external noise can change the system behavior. That model is extended to a MDOF system only with a friction interface. The example of 3-DOF system exhibits the interesting behavior due to influence of external noise.     

High path tracking control of an intelligent walking-support robot under time-varying friction and unknown parameters

Walking is the most fundamental requirement for independent living in daily life. An intelligent walking-support robot has been developed for use by people with walking disabilities. To appropriately assist the user, the robot must precisely track the user’s intentions. However, the robot’s tracking accuracy is severely compromised by time-varying friction, center-of-gravity (CoG) shifts, and load changes induced by the user. In a previous study, we proposed a digital acceleration controller with online inertial parameter identification. However, the tracking accuracy was still affected by CoG shifts introduced by the users. To address these issues, the current study investigated a novel dynamic model, wherein all the load and CoG information processed in the inertial matrix was derived and a new digital acceleration controller with parameter estimation was used to compensate for the time-varying friction, CoG shifts, and load changes. Experiments were conducted under different floor and load conditions to demonstrate the improved tracking accuracy of the proposed control method.     

Accurate dynamic modeling and control parameters design of an industrial hybrid spray-painting robot

To obtain higher performance for hybrid robots subject to nonlinear dynamics and friction, feedforward compensations have been ubiquitously utilized in the industrial robotic field to attenuate these disturbances. However, due to the complex friction model and the coupling and time-varying dynamic of hybrid robots, there is no effective approach to realize accurate feedforward compensations in industrial control systems. This paper investigated an accurate dynamic modeling and control parameters design method to address these issues all at once. Taking the friction of each joint into account, the accurate dynamic model of the hybrid robot is developed and verified by experiments. With the accurate dynamic model, an exact control parameter design method is proposed based on the mapping relationship between the dynamic model and the feedforward compensations. Additionally, the control system designed by the method proposed in this paper is compared with the one tuned by an experienced engineer. Particularly, the robot's position and motion accuracy are also tested by a third-party inspection agency. The experimental and test results show that the position and velocity accuracy of the robot is improved significantly when the control system is designed by using the method proposed in this paper, which proves the effectiveness of the proposed method.     

Modelling the dynamics of industrial robots for milling operations

Using industrial robots as machine tools is targeted by many industries for their lower cost and larger workspace. Nevertheless, performance of industrial robots is limited due to their mechanical structure involving rotational joints with a lower stiffness. As a consequence, vibration instabilities, known as chatter, are more likely to appear in industrial robots than in conventional machine tools. Commonly, chatter is avoided by using stability lobe diagrams to determine the stable combinations of axial depth of cut and spindle speed. Although the computation of stability lobes in conventional machine tools is a well-studied subject, developing them in robotic milling is challenging because of the lack of accurate multi-body dynamics models involving joint compliance able of predicting the posture-dependent dynamics of the robot. In this paper, two multi-body dynamics models of articulated industrial robots suitable for machining applications are presented. The link and rotor inertias along with the joint stiffness and damping parameters of the developed models are identified using a combination of multiple-input multiple-output identification approach, computer-aided design model of the robot, and experimental modal analysis. The performance of the developed models in predicting posture-dependent dynamics of a KUKA KR90 R3100 robotic arm is studied experimentally.     

A modeling approach for robotic catheters: effects of nonlinear internal device friction

Recently, robotic surgery systems using passive flexible catheters have been developed for minimally invasive surgical applications – such as in the treatment of atrial fibrillation – where catheter control in the open chamber of the heart is required. The soft, atraumatic construction of these devices help reduce injury to delicate cardiac structures while providing a means of tool placement and control. To provide kinematic and control relationships, various models of continuous catheters have been developed. However, these approaches cannot explain the nonlinear behavior of the catheter when the effect of internal friction is considered. In this paper, we describe a lumped-parameter modeling approach which directly accounts for the effects of internal device friction. The nonlinear model is validated against experimental results from a prototype robotic catheter and is shown to correctly predict the variations in curvature and path-dependent instantaneous behavior observed. Finally, the validated model is used to investigate and describe a set of non-ideal catheter motions observed in practice.     

Control and system identification for the Berkeley lower extremity exoskeleton (BLEEX)

The Berkeley lower extremity exoskeleton (BLEEX) is an autonomous robotic device whose function is to increase the strength and endurance of a human pilot. In order to achieve an exoskeleton controller which reacts compliantly to external forces, an accurate model of the dynamics of the system is required. In this report, a series of system identification experiments was designed and carried out for BLEEX. As well as determining the mass and inertia properties of the segments of the legs, various non-ideal elements, such as friction, stiffness and damping forces, are identified. The resulting dynamic model is found to be significantly more accurate than the original model predicted from the designs of the robot.     

Geometric calibration of industrial robots using enhanced partial pose measurements and design of experiments

The paper deals with geometric calibration of industrial robots and focuses on reduction of the measurement noise impact by means of proper selection of the manipulator configurations in calibration experiments. Particular attention is paid to the enhancement of measurement and optimization techniques employed in geometric parameter identification. The developed method implements a complete and irreducible geometric model for serial manipulator, which takes into account different sources of errors (link lengths, joint offsets, etc). In contrast to other works, a new industry-oriented performance measure is proposed for optimal measurement configuration selection that improves the existing techniques via using the direct measurement data only. This new approach is aimed at finding the calibration configurations that ensure the best robot positioning accuracy after geometric error compensation. Experimental study of heavy industrial robot KUKA KR-270 illustrates the benefits of the developed pose strategy technique and the corresponding accuracy improvement.