工业机器人的动态和虚构参数的识别、比较以及重复分析外文翻译资料

Dynamic and Friction Parameters of an Industrial Robot: Identification, Comparison and Repetitiveness Analysis

Abstract:

This paper describes the results of dynamic tests performed to study the robustness of a dynamics model of an industrial manipulator. The tests show that the joint friction changes during the robot operation. The variation can be identified in a double exponential law and thus the variation can be predicted. The variation is due to the heat generated by the friction. A model is used to estimate the temperature and related friction variation. Experimental data collected on two robots EFORT ER3A-C60 are presented and discussed. Repetitive tests performed on different days showed that the inertial and friction parameters can be robustly estimated and that the value of the measured joint friction can be used to estimate the unexpected conditions of the joints. Future applications may include sensorless identification of collisions, predictive maintenance programs, or human–robot interaction.

Keywords: robotics; modelling; identification; friction; temperature

1. Introduction

The study of the dynamical performance of robotics machines is one of the most relevant problems in the design of effective robot control strategies. Parameter identification allows obtaining reliable dynamic models of industrial manipulators [1]. It is worth pointing out that a reliable dynamic model can improve position control [2]. Other applications may concern predictive maintenance, trajectory planning for energy efficiency [3], safety systems for robotic cells [4], or management of robot interaction with humans or the environment [5,6]. Many of these applications may require the knowledge of a good dynamic model of the robot. The robotrsquo;s dynamical parameters include inertial parameters (masses, centers of mass, and moments of inertia) and friction description. Joints and transmissions are considered rigid, which is acceptable for many industrial applications. The inertial parameters can be obtained by developing a model and by performing experiments on the robot. The authors of [7–9] showed that it is possible to define a set of parameters that describe the model (torque prediction) for any open-chain robot. The model is linear on these parameters and their regression matrix depends on the position, velocity, and acceleration of the joints. M. Gautier and W. Khalil proposed to drop the parameters that scarcely influence then fitting, obtaining a full-rank matrix. The remaining dynamical parameters involved in the identification are called the base parameters.

The identification process uses the Least Square Method, which provides reliable performance when models are linear in the parameters. During the test, the robot is moved on a properly designed excitation trajectory storing the position, velocity, acceleration, and torque of the joints. Then, the parameters that better predict the measured torques are estimated. Several criteria have been proposed to optimize the trajectory [10,11]. In this paper, the excitation trajectory is designed based on the finite Fourier series introduced by J. Swevers [12], with the benefit of data averaging in the time domain and reducing the measurement noise effect. An additional trajectory is appended to the excitation movement to guarantee the reaching of the joint-velocity bounds.

The linearity in the parameters does not hold in long time intervals, because the friction might change due to thermal effects. This model mismatch is evident comparing two experiments, one executed at the robot startup and one after the thermal transient. Friction is one of the most relevant undesired phenomena to be taken into account to realize a reliable dynamic model. It arises when there is a relative motion between two surfaces in contact or when a body moves inside a fluid. Friction depends on the geometry and the roughness of the surfaces, their materials, the type of lubricant, the velocity, the humidity, and so on [13]. Friction causes the loss of energy, heating of the surfaces in contact. This phenomenon introduces nonlinearities that might cause several problems related to motion control, such as trajectory tracking errors, limit cycles, and dynamic instabilities (stick-slip). For this reason, many friction models have been proposed in the technical literature (see [14]) to provide a suitable description of the physical phenomenon and to obtain effective mathematical formulations. These models can be classified into two main categories: static models and dynamic models.

The first category is simpler to analyze and concerns the models in which the friction force is a static function of the velocity. The most known static models are the Coulomb model, in which the friction action is considered constant and dependent only on the direction of the velocity, and the viscous friction, in which the force is proportional to velocity. Other models that belong to this category are the Stribeck model [15–17], in which an exponential function represents the transition between Coulomb and viscous friction, or the polynomial model, in which a polynomial expression is used to describe the relationship between friction force and velocity.

Regarding the dynamic models, on the other hand, the friction force depends on a state variable that takes into account the history of the system and not only its current situation. Relevant examples are the Dahl model [18], the Maxwell-slip model [19] and its extensions [20–22]. and the LuGre model [23–25]. In general, friction models are used in the control strategies to compensate for the friction effects (see [26,27]).

Despite the numerous efforts supported by research, this phenomenon is still a relevant issue for the control of industrial rob

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