Friction Compensation Method Research Articles

A Novel Friction Compensation Method for Machine Tool Drive Systems in Insufficient Lubrication.

Friction is the dominant factor restricting tracking accuracy and machining surface quality in mechanical systems such as machine tool feed-drive. Hence, friction modeling and compensation is an important method in accurate tracking control of CNC machine tools used for welding, 3D printing, and milling, etc. Many static and dynamic friction models have been proposed to compensate for frictional effects to reduce the tracking error in the desired trajectory and to improve the surface quality. However, most of them focus on the friction characteristics of the pre-sliding zone and low-speed sliding regions. These models do not fully describe friction in the case of insufficient lubrication or high acceleration and deceleration in machine tool systems. This paper presents a new nonlinear friction model that includes the typical Coulomb-Viscous friction, a nonlinear periodic harmonic friction term for describing the lead screw property in insufficient lubrication, and a functional component of acceleration for describing the friction lag caused by the acceleration and deceleration of the system. Experiments were conducted to compare the friction compensation performance between the proposed and the conventional friction models. Experimental results indicate that the root mean square and maximum absolute tracking error can be significantly reduced after applying the proposed friction model.

LuGre model–based robust adaptive control for a pump-controlled hydraulic actuator experiencing friction

In this paper, a LuGre model–based robust adaptive control (RAC) approach is presented for a pump-controlled hydraulic actuator. We first decompose the LuGre friction model into its steady-state model and a lumped dynamic part applying the mean value theorem, which are compensated by a feedforward term and a robust adaptive term, respectively. The robust adaptive term also plays a part in mismatched disturbance attenuation. In addition, parametric uncertainties and matched disturbances are handled by σ-modified adaptation laws and a robust control law, respectively. The stability of the closed-loop system is proved via the Lyapunov analysis. The efficacy and robustness of the proposed approach are validated by comparative experiments. Compared with common adaptive friction compensation methods, the proposed method has a simpler structure, less computational burden, better control performance, and stronger robustness. Moreover, since the available information is separated from the LuGre model and acts as a model-based compensation term, the design conservativeness of RAC is effectively reduced.

A Novel Friction Compensation Method Based on Stribeck Model With Fuzzy Filter for PMSM Servo Systems

Friction deteriorates speed and position control accuracy of high-precision machine tools and causes distortion phenomenon when the speed crosses zero. In this paper, a new compensation method based on static Stribeck model (SB) combined with fuzzy low-pass filter (SBFF) is studied for friction compensation of permanent magnet synchronous motor (PMSM) servo systems. First, models of the servo system, the Stribeck, and the LuGre are established. Then, it is theoretically derived that the hysteresis effect of the LuGre model (LG) can be equivalent to a low-pass filter term compared to the SB. Based on this, the relationship between the filter time constant and the motor reference speed can be derived. To further improve the friction compensation effect near zero speed and to weaken the delay generated by the filter during the non-zero-speed range, fuzzy logic is introduced to adjust the filter time constant according to the reference speed variation. Through simulations and experiments, compensation strategies based on the LG feedforward, SB feedforward, SBFF feedforward and SBFF feedback are compared, and the robustness of the proposed SBFF feedforward compensation method over entire speed range is verified as well.

Research on Friction Compensation Method of Electromechanical Actuator Based on Improved Active Disturbance Rejection Control

The friction factor of harmonic reducers affects the transmission accuracy in electromechanical actuators (EMAs). In this study, we proposed a friction feedforward compensation method based on improved active disturbance rejection control (IADRC). A mathematical model of EMA was developed. The relationship between friction torque and torque current was derived. Furthermore, the compound ADRC control method of second-order speed loop and position loop was studied, and an IADRC control method was proposed. A real EMA was developed, and the working principles of the EMA driving circuit and current sampling were analyzed. The three methods—PI, ADRC, and IADRC—were verified by conducting speed step experiments and sinusoidal tracking experiments. The integral values of time multiplied by the absolute error of the three control modes under the step speed mode were approximately 47.7, 32.1, and 15.5, respectively. Disregarding the inertia of the reducer and assuming that the torque during no-load operation equals the friction torque during constant motion, the findings indicate that, under a load purely driven by inertia, the IADRC control method enhances tracking accuracy.

A nonlinear robot joint friction compensation method including stick and sliding characteristics

PurposeDue to dynamic model is the basis of realizing various robot control functions, and it determines the robot control performance to a large extent, this paper aims to improve the accuracy of dynamic model for n-Degree of Freedom (DOF) serial robot.Design/methodology/approachThis paper exploits a combination of the link dynamical system and the friction model to create robot dynamic behaviors. A practical approach to identify the nonlinear joint friction parameters including the slip properties in sliding phase and the stick characteristics in presliding phase is presented. Afterward, an adaptive variable-step moving average method is proposed to effectively reduce the noise impact on the collected data. Furthermore, a radial basis function neural network-based friction estimator for varying loads is trained to compensate the nonlinear effects of load on friction during robot joint moving.FindingsExperiment validations are carried out on all the joints of a 6-DOF industrial robot. The experimental results of joint torque estimation demonstrate that the proposed strategy significantly improves the accuracy of the robot dynamic model, and the prediction effect of the proposed method is better than that of existing methods.Originality/valueThe proposed method extends the robot dynamic model with friction compensation, which includes the nonlinear effects of joint stick motion, joint sliding motion and load attached to the end-effector.

A feedforward compensation approach for cable-driven musculoskeletal systems

AbstractThis paper presents a feedforward compensation approach for musculoskeletal systems (MSs). Compared with traditional rigid robots, human arms have the advantages of flexibility and safety in operation in unstructured environments. However, the influence of external unknown disturbances, inner friction effects, and dynamic uncertainties of the MS makes it difficult to model and practically apply. In order to reduce the inner friction effects of the hardware platform and the over-relaxation/tension of the cable-pull drive, a feedforward friction compensation method for the cable-pulled artificial muscle unit is proposed. The method analyzes the friction causes of the hardware structure and establishes a mapping network relationship between the joint variables and the muscle force error in the muscle space. The experimental results show that the method can effectively improve the control accuracy and reduce the artificial muscle over-relaxation/tension instability.

Non-model friction disturbance compensation for an inertially stabilized platform based on type-2 fuzzy control with self-adjusting correction factor

To decrease the effect of nonlinear friction disturbance on the control accuracy of the inertially stabilized platform (ISP) for aerial remote sensing applications, a non-model friction compensation method based on type-2 fuzzy control (T-2 FC) is proposed. To improve the controller’s adaptability, an online complex self-adjusting correction factor method is designed, in which both the fuzzy regulator and the improved integral time absolute error method are combined to adjust the weighting factor and the quantitative factor of the T-2 FC online, respectively. In this way, the nonlinear friction disturbance error is compensated in real-time to obtain high system control accuracy. The experimental results show that compared with traditional PID control and the single T-2 FC, the proposed T-2 FC with self-adjusting correction factor has better disturbance rejection capability to the nonlinear friction disturbance. The ISP has seen a tremendous improvement in both stability and control precision.

Friction compensation for an m-Level telescope based on high-precision LuGre parameters identification

Friction torque severely weakens the tracking accuracy and low-speed stability of an m-level TCS (telescope control system). To solve this problem, a friction compensation method is proposed, based on high-precision LuGre friction model parameters identification. Together with dynamometer calibration, we first design a DOB (disturbance observer) to acquire high-accuracy TCS friction value in real time. Then, the PSO-GA (a hybrid algorithm combined particle swarm optimization algorithm and genetic algorithm) optimization algorithm proposed effectively and efficiently realizes the LuGre model parameters identification. In addition, we design a TCS controller including DOB and LuGre model parameters identification based on double-loop PID controller for practical application. Engineering verification tests indicate that the accuracy of DOB calibrated can reach 96.94%of the real measured friction.When azimuth axis operates in the speed cross-zero work mode, the average positive peak to tracking error reduces from 0.8926″ to 0.2252″ and the absolute average negative peak to tracking error reduces from 0.8881″ to 0.3984″. Moreover, the azimuth axis tracking MSE reduces from 0.1155″ to 0.0737″, which decreases by 36.2%. Experimental results validate the high precision, facile portability and high real-time ability of our approach.

Research on Adaptive Friction Compensation of Digital Hydraulic Cylinder Based on LuGre Friction Model

This paper aims to eliminate nonlinear friction from the performance of the digital hydraulic cylinder to enable it to have good adaptive ability. First, a mathematical model of a digital hydraulic cylinder based on the LuGre friction model was established, and then a dual‐observer structure was designed to estimate the unobservable state variables in the friction model. The Lyapunov method is used to prove the global asymptotic stability of the closed‐loop system using the adaptive friction compensation method. Finally, Simulink is used to simulate the system performance. The simulation results indicate that the addition of adaptive friction compensation control can effectively reduce system static error, suppress system limit loop oscillation, “position decapitation,” “speed dead zone,” and low‐speed creep phenomena, and improve the overall performance of the digital hydraulic cylinder. The control method has practical application value for improving the performance index of the digital hydraulic cylinder.

Nonlinear identification and optimal feedforward friction compensation for a motion platform

In this study, we present a method of nonlinear identification and optimal feedforward friction compensation for an industrial single degree of freedom motion platform. The platform has precise reference tracking requirements while suffering from nonlinear dynamic effects, such as friction and backlash in the driveline. To eliminate nonlinear dynamic effects and achieve precise reference tracking, we first identified the nonlinear dynamics of the platform using Higher Order Sinusoidal Input Describing Function (HOSIDF) based system identification. Next, we present optimal feedforward compensation design to improve reference tracking performance. We modeled the friction using the Stribeck model and identified its parameters through a procedure including a special reference signal and the Nelder–Mead algorithm. Our results show that the RMS trajectory tracking error decreased from 0.0431 deg/s to 0.0117 deg/s when the proposed nonlinear identification and friction compensation method is utilized.

An ILC approach to feed-forward friction compensation

An iterative, learning based, feed-forward method for compensation of friction in industrial robots is studied. The method is put into an ILC framework by using a two step procedure proposed in literature. The friction compensation method is based on a black-box friction model which is learned from operational data, and this can be seen as the first step in the method. In the second step the learned model is used for compensation of the friction using the reference joint velocity as input. The approach is supported by simulation experiments.

An Angular Position-Based Two-Stage Friction Modeling and Compensation Method for RV Transmission System

An Angular Position-Based Two-Stage Friction Modeling and Compensation Method for RV Transmission System

Performance analysis of flexible intelligent hand prosthesis

The study of flexible intelligent hand prosthesis has important practical significance for handicapped people. Aiming at the grasping force control of flexible hand prosthesis, this paper draws lessons from the anatomical structure design of human hand, and proposes a fingertip grasping force control method based on linear tension feedback. The control strategy can realize the stable control of fingertip grasping force. The static mechanical model of finger is established, and the mathematical relationship of fingertip contact force calculation in static state is obtained. Then a friction compensation method is proposed. The experimental results show that the fitting value of the friction moment of the lasso matches well with the test value, and the error after friction compensation is obviously reduced.

Indirect Force Control of a Cable-Driven Parallel Robot: Tension Estimation using Artificial Neural Network trained by Force Sensor Measurements.

In a cable-driven parallel robot (CDPR), force sensors are utilized at each winch motor to measure the cable tension in order to obtain the force distribution at the robot end-effector. However, because of the effects of friction in the pulleys and the unmodeled cable properties of the robot, the measured cable tensions are often inaccurate, which causes force-control difficulties. To overcome this issue, this paper presents an artificial neural network (ANN)-based indirect end-effector force-estimation method, and its application to CDPR force control. The pulley friction and other unmodeled effects are considered as black-box uncertainties, and the tension at the end-effector is estimated by compensating for these uncertainties using an ANN that is developed using the training datasets from CDPR experiments. The estimated cable tensions at the end-effector are used to design a P-controller to track the desired force. The performance of the proposed ANN model is verified through comparisons with the forces measured directly at the end-effector. Furthermore, cable force control is implemented based on the compensated tensions to evaluate the performance of the CDPR in wrench space. The experimental results show that the proposed friction-compensation method is suitable for application in CDPRs to control the cable force.

Significant influence of nonlinear friction torque on motion performance of tracking turntables

Abstract Friction compensation methods are widely adopted to improve the motion performance of tracking turntables. However, friction behavior is inherently nonlinear in nature and cannot be fully described by a parametric model, which limits the further precision improvements. Here, the motion performance fluctuations caused by friction model errors was simulated and demonstrated based on the Column-Viscous model. Then, an experimental investigation under three different friction states was performed on an established two-axis tracking turntable by applying external nonlinear friction torque on the azimuth axis. The simulation and experimental results reveal the significance of friction nonlinearity on motion performance. Meanwhile, this study provides a ground simulation experimental scheme for space applications characterized by strong nonlinearity of friction under the gravity release state.

Motion control applications: observer based DC motor parameters estimation for novices

<span>Estimation of motor inertia and friction components is a complex and challenging task in motion control applications where small size DC motors (<100W) are used for precise control. It is essential to estimate the accurate friction components and motor inertia, because the parameters provided by the manufacturer are not always accurate. This research proposes a Sensorless method of determining DC motor parameters, including moment of inertia, torque coefficient and frictional components using the Disturbance Observer (DOB) as a torque sensor. The constant velocity motion test and a novel Reverse Motion Acceleration test were conducted to estimate frictional components and moment of inertia of the motor. The validity of the proposed novel method was verified by experimental results and compared with conventional acceleration and deceleration motion tests. Experiments have been carried out to show the effectiveness and viability of the estimated parameters using a Reaction Torque Observer (RTOB) based friction compensation method.</span>

Friction Compensation in the Inverted Pendulum Controller by Means of a Neural Network

Abstract This paper presents an experimental confirmation of the novel method of friction modelling and compensation. The method has been applied to an inverted pendulum control system. The practical procedure of data acquisition and processing has been described. Training of the neural network friction model has been covered. Application of the obtained model has been presented. The main asset of the presented model is its correctness in a wide range of relative velocities. Moreover, the model is relatively easy to build.

Friction compensation and observer-based adaptive sliding mode control of electromechanical actuator

The performance of the electromechanical actuator system is usually affected by the nonlinear friction torque disturbance, model uncertainty, and unknown disturbances. In order to solve this problem, a model-based friction compensation method combined with an observer-based adaptive sliding mode controller for the speed loop of electromechanical actuator system is presented in this article. All the disturbances and model uncertainty of electromechanical actuator system are divided into two parts. One is model-based friction torque disturbance which can be identified by experiments, and the other is the residual disturbance which cannot be identified by experiments. A modified LuGre model is adopted to describe the friction torque disturbance of electromechanical actuator system. An extended state observer is designed to estimate the residual disturbance. An adaptive sliding mode controller is designed to control the system and compensate the friction torque disturbance and the residual disturbance. The stability of the electromechanical actuator system is discussed with Lyapunov stability theory and Barbalat’s lemma. Experiments are designed to validate the proposed method. The results demonstrate that the proposed control strategy not only provides better disturbance rejecting ability but also provides better steady state and dynamic performance.

A Learning Approach for Feed-Forward Friction Compensation

An experimental comparison of two feed-forward based frictioncompensation methods is presented. The first method is based on theLuGre friction model, using identified friction model parameters, and ...

A novel approach for mitigating the effects of pre-rolling/pre-sliding friction on the settling time of rolling bearing nanopositioning stages using high frequency vibration

Nanopositioning stages are used for ultra-precise positioning in many different applications. Among the bearing options available for long-range nanopositioning stages, rolling bearings are the most cost effective. However, rolling bearing nanopositioning (RB-NP) stages are well known to take a long time to reach their target positions in point-to-point positioning tasks due to pre-rolling/pre-sliding friction. This paper presents vibration assisted nanopositioning (VAN) – a novel approach for reducing the settling time of RB-NP stages using high frequency vibration (aka dither). VAN synergistically combines the electromechanical design and control of RB-NP stages to mitigate pre-rolling/pre-sliding friction using dither, without jeopardizing positioning precision. It is shown in simulations to exhibit superior positioning performance and robustness compared to model-based friction compensation methods. Experiments carried out on a prototype stage incorporating the VAN approach show a 52% reduction of mean settling time, based on 50 trials, when compared to a case without VAN. Moreover, the heat and potential wear induced by dither are shown to be practically insignificant.