Adaptive Sliding Mode Contouring Control Design Based on Reference Adjustment and Uncertainty Compensation for Feed Drive Systems

Industrial feed drive systems, particularly, ball-screw and lead-screw feed drives are among the dominating motion components in production and manufacturing industries. They operate around the clock at high speeds for coping with the rising production demands. Adversely, high-speed motions cause mechanical vibrations, high-energy consumption, and insufficient accuracy. Although there are many control strategies in the literature, such as sliding mode and model predictive controls, further research is necessary for precision enhancement and energy saving. This study focused on design of an adaptive sliding mode contouring control based on reference adjustment and uncertainty compensation for feed drive systems. A combined reference adjustment and uncertainty compensator for precision motion of industrial feed drive systems were designed. For feasibility of the approach, simulation using matlab was conducted, and results are compared with those of an adaptive nonlinear sliding model contouring controller. The addition of uncertainty compensator showed a substantial improvement in performance by reducing the average contour error by 85.71% and the maximum contouring error by 78.64% under low speed compared to the adaptive sliding mode contouring controller with reference adjustment. Under high speed, the addition of uncertainty compensator reduced the average and absolute maximum contour errors by 4.48% and 10.13%, respectively. The experimental verification will be done in future. Keywords:    Machine tools, Feed drive systems, contouring control, Uncertainty dynamics, Sliding mode control

Reference trajectory modification based on spatial iterative learning for contour control of 2-axis NC systems

Contour error is a main factor that affects the quality of products in numerical control (NC) machining. This paper presents a contour control strategy based on digital curves for high-precision control of computer numerical control (CNC) machines. A contour error estimation algorithm is presented for digital curves based on a geometrical method. The dynamic model of the motion control system is transformed from time domain to space domain because the contour error is dependent on space instead of time. Spatial iterative learning control (sILC) is developed to reduce the contour error, by modifying the reference trajectory in the form of G code. This allows system improvement without interference of low-level controllers so it is applicable to many commercial controllers where interpolators and feed-drive controllers cannot be altered. The effectiveness of this method is verified by experiments on a NC machine, which have shown good performance not only for smooth trajectories but also for large curvature trajectories

Neural Network Contour Error Predictor in CNC Control Systems

Paper presented as poster presentation at MMAR 2016 conference (Międzyzdroje,Poland, 29 Aug.-1 Sept. 2016)This article presents a method for predicting contour error using artificial neural networks. Contour error is defined as the minimum distance between actual position and reference toolpath and is commonly used to measure machining precision of Computerized Numerically Controlled (CNC) machine tools. Offline trained Nonlinear Autoregressive networks with exogenous inputs (NARX) are used to predict following error in each axis. These values and information about toolpath geometry obtained from the interpolator are then used to compute the contour error. The method used for effective off-line training of the dynamic recurrent NARX neural networks is presented. Tests are performed that verify the contour error prediction accuracy using a biaxial CNC machine in a real-time CNC control system. The presented neural network based contour error predictor was used in a predictive feedrate optimization algorithm with constrained contour error

Sliding Mode Contouring Control for Biaxial Feed Drive Systems with a Nonlinear Sliding Surface

Control input variance is one of the important criteria in machining because it affects the surface roughness, machining precisions and consumed energy. This paper presents a nonlinear controller design for biaxial feed drive systems for reducing the control input variance while maintaining the motion accuracy. The contour error, which is defined as the error component orthogonal to the desired contour curve, is considered to design the controller because it directly affects the precision of machined work-piece profile. The proposed nonlinear controller allows to adjust a controller gain from its low value to high value as the contour error changes from low value to high value and vice versa, and hence a closed-loop system simultaneously achieves low overshoot and settling time, resulting in a smaller error. In order to design the variable controller gain, a sliding mode control based on a nonlinear sliding surface is employed. Experimental results demonstrate a significant performance improvement in terms of control input variance while maintaining the motion accuracy

Contour error compensation based on feed rate adjustment

To improve the performance of computer numerical control (CNC) machining, especially for large-curvature trajectories, this paper presents a contour error compensation algorithm based on reference trajectory modification. In order to estimate the contour error accurately and efficiently, a contour error estimation model is established. The reference trajectory is modified on the basis of the estimated contour error and partitioned into different segments, which adopt different feed rates according to a corner detection algorithm. The effectiveness of this contour error compensation algorithm is verified by experiments on a CNC machine tool

Contouring Accuracy Improvement Using an Adaptive Feedrate Planning Method for CNC Machine Tools

AbstractThe reduction of contour error plays an important role in achieving high accuracy machining. To reduce contour error, most of previous studies have focused on developing advanced control strategies. As an alternative strategy, contouring accuracy improvement using an adaptive feedrate planning method is proposed in this paper. First, a typical PID controller is adopted to build the contour error model, from which the feedrate can be scheduled in the contour error violated zones. Then, the relations between each constraint and the cutter tip feedrate are derived. After that, a linear programming model is applied to obtain the optimal feedrate profile on the sampling positions of the given tool path. Finally, illustrated examples are given to validate the feasibility and applicability of the proposed feedrate planning method. The comparison results show that the proposed method has a significant effect on improving contouring accuracy

Prediction and compensation of contour error of CNC systems based on LSTM neural-network

This paper proposes a contour error estimation and compensation method for computer numerical control (CNC) systems based on the long short-term memory neural network (LSTM-NN). This is achieved by performing modeling of each axis to predict the tracking error, calculating the actual trajectory, estimating the contour error, and modifying the reference trajectory. First, linear feature selection based on a simplified single-axis model and nonlinear feature selection based on a circular test are performed to achieve tracking error prediction. Then, a spline-approximation-based contour error estimation method is proposed to estimate the contour error between the reference trajectory and the predicted trajectory. Finally, contour error compensation is performed on the reference trajectory before it is run on CNC systems. The proposed method is validated through experiments on a three-axis CNC system

Modeling and Contour Control of Multi-Axis Linear Driven Machine Tools

In modern manufacturing industries, many applications require precision motion control of multi-agent systems, like multi-joint robot arms and multi-axis machine tools. Cutter (end effector) should stay as close as possible to the reference trajectory to ensure the quality of the final products. In conventional computer numerical control (CNC), the control unit of each axis is independently designed to achieve the best individual tracking performance. However, this becomes less effective when dealing with multi-axis contour following tasks because of the lack of coordination among axes. This dissertation studies the control of multi-axis machine tools with focus on reducing the contour error. The proposed research explicitly addresses the minimization of contour error and treats the multi-axis machine tool as a multi-input-multi-output (MIMO) system instead of several decoupled single-input-single-output (SISO) systems. New control schemes are developed to achieve superior contour following performance even in the presence of disturbances. This study also extends the applications of the proposed control system from plane contours to regular contours in R3. The effectiveness of the developed control systems is experimentally verified on a micro milling machine