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

Design of cross-coupled CMAC for contour-following – a reinforcement-based ILC approach

One of the most popular applications of a bi-axial motion stage is precision motion control. The reduction of tracking error and contour error is one of the most coveted goals in precision motion control systems. The accuracy of a motion control system is often affected by external disturbances. In addition, system non-linearity such as friction also represents a major hurdle to motion precision. In order to deal with the aforementioned problem, this paper proposes a fuzzy logic-based Reinforcement Iterative Learning Control (RILC) and a Cross-Coupled Cerebellar Model Articulation Controller (CCCMAC). In particular, the proposed fuzzy logicbased RILC and a LuGre friction model-based compensation approach are exploited to improve motion accuracy. The fuzzy logic-based RILC aims at reducing tracking error and compensating for external disturbance, while the LuGre friction model is responsible for friction compensation. In addition, the CCCMAC consisting of a cerebellar model articulation controller and a cross-coupled controller aims at reducing contour error and dealing with the problem of dynamics mismatch between different axes. Performance comparisons between the proposed fuzzy logic-based Reinforcement Iterative Learning Cross-Coupled Cerebellar Model Articulation Controller (RIL–CCCMAC) and several existing control schemes are conducted on a bi-axial motion stage. Experimental results verify the effectiveness of the proposed RIL–CCCMAC

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

Development of a modular control algorithm for high precision positioning systems

Ankara : The Department of Mechanical Engineering and the Graduate School of Engineering and Science of Bilkent University, 2012.Thesis (Master's) -- Bilkent University, 2012.Includes bibliographical references.In the last decade, micro/nano-technology has been improved significantly. Micro/nano-technology related products started to be used in consumer market in addition to their applications in the science and technology world. These developments resulted in a growing interest for high precision positioning systems since precision positioning is crucial for micro/nano-technology related applications. With the rise of more complex and advanced applications requiring smaller parts and higher precision performance, demand for new control techniques that can meet these expectations is increased. The goal of this work is developing a new control technique that can meet increased expectations of precision positioning systems. For this purpose, control of a modular multi-axis positioning system is studied in this thesis. The multiaxis precision positioning system is constructed by assembling modular single-axis stages. Therefore, a single-axis stage can be used in several configurations. Model parameters of a single-axis stage change depending on which axis it is used for. For this purpose, an iterative learning controller is designed to improve tracking performance of a modular single-axis stage to help modular sliders adapting to repeated disturbances and nonlinearities of the axis they are used for. When modular single-axis stages are assembled to form multi-axis systems, the interaction between the axes should be considered to operate stages simultaneously. In order to compensate for these interactions, a multi input multi output (MIMO) controller can be used such as cross-coupled controller (CCC). Cross-coupled controller examines the effects between axes by controlling the contour error resulting in an improved contour tracking. In this thesis, a controller featuring cross-coupled control and iterative learning control schemes is presented to improve contour and tracking accuracy at the same time. Instead of using the standard contour estimation technique proposed with the variable gain cross-coupled control, presented control design incorporates a computationally efficient contour estimation technique. In addition to that, implemented contour estimation technique makes the presented control scheme more suitable for arbitrary nonlinear contours and multi-axis systems. Also, using the zero-phase filtering based iterative learning control results in a practical design and an increased applicability to modular systems. Stability and convergence of the proposed controller has been shown with the necessary theoretical analysis. Effectiveness of the control design is verified with simulations and experiments on two-axis and three-axis positioning systems. The resulting controller is shown to achieve nanometer level contouring and tracking performance.Ulu, Nurcan GeçerM.S

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

Learning based cross-coupled control for multi-axis high precision positioning systems

In this paper, a controller featuring cross-coupled control and iterative learning control schemes is designed and implemented on a modular two-axis positioning system in order to improve both contour and tracking accuracy. Instead of using the standard contour estimation technique proposed with the variable gain cross-coupled control, a computationally efficient contour estimation technique is incorporated with the presented control design. Moreover, implemented contour estimation technique makes the presented control scheme more suitable for arbitrary nonlinear contours. Effectiveness of the control design is verified with simulations and experiments on a two-axis positioning system. Also, simulations demonstrating the performance of the control method on a three-axis positioning system are provided. The resulting controller is shown to achieve nanometer level contouring and tracking performance. Simulation results also show its applicability to three-axis nano-positioning systems. Copyright © 2012 by ASME

Improving Contouring Accuracy in CNC Machines

Ph.DDOCTOR OF PHILOSOPH