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

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

Hierarchical control of complex manufacturing processes

The need for changing the control objective during the process has been reported in many systems in manufacturing, robotics, etc. However, not many works have been devoted to systematically investigating the proper strategies for these types of problems. In this dissertation, two approaches to such problems have been suggested for fast varying systems. The first approach, addresses problems where some of the objectives are statically related to the states of the systems. Hierarchical Optimal Control was proposed to simplify the nonlinearity caused by adding the statically related objectives into control problem. The proposed method was implemented for contour-position control of motion systems as well as force-position control of end milling processes. It was shown for a motion control system, when contour tracking is important, the controller can reduce the contour error even when the axial control signals are saturating. Also, for end milling processes it was shown that during machining sharp edges where, excessive cutting forces can cause tool breakage, by using the proposed controller, force can be bounded without sacrificing the position tracking performance. The second approach that was proposed (Hierarchical Model Predictive Control), addressed the problems where all the objectives are dynamically related. In this method neural network approximation methods were used to convert a nonlinear optimization problem into an explicit form which is feasible for real time implementation. This method was implemented for force-velocity control of ram based freeform extrusion fabrication of ceramics. Excellent extrusion results were achieved with the proposed method showing excellent performance for different changes in control objective during the process --Abstract, page iv

Neural network contour error prediction of a bi-axial linear motor positioning system

In the article a method of predicting contour error using artificial neural network for a bi-axial positioning system is presented. The machine consists of two linear stages with permanent magnet linear motors controlled by servo drives. The drives are controlled from a PC with real-time operating system via EtherCAT fieldbus. A randomly generated Non-Uniform Rational B-Spline (NURBS) trajectory is used to train offline a NARX-type artificial neural network for each axis. These networks allow prediction of following errors and contour errors of the motion trajectory. Experimental results are presented that validate the viability of the neural network based contour error prediction. The presented contour error predictor will be used in predictive control and velocity optimization algorithms of linear motor based CNC machines

Model Predictive Contouring Control for Vehicle Obstacle Avoidance at the Limit of Handling

This paper proposes a non-linear Model Predictive Contouring Control (MPCC) for obstacle avoidance in automated vehicles driven at the limit of handling. The proposed controller integrates motion planning, path tracking and vehicle stability objectives, prioritising obstacle avoidance in emergencies. The controller's prediction model is a non-linear single-track vehicle model with the Fiala tyre to capture the vehicle's non-linear behaviour. The MPCC computes the optimal steering angle and brake torques to minimise tracking error in safe situations and maximise the vehicle-to-obstacle distance in emergencies. Furthermore, the MPCC is extended with the tyre friction circle to fully exploit the vehicle's manoeuvrability and stability. The MPCC controller is tested using real-time rapid prototyping hardware to prove its real-time capability. The performance is compared with a state-of-the-art Model Predictive Control (MPC) in a high-fidelity simulation environment. The double lane change scenario results demonstrate a significant improvement in successfully avoiding obstacles and maintaining vehicle stability.Comment: Accepted to the 28th IAVSD International Symposium on Dynamics of Vehicles on Roads and Track