Motion control and synchronisation of multi-axis drive systems

Motion control and synchronisation of multi-axis drive system

A review of dynamics design methods for high-speed and high-precision CNC machine tool feed systems

With the development of CNC machine tools toward high speed and high precision, the traditional static design methods can hardly meet the demand. Hence, in this paper, the dynamics matching design methods of existing CNC machine tool feed systems were investigated and analyzed. Further, sub-system coupling mechanisms and optimization design studies were carried out for each sub-system. First, the required kinematic indexes must be achieved when designing the feed system dynamics of high-speed, high-precision CNC machine tools. Second, the CNC machine tool feed systems generally have four sub-systems: motion process, control system, motor, and mechanical structure. The coupling effect between the sub-systems should also be considered in the design. Based on the dynamics design, each sub-system should be optimized to maximize the system dynamic performance with minimum resource allocation. Finally, based on the review, future research directions within the field were detected

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

From computer-aided to intelligent machining: Recent advances in computer numerical control machining research

The aim of this paper is to provide an introduction and overview of recent advances in the key technologies and the supporting computerized systems, and to indicate the trend of research and development in the area of computational numerical control machining. Three main themes of recent research in CNC machining are simulation, optimization and automation, which form the key aspects of intelligent manufacturing in the digital and knowledge based manufacturing era. As the information and knowledge carrier, feature is the efficacious way to achieve intelligent manufacturing. From the regular shaped feature to freeform surface feature, the feature technology has been used in manufacturing of complex parts, such as aircraft structural parts. The authors’ latest research in intelligent machining is presented through a new concept of multi-perspective dynamic feature (MpDF), for future discussion and communication with readers of this special issue. The MpDF concept has been implemented and tested in real examples from the aerospace industry, and has the potential to make promising impact on the future research in the new paradigm of intelligent machining. The authors of this paper are the guest editors of this special issue on computational numerical control machining. The guest editors have extensive and complementary experiences in both academia and industry, gained in China, USA and UK

Modeling, Analysis, and Control of a Mobile Robot for \u3ci\u3eIn Vivo\u3c/i\u3e Fluoroscopy of Human Joints during Natural Movements

In this dissertation, the modeling, analysis and control of a multi-degree of freedom (mdof) robotic fluoroscope was investigated. A prototype robotic fluoroscope exists, and consists of a 3 dof mobile platform with two 2 dof Cartesian manipulators mounted symmetrically on opposite sides of the platform. One Cartesian manipulator positions the x-ray generator and the other Cartesian manipulator positions the x-ray imaging device. The robotic fluoroscope is used to x-ray skeletal joints of interest of human subjects performing natural movement activities. In order to collect the data, the Cartesian manipulators must keep the x-ray generation and imaging devices accurately aligned while dynamically tracking the desired skeletal joint of interest. In addition to the joint tracking, this also requires the robotic platform to move along with the subject, allowing the manipulators to operate within their ranges of motion. A comprehensive dynamic model of the robotic fluoroscope prototype was created, incorporating the dynamic coupling of the system. Empirical data collected from an RGB-D camera were used to create a human kinematic model that can be used to simulate the joint of interest target dynamics. This model was incorporated into a computer simulation that was validated by comparing the simulation results with actual prototype experiments using the same human kinematic model inputs. The computer simulation was used in a comprehensive dynamic analysis of the prototype and in the development and evaluation of sensing, control, and signal processing approaches that optimize the subject and joint tracking performance characteristics. The modeling and simulation results were used to develop real-time control strategies, including decoupling techniques that reduce tracking error on the prototype. For a normal walking activity, the joint tracking error was less than 20 mm, and the subject tracking error was less than 140 mm

Flexible structure control laboratory development and technology demonstration

An experimental structure is described which was constructed to demonstrate and validate recent emerging technologies in the active control and identification of large flexible space structures. The configuration consists of a large, 20 foot diameter antenna-like flexible structure in the horizontal plane with a gimballed central hub, a flexible feed-boom assembly hanging from the hub, and 12 flexible ribs radiating outward. Fourteen electrodynamic force actuators mounted to the hub and to the individual ribs provide the means to excite the structure and exert control forces. Thirty permanently mounted sensors, including optical encoders and analog induction devices provide measurements of structural response at widely distributed points. An experimental remote optical sensor provides sixteen additional sensing channels. A computer samples the sensors, computes the control updates and sends commands to the actuators in real time, while simultaneously displaying selected outputs on a graphics terminal and saving them in memory. Several control experiments were conducted thus far and are documented. These include implementation of distributed parameter system control, model reference adaptive control, and static shape control. These experiments have demonstrated the successful implementation of state-of-the-art control approaches using actual hardware

Virtual Model Building for Multi-Axis Machine Tools Using Field Data

Accurate machine dynamic models are the foundation of many advanced machining technologies such as virtual process planning and machine condition monitoring. Viewing recent designs of modern high-performance machine tools, to enhance the machine versatility and productivity, the machine axis configuration is becoming more complex and diversified, and direct drive motors are more commonly used. Due to the above trends, coupled and nonlinear multibody dynamics in machine tools are gaining more attention. Also, vibration due to limited structural rigidity is an important issue that must be considered simultaneously. Hence, this research aims at building high-fidelity machine dynamic models that are capable of predicting the dynamic responses, such as the tracking error and motor current signals, considering a wide range of dynamic effects such as structural flexibility, inter-axis coupling, and posture-dependency. Building machine dynamic models via conventional bottom-up approaches may require extensive investigation on every single component. Such approaches are time-consuming or sometimes infeasible for the machine end-users. Alternatively, as the recent trend of Industry 4.0, utilizing data via Computer Numerical Controls (CNCs) and/or non-intrusive sensors to build the machine model is rather favorable for industrial implementation. Thus, the methods proposed in this thesis are top-down model building approaches, utilizing available data from CNCs and/or other auxiliary sensors. The achieved contributions and results of this thesis are summarized below. As the first contribution, a new modeling and identification technique targeting a closed-loop control system of coupled rigid multi-axis feed drives has been developed. A multi-axis closed-loop control system, including the controller and the electromechanical plant, is described by a multiple-input multiple-output (MIMO) linear time-invariant (LTI) system, coupled with a generalized disturbance input that represents all the nonlinear dynamics. Then, the parameters of the open-loop and closed-loop dynamic models are respectively identified by a strategy that combines linear Least Squares (LS) and constrained global optimization. This strategy strikes a balance between model accuracy and computational efficiency. This proposed method was validated using an industrial 5-axis laser drilling machine and an experimental feed drive, achieving 2.38% and 5.26% root mean square (RMS) prediction error, respectively. Inter-axis coupling effects, i.e., the motion of one axis causing the dynamic responses of another axis, are correctly predicted. Also, the tracking error induced by motor ripple and nonlinear friction is correctly predicted as well. As the second contribution, the above proposed methodology is extended to also consider structural flexibility, which is a crucial behavior of large-sized industrial 5-axis machine tools. More importantly, structural vibration is nonlinear and posture-dependent due to the nature of a multibody system. In this thesis, prominent cases of flexibility-induced vibrations in a linear feed drive are studied and modeled by lumped mass-spring-damper system. Then, a flexible linear drive coupled with a rotary drive is systematically analyzed. It is found that the case with internal structural vibration between the linear and rotary drives requires an additional motion sensor for the proposed model identification method. This particular case is studied with an experimental setup. The thesis presents a method to reconstruct such missing internal structural vibration using the data from the embedded encoders as well as a low-cost micro-electromechanical system (MEMS) inertial measurement unit (IMU) mounted on the machine table. It is achieved by first synchronizing the data, aligning inertial frames, and calibrating mounting misalignments. Finally, the unknown internal vibration is reconstructed by comparing the rigid and flexible machine kinematic models. Due to the measurement limitation of MEMS IMUs and geometric assembly error, the reconstructed angle is unfortunately inaccurate. Nevertheless, the vibratory angular velocity and acceleration are consistently reconstructed, which is sufficient for the identification with reasonable model simplification. Finally, the reconstructed internal vibration along with the gathered servo data are used to identify the proposed machine dynamic model. Due to the separation of linear and nonlinear dynamics, the vibratory dynamics can be simply considered by adding complex pole pairs into the MIMO LTI system. Experimental validation shows that the identified model is able to predict the dynamic responses of the tracking error and motor force/torque to the input command trajectory and external disturbances, with 2% ~ 6% RMS error. Especially, the vibratory inter-axis coupling effect and posture-dependent effect are accurately depicted. Overall, this thesis presents a dynamic model-building approach for multi-axis feed drive assemblies. The proposed model is general and can be configured according to the kinematic configuration. The model-building approach only requires the data from the servo system or auxiliary motion sensors, e.g., an IMU, which is non-intrusive and in favor of industrial implementation. Future research includes further investigation of the IMU measurement, geometric error identification, validation using more complicated feed drive system, and applications to the planning and monitoring of 5-axis machining process

Supervisory control of machine tool feed drives.

While motion control of machine tool feed drives is the targeted application. The goal of this study is to explore the relative performance potentials of supervisory control systems against the classical servo control systems; Reconfiguration aspects at the control level are the scope of this study. One of the most essential nonlinear problems faced during modeling and control stages of the CNC machining systems is called backlash. Reversal of motion for each moving axis can lead to that area of disengagement where backlash occurs due to inherently unavoidable clearance between linkages of the machine tool feed drive system. Due to backlash, efficiency of machine tools will be undesirably turned down causing higher vibrations, lower contouring accuracy, and may draw the whole system into instability region. A Switching control scheme is designed to manage the control process where two different controllers with two different control functionalities, acting differently in two vital zones---one of them where the backlash lies, and the other when moving past the backlash---seem to be an important need. (Abstract shortened by UMI.)Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .S53. Source: Masters Abstracts International, Volume: 43-03, page: 0961. Adviser: Waguih ElMaraghy. Thesis (M.A.Sc.)--University of Windsor (Canada), 2004