Joint Dynamics and Adaptive Feedforward Control of Lightweight Industrial Robots

The use of lightweight strain-wave transmissions in collaborative industrial robots leads to structural compliance and a complex nonlinear behavior of the robot joints. Furthermore, wear and temperature changes lead to variations in the joint dynamics behavior over time. The immediate negative consequences are related to the performance of motion and force control, safety, and lead-through programming.This thesis introduces and investigates new methods to further increase the performance of collaborative industrial robots subject to complex nonlinear and time-varying joint dynamics behavior. Within this context, the techniques of mathematical modeling, system identification, and adaptive estimation and control are applied. The methods are experimentally validated using the collaborative industrial robots by Universal Robots.Mathematically, the robot and joint dynamics are considered as two coupled subsystems. The robot dynamics are derived and linearly parametrized to facilitate identification of the inertial parameters. Calibrating these parameters leads to improvements in torque prediction accuracy of 16.5 %-28.5 % depending on the motion.The joint dynamics are thoroughly analyzed and characterized. Based on a series of experiments, a comprehensive model of the robot joint is established taking into account the complex nonlinear dynamics of the strain-wave transmission, that is the nonlinear compliance, hysteresis, kinematic error, and friction. The steady-state friction is considered to depend on angular velocity, load torque, and temperature. The dynamic friction characteristics are described by an Extended Generalized Maxwell-Slip (E-GMS) model which describes in a combined framework; hysteresis characteristics that depend on angular position and Coulomb friction that depend on load torque. E-GMS model-based feedforward control improves the torque prediction accuracy by a factor 2.1 and improve the tracking error by a factor 1.5.An E-GMS model-based adaptive feedforward controller is developed to address the issue of friction changing with wear and temperature. The adaptive control strategy leads to improvements in torque prediction of 84 % and tracking error of 20 %

Active and Passive Control of Machine Tool Vibrations for High Speed and Accuracy

High-performance mechatronic systems are widely used in precision manufacturing equipment such as CNC machine tools, 3D-Printers, photolithography systems, industrial robots, and Coordinate Measuring Machines (CMMs). These equipment are utilized in producing parts and components for aviation, semiconductor, optics, and many other emerging industries, with geometric features and surface properties within micrometer-, or even in some cases nanometer-level accuracy. To keep up with the rapidly increasing productivity and accuracy demands, it is crucial that mechatronic systems of these manufacturing equipment deliver high-speed motion with high precision. In this dissertation, motion control strategies are presented to increase dynamic positioning accuracy and productivity of such mechatronic systems. First, a novel trajectory generation method is presented to avoid exciting low frequency structural vibration modes of machine tools and 3D-Printers, without compromising from productivity. The trajectory generation problem is posed as a convex optimization problem, and a practical windowing method is presented to implement the proposed strategy in real-time for long and realistic manufacturing scenarios. The proposed algorithm is validated on an industrial 3-Axis machine tool, and 4-6 times attenuation of the column vibration mode is achieved with 1[g] acceleration commands, without increasing the cycle time compared to state-of-the-art trajectory generation methods. This is followed by proposition of a data-driven trajectory shaping algorithm designed to eliminate dynamic positioning errors induced by flexible motion transmission components (such as ball-screw drives) and nonlinear friction forces typically caused by mechanical bearings and guiding units. The proposed algorithm is used for optimizing trajectory pre-filters through machine-in-the-loop iterations, in a data-driven fashion, and therefore it can be applied on a wide variety of systems without requiring elaborate dynamic modeling. Effectiveness of the proposed technique is validated on a linear-motor-driven planar motion stage and an industrial 3-Axis machine tool, and it is shown that dynamic errors are reduced by 3-5 times compared to industry-standard approaches. Finally, an active tool position control strategy is proposed to mitigate self-excited (chatter) vibrations for improving stability margins of turning processes. Two motion control algorithms are developed to control the dynamic process defined by the interaction of the tool and the workpiece. An industrial lathe (turning center) is utilized for validating the effectiveness of proposed algorithms. A piezo-actuator driven tool-assembly is utilized to control tool position during the machining process, utilizing tool acceleration feedback, and the experiments show that 4-5 times increase in productivity (widths of cut) is achieved by the proposed strategy

Position control on nanometer scale based on an adaptive friction compensation scheme

This work concerns a non-model-based friction compensation scheme for dynamic position control on nanometer scale. The main goal of this work is to build up and implement a simple dynamic friction observer which allows an estimation of the friction force in combination with the system inertia against displacement. Experiments in the pre-sliding and sliding friction regimes are con-ducted on an experimental setup. After a short review of friction compensation, the experimental setup is explained in detail. Next, the observer is modeled mathematically and the used control scheme is presented. Finally, the friction observer is utilized as a non-model-based friction estimator combined with a classical feedback controller to compensate the nonlinear friction force and reduce tracking errors significantly. It is shown that the proposed controlling approach is able to realize a fast and ultra precise positioning over long distances

Modelling and control of position and velocity drives subject to friction

Performance degradation in most mechanical systems with friction which are not easily eliminated through design mechanisms can be greatly reduced within acceptable limits through the process of friction compensation. Generally friction compensators are used to improve system performance in terms of error reduction, transient response, thereby countering the effects of friction. Model-based techniques for friction compensation require an accurate model of the system friction. This is very important for high precision mechanical systems where excellent positioning and motion tracking, especially in the low velocities, is critical. This thesis proposes a new integrated friction model structure capable of modelling known friction dynamics. The new friction model incorporates a pre-sliding friction function with non-local hysteretic features. Analysis of the model shows the model to possess dissipative, boundedness, passivity and uniqueness properties. Results of sensitivity and robustness analysis indicate the new friction model is robust to parameter variations. A friction characterisation test-bed was designed and constructed for the purposes of friction identification, compensation and control. A set of experiments were designed and implemented on the test rig/bed to demonstrate friction dynamics. The input- output results of the experiments were used for parameter estimation of the proposed new friction model and some other relevant friction model structures. The performance of the new friction model for position and velocity control was studied using the experimental friction test-bed and simulations. The result of such analysis underscores the advantage of integrating a friction observer in the system control loop. The new friction model provided better position and velocity control of the experimental friction test-rig when compared with other well known models of friction

Multi-scale Structural Health Monitoring using Wireless Smart Sensors

Tremendous progress has been made in recent years in the wireless smart sensor (WSS) technology to monitor civil infrastructures, shifting focus away from traditional wired methods. Successful implementations of such WSS networks for full-scale SHM have demonstrated the feasible use of the technology. Much of the previous research and application efforts have been directed toward single-metric applications. Multi-metric monitoring, in combination with physics-based models, has great potential to enhance SHM methods; however, the efficacy of the multi-metric SHM has not been illustrated using WSS networks to date, due primarily to limited hardware capabilities of currently available smart sensors and lack of effective algorithms. This research seeks to develop multi-scale WSSN strategies for advanced SHM in cost effective manner by considering: (1) the development of hybrid SHM method, which combine numerical modeling and multi-metric physical monitoring, (2) multi-metric and high-sensitivity hardware developments for use in WSSNs, (3) network software developments for robust WSSN, (4) algorithms development to better utilize the outcomes from SHM system, and (5) fullscale experimental validation of proposed research. The completion of this research will result in an advanced multi-scale WSS framework to provide innovate ways civil infrastructure is monitored.Financial support for this research was provided in part by the National Science Foundation under NSF Grants No. CMS-0600433 and CMMI-0928886.Ope

Haptic teleoperation of the youbot with friction compensation for the base

Haptic devices are bringing new possibilities for teleoperation by increasing the level of awareness that the operator can have over the slave. In other words, they create a stronger link between them. Because it is not enough to have a view of the task at hand, it is better to feel what is really happening at the other side. The main goal of the project is to provide the KUKA youBot with an Omega 6 haptic interface. The operator can feel the movement limitations that the arm’s tooltip may be experiencing, resulting in a better driving practice. But with these new capabilities other concerns arise, like the choice of an appropriate control algorithm, the correct coupling of workspaces or the design of a suitable data handling scheme. However, the current setup has not yet been submitted to a proper system validation and so there is still much work to do in order to increase its overall performance. Since friction can have a major role in the control scheme of the system, the latter should be provided with friction compensation. To achieve this, a study of the youBot wheels motor block friction has been carried out. These results are then also incorporated in the robot simulation. Moreover, when identifying this kind of behaviours some important decisions have to be made in order to get the best results from the time invested. Among those are the selection of a friction model, the system identification experiments and the validation of results. In conclusion, it has been proven that the implementation of a haptic interface for the youBot is not only feasible but that it delivers a greater overall teleoperation experience. Also, although the results of this project are an initial version of the system, the friction compensation for the base motor blocks is already working with acceptable performance. ________________________________________________________________________________________________________________Los dispositivos hápticos están trayendo nuevas posibilidades a la teleoperación, aumentando el nivel de consciencia que el operador puede tener sobre la máquina que dirige. En otras palabras, crean un vínculo más fuerte entre ambos. Y es que a veces no es suficiente visualizar la tarea que se esta realizando, es mejor notar lo que realmente está pasando en el otro lado. El objetivo principal del proyecto es proporcionar al robot youBot de KUKA una interfaz con el dispositivo háptico Omega6. El operador puede notar las limitaciones en los movimientos que la herramienta del brazo robot pueda estar experimentando, resultando así en una mejor experiencia de conducción. Pero con estas nuevas capacidades aparecen otras preocupaciones, como elegir un algoritmo de control apropiado, la correcta unión de los espacios de trabajo o el diseño de un esquema de manejo de datos adecuado. Sin embargo, la instalación actual aún no ha sido sometida a una evaluación de sistema apropiada y por lo tanto todavía hay mucho trabajo por hacer para incrementar el rendimiento general. Ya que la fricción puede tener un rol importante en el esquema de control del sistema, este debería ser provisto con compensación de fricción. Para lograr esto se ha llevado a cabo un estudio de los bloques motor de las ruedas del youBot. Estos resultados se han incorporado también a la simulación del robot. Por otra parte, cuando se identifican esta clase de comportamientos se han de tomar decisiones importantes para obtener los mejores resultados del tiempo empleado. Entre estas están la selección de un modelo de fricción adecuado, los experimentos para identificar el sistema y la validación de los resultados. En conclusión, se ha probado que la implementación del youBot con una interfaz háptica no es solo posible sino que mejora la experiencia general de teleoperación. Además, aunque los resultados del proyecto son una versión inicial del sistema, la compensación de la fricción para los bloques motor de la base ya está funcionando con un rendimiento aceptable.Ingeniería Industria

Battery Management System for Future Electric Vehicles

The future of electric vehicles relies nearly entirely on the design, monitoring, and control of the vehicle battery and its associated systems. Along with an initial optimal design of the cell/pack-level structure, the runtime performance of the battery needs to be continuously monitored and optimized for a safe and reliable operation and prolonged life. Improved charging techniques need to be developed to protect and preserve the battery. The scope of this Special Issue is to address all the above issues by promoting innovative design concepts, modeling and state estimation techniques, charging/discharging management, and hybridization with other storage components

Model Referenced Condition Monitoring of High Performance CNC Machine Tools

Generally, machine tool monitoring is the prediction of the system’s health based on signal acquisition and processing and classification in order to identify the causes of the problem. The producers of machine tools need to pay more attention to their products life cycle because their customers increasingly focus on machine tool reliability and costs. The present study is concerned with the development of a condition monitoring system for high speed Computer Numerical Control (CNC) milling machine tools. A model is a simplification of a real machine to visualize the dynamics of a mechatronic system. This thesis applies recent modelling techniques to represent all parameters which affect the accuracy of a component produced automatically. The control can achieve an accuracy approaching the tolerance restrictions imposed by the machine tool axis repeatability and its operating environment. The motion control system of the CNC machine tool is described and the elements, which compose the axis drives including both the electrical components and the mechanical ones, are analysed and modelled. SIMULINK models have been developed to represent the majority of the dynamic behaviour of the feed drives from the actual CNC machine tool. Various values for the position controller and the load torque have been applied to the motor to show their behaviour. Development of a mechatronic hybrid model for five-axis CNC machine tool using Multi-Body-System (MBS) simulation approach is described. Analysis of CNC machine tool performance under non-cutting conditions is developed. ServoTrace data have been used to validate the Multi-body simulation of tool-to-workpiece position. This thesis aspects the application of state of art sensing methods in the field of condition monitoring of electromechanical systems. The ballscrew-with-nut is perhaps the most prevalent CNC machine subsystem and the condition of each element is crucial to the success of a machining operation. It’s essential to know of the health status of ballscrew, bearings and nut. Acoustic emission analysis of machines has been carried out to determine the deterioration of the ballscrew. Standard practices such as use of a Laser Interferometer have been used to determine the position of the machine tool. A novel machine feed drive condition monitoring system using acoustic emission (AE) signals has been proposed. The AE monitoring techniques investigated can be categorised into traditional AE parameters of energy, event duration and peak amplitude. These events are selected and normalised to estimate remaining life of the machine. This method is shown to be successfully applied for the ballscrew subsystem of an industrial high-speed milling machine. Finally, the successful outcome of the project will contribute to machine tool industry making possible manufacturing of more accurate products with lower costs in shorter time

Joint friction estimation and slip prediction of biped walking robots

Friction is a nonlinear and complex phenomenon. It is unwanted at the biped joints since it deteriorates the robot’s walking performance in terms of speed and dynamic behavior. On the other hand, it is desired and required between the biped feet and the walking surface to facilitate locomotion. Further, friction forces between the feet and the ground determine the maximum acceleration and deceleration that the robot can afford without foot slip. Although several friction models are developed, there is no exact model that represents the friction behavior. This is why online friction estimation and compensation enter the picture. However, when online model-free estimation is difficult, a model-based method of online identification can prove useful. This thesis proposes a new approach for the joint friction estimation and slip prediction of walking biped robots. The joint friction estimation approach is based on the combination of a measurementbased strategy and a model-based method. The former is used to estimate the joint friction online when the foot is in contact with the ground, it utilizes the force and acceleration measurements in a reduced dynamical model of the biped. The latter adopts a friction model to represent the joint friction when the leg is swinging. The model parameters are identified adaptively using the estimated online friction whenever the foot is in contact. Then the estimated joint friction contributes to joint torque control signals to improve the control performance. The slip prediction is a model-free friction-behavior-inspired approach. A measurement-based online algorithm is designed to estimate the Coulomb friction which is regarded as a slip threshold. To predict the slip, a safety margin is introduced in the negative vicinity of the estimated Coulomb friction. The estimation algorithm concludes that if the applied force is outside the safety margin, then the foot tends to slip. The proposed estimation approaches are validated by experiments on SURALP (Sabanci University Robotics Research Laboratory Platform) and simulations on its model. The results demonstrate the effectiveness of these methods

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