Detection of Communities within the Multibody System Dynamics Network and Analysis of Their Relations

Multibody system dynamics is already a well developed branch of theoretical, computational and applied mechanics. Thousands of documents can be found in any of the well-known scientific databases. In this work it is demonstrated that multibody system dynamics is built of many thematic communities. Using the Elsevier’s abstract and citation database SCOPUS, a massive amount of data is collected and analyzed with the use of the open source visualization tool Gephi. The information is represented as a large set of nodes with connections to study their graphical distribution and explore geometry and symmetries. A randomized radial symmetry is found in the graphical representation of the collected information. Furthermore, the concept of modularity is used to demonstrate that community structures are present in the field of multibody system dynamics. In particular, twenty-four different thematic communities have been identified. The scientific production of each community is analyzed, which allows to predict its growing rate in the next years. The journals and conference proceedings mainly used by the authors belonging to the community as well as the cooperation between them by country are also analyzed

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 %

Nonlinear control of an industrial robot

The precise control of a robot manipulator travelling at high speed constitutes a major research challenge. This is due to the nonlinear nature of the dynamics of the arm which make many traditional, linear control methodologies inappropriate. An alternative approach is to adopt controllers which are themselves nonlinear. Variable structure control systems provide the possibility of imposing dynamic characteristics upon a poorly modelled and time varying system by means of a discontinuous control signal. The basic algorithm overcomes some nonlinear effects but is sensitive to Coulomb friction andactuator saturation. By augmenting this controller with compensation terms, these effects may largely be eliminated.In order to investigate these ideas, a number of variable structure control systems ~re applied to a low cost industrial robot having a highly nonlinear and flexible drive system. By a combination of hardware enhancements and control system developments, an improvement in speed by a factor of approximately three was achieved while the trajectory tracking accuracy was improved by a factor of ten, compared with the manufacturer's control system.In order to achieve these improvements, it was necessary to develop a dynamic model of the arm including the effects of drive system flexibility and nonlinearities. The development of this model is reported in this thesis, as is work carried out on a comparison of numerical algorithms for the solution of differential equations with discontinuous right hand sides, required in the computer aided design of variable structure control systems

A differential-based parallel force/velocity actuation concept : theory and experiments

textRobots are now moving from their conventional confined habitats such as factory floors to human environments where they assist and physically interact with people. The requirement for inherent mechanical safety is overarching in such human-robot interaction systems. We propose a dual actuator called Parallel Force/Velocity Actuator (PFVA) that combines a Force Actuator (FA) (low velocity input) and a Velocity Actuator (VA) (high velocity input) using a differential gear train. In this arrangement mechanical safety can be achieved by limiting the torque on the FA and thus making it a backdriveable input. In addition, the kinematic redundancy in the drive can be used to control output velocity while satisfying secondary operational objectives. Our research focus was on three areas: (i) scalable parametric design of the PFVA, (ii) analytical modeling of the PFVA and experimental testing on a single-joint prototype, and (iii) generalized model formulation for PFVA-driven serial robot manipulators. In our analysis, the ratio of velocity ratios between the FA and the VA, called the relative scale factor, emerged as a purely geometric and dominant design parameter. Based on a dimensionless parametric design of PFVAs using power-flow and load distributions between the inputs, a prototype was designed and built using commercial-off-the-shelf components. Using controlled experiments, two performance-limiting phenomena in our prototype, friction and dynamic coupling between the two inputs, were identified. Two other experiments were conducted to characterize the operational performance of the actuator in velocity-mode and in what we call ‘torque-limited’ mode (i.e. when the FA input can be backdriven). Our theoretical and experimental results showed that the PFVA can be mechanical safe to both slow collisions and impacts due to the backdriveability of the FA. Also, we show that its kinematic redundancy can be effectively utilized to mitigate low-velocity friction and backlash in geared mechanisms. The implication at the system level of our actuator level analytical and experimental work was studied using a generalized dynamic modeling framework based on kinematic influence coefficients. Based on this dynamic model, three design case studies for a PFVA-driven serial planar 3R manipulator were presented. The major contributions of this research include (i) mathematical models and physical understanding for over six fundamental design and operational parameters of the PFVA, based on which approximately ten design and five operational guidelines were laid out, (ii) analytical and experimental proof-of-concept for the mechanical safety feature of the PFVA and the effective utilization of its kinematic redundancy, (iii) an experimental methodology to characterize the dynamic coupling between the inputs in a differential-summing mechanism, and (iv) a generalized dynamic model formulation for PFVA-driven serial robot manipulators with emphasis on distribution of output loads between the FA and VA input-sets.Mechanical Engineerin

Identification of robotic manipulators' inverse dynamics coefficients via model-based adaptive networks

The values of a given manipulator's dynamics coefficients need to be accurately identified in order to employ model-based algorithms in the control of its motion. This thesis details the development of a novel form of adaptive network which is capable of accurately learning the coefficients of systems, such as manipulator inverse dynamics, where the algebraic form is known but the coefficients' values are not. Empirical motion data from a pair of PUMA 560s has been processed by the Context-Sensitive Linear Combiner (CSLC) network developed, and the coefficients of their inverse dynamics identified. The resultant precision of control is shown to be superior to that achieved from employing dynamics coefficients derived from direct measurement. As part of the development of the CSLC network, the process of network learning is examined. This analysis reveals that current network architectures for processing analogue output systems with high input order are highly unlikely to produce solutions that are good estimates throughout the entire problem space. In contrast, the CSLC network is shown to generalise intrinsically as a result of its structure, whilst its training is greatly simplified by the presence of only one minima in the network's error hypersurface. Furthermore, a fine-tuning algorithm for network training is presented which takes advantage of the CSLC network's single adaptive layer structure and does not rely upon gradient descent of the network error hypersurface, which commonly slows the later stages of network training

A fast trajectory tracking adaptive controller for robot manipulators

An adaptive decentralized nonlinear controller for a robot manipulator is presented in this thesis. Based on the adaptive control schemes designed by Seraji [18], Dai [30], and Jimenez [31], we redesigned and further simplified the control algorithm and, as a consequence, we achieved better path tracking performance. The proposed adaptive controller is made of a PD feedback controller which has time varying gains, a feedforward compensator based on the idea of inverse dynamics, and an auxiliary signal. Due to its adaptive structure, the controller shows robustness against disturbances and unmodeled dynamics. In order to ensure asymptotic tracking we select a Lyapunov function such that the controller forces the negative definiteness of the time derivative of such a Lyapunov function. To do this, the tracking position and velocity error are penalized and used as a part of the adaptive control gain. The main advantages of this scheme are the comparably faster convergence of tracking error, relatively simpler structure, and smoother control activity. This controller only requires the position and angular speed measurement, it does not require any knowledge about the mathematical model of the robot manipulator. Simulation shows the capacity of this controller and its robustness against disturbances

Model-Based Robot Control and Multiprocessor Implementation

Model-based control of robot manipulators has been gaining momentum in recent years. Unfortunately there are very few experimental validations to accompany simulation results and as such majority of conclusions drawn lack the credibility associated with the real control implementation

Robust high-performance control for robotic manipulators

Model-based and performance-based control techniques are combined for an electrical robotic control system. Thus, two distinct and separate design philosophies have been merged into a single control system having a control law formulation including two distinct and separate components, each of which yields a respective signal component that is combined into a total command signal for the system. Those two separate system components include a feedforward controller and a feedback controller. The feedforward controller is model-based and contains any known part of the manipulator dynamics that can be used for on-line control to produce a nominal feedforward component of the system's control signal. The feedback controller is performance-based and consists of a simple adaptive PID controller which generates an adaptive control signal to complement the nominal feedforward signal