New Approaches in Automation and Robotics

The book New Approaches in Automation and Robotics offers in 22 chapters a collection of recent developments in automation, robotics as well as control theory. It is dedicated to researchers in science and industry, students, and practicing engineers, who wish to update and enhance their knowledge on modern methods and innovative applications. The authors and editor of this book wish to motivate people, especially under-graduate students, to get involved with the interesting field of robotics and mechatronics. We hope that the ideas and concepts presented in this book are useful for your own work and could contribute to problem solving in similar applications as well. It is clear, however, that the wide area of automation and robotics can only be highlighted at several spots but not completely covered by a single book

Validation of Linearized Flight Models using Automated System-Identification

Optimization based flight control design tools depend on automatic linearization tools, such as Simulink®’s LINMOD, to extract linear models. In order to ensure the usefulness and correctness of the generated linear model, this linearization must be accurate. So a method of independently verifying the linearized model is needed. This thesis covers the automation of a system identification tool, CIFER®, for use as a verification tool integrated with CONDUIT®, an optimization based design tool. Several test cases are built up to demonstrate the accuracy of the verification tool with respect to analytical results and matches with LINMOD. Several common nonlinearities are tested, comparing the results from CIFER and LINMOD, as well as analytical results where possible. The CIFER results show excellent agreement with analytical results. LINMOD treated most nonlinearity as a unit gain, but some nonlinearities linearized to a zero, causing the linearized model to omit that path. Although these effects are documented within Simulink, their presence may be missed by a user. The verification tool is successful in identifying these problems when present. A section is dedicated to the diagnosis of linearization errors, suggesting solutions where possible

Feedback Control Goes Wireless: Guaranteed Stability over Low-power Multi-hop Networks

Closing feedback loops fast and over long distances is key to emerging applications; for example, robot motion control and swarm coordination require update intervals of tens of milliseconds. Low-power wireless technology is preferred for its low cost, small form factor, and flexibility, especially if the devices support multi-hop communication. So far, however, feedback control over wireless multi-hop networks has only been shown for update intervals on the order of seconds. This paper presents a wireless embedded system that tames imperfections impairing control performance (e.g., jitter and message loss), and a control design that exploits the essential properties of this system to provably guarantee closed-loop stability for physical processes with linear time-invariant dynamics. Using experiments on a cyber-physical testbed with 20 wireless nodes and multiple cart-pole systems, we are the first to demonstrate and evaluate feedback control and coordination over wireless multi-hop networks for update intervals of 20 to 50 milliseconds.Comment: Accepted final version to appear in: 10th ACM/IEEE International Conference on Cyber-Physical Systems (with CPS-IoT Week 2019) (ICCPS '19), April 16--18, 2019, Montreal, QC, Canad

Design and Implementation of One and Two-Degree-of-Freedom Magnetic Suspension and Balance Systems

The main objectives of this research were to design and implement one and two-degree-of-freedom (1 DOF and 2-DOF) magnetic levitation systems to levitate permanent magnet cores contained in PVC pipes, 8.4 cm and 76.2 cm in length, respectively. This project used the components of a Magnetic Suspension and Balance System (MSBS) that is being built to provide obstruction free positioning of test models in six degrees of freedom (6-DOF) inside the Princeton University/Office of Naval Research High Reynolds Number Test Facility (HRTF). The HRTF, a specialized wind tunnel designed to simulate undersea conditions by creating a low-speed, 3500 PSI air environment, imposes design challenges unique to this MSBS. Among these challenges are the need to control magnetic flux densities through the two-inch thick stainless steel walls of the suspension chamber and to suspend a heavy test object for long periods due to the limited access to the chamber\u27s interior

Adaptive Augmentation of Non-Minimum Phase Flexible Aerospace Systems

This work demonstrates the efficacy of direct adaptive augmentation on a robotic flexible system as an analogue of a large flexible aerospace structure such as a launch vehicle or aircraft. To that end, a robot was constructed as a control system testbed. This robot, named “Penny,” contains the command and data acquisition capabilities necessary to influence and record system state data, including the flex states of its flexible structures. This robot was tested in two configurations, one with a vertically cantilevered flexible beam, and one with a flexible inverted pendulum (a flexible cart-pole system). The physical system was then characterized so that linear analysis and control design could be performed. These characterizations resulted in linear and nonlinear models developed for each testing configuration. The linear models were used to design linear controllers to regulate the nominal plant’s dynamical states. These controllers were then augmented with direct adaptive output regulation and disturbance accommodation. To accomplish this, sensor blending was used to shape the output such that the nonminimum phase open loop plant appears to be minimum phase to the controller. It was subsequently shown that augmenting linear controllers with direct adaptive output regulation and disturbance accommodation was effective in enhancing system performance and mitigating oscillation in the flexible structures through the system’s own actuation effort

Digital Twin Feed Drive Identification for Virtual Process Planning

Computer numerical controlled (CNC) machines have become an integral part of the manufacturing industry, allowing companies to increase the accuracy and productivity of their manufacturing lines. The next step to improving and accelerating the development process of a part is to involve virtual prototyping during the design phases. Virtual manufacturing has become an invaluable tool to process planners and engineers in recent years to model the manufacturing environment in a virtual setting to determine the final geometry and tolerances of new parts and processes. For a virtual twin of a CNC machine to be built, the dynamics of the drive and CNC controller must be identified. Traditionally, these identification techniques require several intrusive tests to be run on the machine tool, causing valuable time lost on production machines. In this thesis, three new techniques of developing virtual models of machine tools are discussed. The first model presented is a quasi-static model which is suitable for trajectory tracking error prediction. This technique is used to determine the contributions of the commanded velocity, acceleration, and jerk to the tracking errors of each axis of the machine tool. After determining these contributions, process planners can modify the axis feedrates in a virtual environment during trajectory optimization to find the best parameters for the shortest cycle time. This method was validated using a laser drilling machine tool from Pratt and Whitney Canada (P&WC) and was able to predict the root mean square (RMS) of the tracking error within 2.62 to 11.91 µm. A simple graphical user interface (GUI) was developed so that process planners and engineers can import data collected from the FANUC and Siemens CNC controllers to identify quasi-static models. The second model presented is a single input – single output (SISO) rigid body rapid identification model. In previous literature, a rapid identification method was proposed where a short G-code was run on machine tools, the input and output signals were collected from the controller and the dynamics were reverse engineered from the gathered data. However there were some shortfalls with this older method, the new proposed rapid identification model addresses these by improving parameter convergence and using commanded signal derivatives for identification. Tests were conducted on a five-axis machine tool located at the University of Waterloo (UW) to verify and compare the new rapid identification model to the previous model. It was determined that the model is able to predict the RMS of the tracking errors with 50-76% improvement and maximum contour error discrepancy with 22-35% improvement. Another GUI was developed for the SISO rigid body rapid identification model that allows users to import data collected from different machine tools and identify a model. The third model that is discussed in this thesis is a multi input – multi output (MIMO) model. This model builds upon the SISO rigid body model and is able to capture vibratory and elastic dynamics. Relations between inputs, such as reference and disturbance signals, can be related to a variety of measurable outputs. The model is used to predict the relationship between the inputs of commanded position and disturbance to the outputs of tracking error and velocity of the x- and y- axes of a P&WC five axis milling machine tool. Three different models were identified using this algorithm, two 1-axis 3rd order decoupled models and two 2-axis 6th order coupled model are compared in this thesis. The two 6 th order models have different search spaces, the first has a search space defined from the 3rd order decoupled identified parameters while the second has a more general search space. Overall, the 6th order model with a larger search space was able to predict the RMS and maximum tracking error more closely, with a maximum improvement of 19% for both metrics. However it should be noted that 6th order model with a smaller search space was still able to predict the RMS and maximum tracking error similarly to the 6th order model with the larger search space. The smaller search space configuration can save on computational time which can be advantageous in real world applications. In order to verify that the MIMO rapid identification technique would be able to identify a vibration mode, an experimental setup was designed and machined. A flexure mount with known vibration modes was designed, built and tested to validate Solidworks frequency simulation results. It was concluded that the simulation results were able to estimate the frequencies of the flexure with 95-98% accuracy and with a maximum absolute difference of 2.87 Hz. The flexure was mounted onto the five-axis machine tool at UW to introduce vibratory dynamics. Since there is a flexible mode being introduced at the tool-workpiece interface, the motor encoders would not be able to capture these dynamics, therefore a two-dimensional grid encoder (KGM) and two 3-axis accelerometers (one located on the tool head and the other on the workpiece table) were also placed on the machine tool to record the true tool-workpiece response. The data collected from the accelerometers were corrected for possible roll, pitch and yaw misalignments before synchronizing the accelerometer and KGM data to the motor encoder data. This data was then used to build MIMO rapid identification models with the commanded position (recorded from the motor encoders) and normalized Coulomb disturbance as the inputs to the system and the true tool-workpiece position or acceleration and machine tool feed drive velocity as the outputs to the model. The model estimated from the position measurements from the KGM yielded better results 19-1496% improvement in RMS tracking error prediction over the acceleration based models. The contouring error estimated using the KGM position model also has an improvement of 233-370% over the acceleration models. Using the transfer functions estimated from the accelerometer data, there was a 16-33% improvement in the RMS tracking error prediction and an 11-51% improvement in the maximum tracking error prediction over the KGM acceleration based model. The RMS contour prediction error also improved 4-5% and the maximum contour error prediction improved by 1-6% between the two models. Further development into the MIMO LTI algorithm is currently being done in the laboratory, including research into more complex friction models. It is also recommended to machine an actual part on the five axis machine tool and to measure the contouring error of the part on the coordinate measuring machine to verify the predictions presented in this thesis

Identification and Optimal Linear Tracking Control of ODU Autonomous Surface Vehicle

Autonomous surface vehicles (ASVs) are being used for diverse applications of civilian and military importance such as: military reconnaissance, sea patrol, bathymetry, environmental monitoring, and oceanographic research. Currently, these unmanned tasks can accurately be accomplished by ASVs due to recent advancements in computing, sensing, and actuating systems. For this reason, researchers around the world have been taking interest in ASVs for the last decade. Due to the ever-changing surface of water and stochastic disturbances such as wind and tidal currents that greatly affect the path-following ability of ASVs, identification of an accurate model of inherently nonlinear and stochastic ASV system and then designing a viable control using that model for its planar motion is a challenging task. For planar motion control of ASV, the work done by researchers is mainly based on the theoretical modeling in which the nonlinear hydrodynamic terms are determined, while some work suggested the nonlinear control techniques and adhered to simulation results. Also, the majority of work is related to the mono- or twin-hull ASVs with a single rudder. The ODU-ASV used in present research is a twin-hull design having two DC trolling motors for path-following motion. A novel approach of time-domain open-loop observer Kalman filter identifications (OKID) and state-feedback optimal linear tracking control of ODU-ASV is presented, in which a linear state-space model of ODU-ASV is obtained from the measured input and output data. The accuracy of the identified model for ODU-ASV is confirmed by validation results of model output data reconstruction and benchmark residual analysis. Then, the OKID-identified model of the ODU-ASV is utilized to design the proposed controller for its planar motion such that a predefined cost function is minimized using state and control weighting matrices, which are determined by a multi-objective optimization genetic algorithm technique. The validation results of proposed controller using step inputs as well as sinusoidal and arc-like trajectories are presented to confirm the controller performance. Moreover, real-time water-trials were performed and their results confirm the validity of proposed controller in path-following motion of ODU-ASV