978 research outputs found

    A survey of non-prehensible pneumatic manipulation surfaces : principles, models and control.

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    International audienceMany manipulation systems using air flow have been proposed for object handling in a non-prehensile way and without solid-to-solid contact. Potential applications include high-speed transport of fragile and clean products and high-resolution positioning of thin delicate objects. This paper discusses a comprehensive survey of state-of-the-art pneumatic manipulation from the macro scale to the micro scale. The working principles and actuation methods of previously developed air-bearing surfaces, ultra-sonic bearing surfaces, air-flow manipulators, air-film manipulators, and tilted air-jet manipulators are reviewed with a particular emphasis on the modeling and the control issues. The performance of the previously developed devices are compared quantitatively and open problems in pneumatic manipulation are discussed

    Comparison of Modern Controls and Reinforcement Learning for Robust Control of Autonomously Backing Up Tractor-Trailers to Loading Docks

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    Two controller performances are assessed for generalization in the path following task of autonomously backing up a tractor-trailer. Starting from random locations and orientations, paths are generated to loading docks with arbitrary pose using Dubins Curves. The combination vehicles can be varied in wheelbase, hitch length, weight distributions, and tire cornering stiffness. The closed form calculation of the gains for the Linear Quadratic Regulator (LQR) rely heavily on having an accurate model of the plant. However, real-world applications cannot expect to have an updated model for each new trailer. Finding alternative robust controllers when the trailer model is changed was the motivation of this research. Reinforcement learning, with neural networks as their function approximators, can allow for generalized control from its learned experience that is characterized by a scalar reward value. The Linear Quadratic Regulator and the Deep Deterministic Policy Gradient (DDPG) are compared for robust control when the trailer is changed. This investigation quantifies the capabilities and limitations of both controllers in simulation using a kinematic model. The controllers are evaluated for generalization by altering the kinematic model trailer wheelbase, hitch length, and velocity from the nominal case. In order to close the gap from simulation and reality, the control methods are also assessed with sensor noise and various controller frequencies. The root mean squared and maximum errors from the path are used as metrics, including the number of times the controllers cause the vehicle to jackknife or reach the goal. Considering the runs where the LQR did not cause the trailer to jackknife, the LQR tended to have slightly better precision. DDPG, however, controlled the trailer successfully on the paths where the LQR jackknifed. Reinforcement learning was found to sacrifice a short term reward, such as precision, to maximize the future expected reward like reaching the loading dock. The reinforcement learning agent learned a policy that imposed nonlinear constraints such that it never jackknifed, even when it wasn\u27t the trailer it trained on

    Development of a novel additive manufacturing method: process generation and evaluation of 3D printed parts made with alumina nanopowder

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    2017 Fall.Includes bibliographical references.Direct coagulation printing (DCP) is a new approach to extrusion-based additive manufacturing, developed during this thesis project using alumina nanopowder. The fabrication of complex ceramic parts, sintered to full density, was achieved and the details of this invention are described. With the use of additive manufacturing, complex features can be generated that are either very difficult or unattainable by conventional subtractive manufacturing methods. Three unique approaches were taken to create a slurry suitable for extrusion 3D-printing. Each represented a different method of suspending alumina nanopowder in a liquid; a bio-polymer gel based on chitosan, a synthetic polymer binder using poly-vinyl acetate (PVA), and electrostatic stabilization with the dispersant tri-ammonium citrate (TAC). It was found that TAC created a slurry with viscosity and coagulation rate that were tuneable through pH adjustment with nitric acid. This approach led to the most promising printing and sintering results, and is the basis of DCP. Taguchi and fractional factorial design of experiments models were used to optimize mixing of the alumina slurry, rheological properties, print quality, and sinterability. DCP was characterized by measuring the mechanical properties and physical characteristics of printed parts. Features as small as ~450 μm in width were produced, in parts with overhangs and enclosed volumes, in both linear and radial geometries. After sintering, these parts exhibited little to no porosity, with flexural modulus and hardness comparing favorably with conventionally manufactured alumina parts. A remarkable aspect of DCP is that it is a completely binderless process, requiring no binder removal step. In addition, DCP can employ nanopowders, allowing for enhanced mechanical properties as observed in nano-grained materials. Perhaps most importantly, any material that acquires a surface charge when in aqueous media has the potential to be used in DCP, making it a method of additive manufacturing using many metals and ceramics other than alumina

    Reinforcement Learning and Planning for Preference Balancing Tasks

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    Robots are often highly non-linear dynamical systems with many degrees of freedom, making solving motion problems computationally challenging. One solution has been reinforcement learning (RL), which learns through experimentation to automatically perform the near-optimal motions that complete a task. However, high-dimensional problems and task formulation often prove challenging for RL. We address these problems with PrEference Appraisal Reinforcement Learning (PEARL), which solves Preference Balancing Tasks (PBTs). PBTs define a problem as a set of preferences that the system must balance to achieve a goal. The method is appropriate for acceleration-controlled systems with continuous state-space and either discrete or continuous action spaces with unknown system dynamics. We show that PEARL learns a sub-optimal policy on a subset of states and actions, and transfers the policy to the expanded domain to produce a more refined plan on a class of robotic problems. We establish convergence to task goal conditions, and even when preconditions are not verifiable, show that this is a valuable method to use before other more expensive approaches. Evaluation is done on several robotic problems, such as Aerial Cargo Delivery, Multi-Agent Pursuit, Rendezvous, and Inverted Flying Pendulum both in simulation and experimentally. Additionally, PEARL is leveraged outside of robotics as an array sorting agent. The results demonstrate high accuracy and fast learning times on a large set of practical applications

    A Novel Semi-Active Suspension System for Automobiles Using Jerk-Driven Damper (JDD)

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    With the new advancements in the vibration control, the control strategies for the controllable semi-active dampers are finding their way as an essential part of vibration isolators, particularly in vehicle suspension systems. An analysis of frequency response for single degree of freedom (1DOF) system gives an attribute to the fact that in a semi-active suspension system, the damping coefficients can be adjusted to improve ride comfort and road handling performances. The systems study includes various type of semi-active suspension systems, employing nonlinear magnetorheological(MR) dampers that are controlled to provide improved vibration isolation. The currently available control strategies for semi-active dampers can be divided into two main groups. The first one is `On-Off' control and second one is `continuous' control of variable dampers. Available control strategies are either proportional to the relative velocity of sprung mass or the acceleration of sprung mass. A new control strategy which is proportional to the jerk produced in sprung mass called Jerk Driven Damper (JDD) is proposed and analyzed by the use of two state 'On-Off' damper. The control strategy for 'JDD' system is extremely simple and it involves very common logic. `JDD' system requires a two state controllable damper and jerk sensor. A brief study on controllable damper and jerk sensors are presented in this thesis

    Responsive Building Envelope for Grid-Interactive Efficient Buildings – Thermal Performance and Control

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    The building sector accounts for 30% of total energy consumption worldwide. Responsive building envelopes (or RBEs) are one of the approaches to achieving net-zero energy and grid-interactive efficient buildings. However, research and development of RBEs are still in the early stages of technologies, simulation, control, and design. The control strategies in prior studies did not fully explore the potential of RBEs or they obtained good performance with high design and deployment costs. A low-cost strategy that does not require knowledge of complex systems is needed, while no studies have investigated online implementations of model-free control approaches for RBEs. To address these challenges, this dissertation describes a multidisciplinary study of the modeling, control, and design of RBEs, to understand mechanisms governing their dynamic properties and synthesis rules of multiple technologies through simulation analyses. Widely applicable mathematical models are developed that can be easily extended for multiple RBE types with validation. Computational frameworks (or co-simulation testbeds) that flexibly integrate multiple control methods and building simulation models are established with higher computation efficiency than that using commercial software during offline training. To overcome the limitations of the control strategies (e.g., rule-based control and MPC) in prior research, a novel easy-to-implement yet flexible ‘demand-based’ control strategy, and model-free online control strategies using deep reinforced learning are proposed for RBEs composed of active insulation systems (AISs). Both the physics-derived and model-free control strategies fully leverage the advantages of AISs and provide higher energy savings and thermal comfort improvement over traditional temperature-based control methods in prior research and demand-based control. The case studies of RBEs that integrate AISs and high thermal mass or self-adaptive/active modules (e.g., evaporative cooling techniques and dynamic glazing/shading) demonstrate the superior performance of AISs in regulating thermal energy transfer to offset AC demands during the synergy. Moreover, the controller design and training implications are elaborated. The applicability assessment of promising RBE configurations is presented along with design implications based on building energy analyses in multiple scenarios. The design and control implications represent an interactive and holistic way to operate RBEs allowing energy and thermal comfort performances to be tuned for maximum efficiency

    Integrated control of vehicle chassis systems

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    This thesis develops a method to integrate several automotive intelligent chassis systems, such as Anti-lock Brake System, Traction Control System, Direct Yaw Control and Active Rear Wheel Steering, using evolutionary approaches. The Integrated Vehicle Control System (IVCS) combines and supervises all controllable systems in the vehicle, optimising the over all performance and minimising the energy consumption. The IVCS is able to improve the driving safety avoiding and preventing critical or unstable situations. Furthermore, if a critical or unstable configuration is reached, the integrated system should be able to recover a stable condition. The control structure proposed in this work has as main characteristics the modularity, extensibility and flexibility, fitting the requirements of a 'plug-and-play' philosophy. The investigation is divided into four steps: Vehicle Modelling, Soft-Computing, Behaviour Based Control, and Integrated Vehicle Control System. Several mathematical vehicle models, which are applied to designing and developing the control systems, are presented. MATLAB, SIMULINK and ADAMS are used as tools to implement and simulate those models. A methodology for learning and optimisation is presented. This methodology is based on Evolutionary Algorithms, integrating the Genetic Leaming Automata, CARLA and Fuzzy Logic System. The Behaviour Based Control is introduced as the main approach to designing the controllers and coordinators. The methodology previously described is used to learn the behaviours and optimise their performance, and the same technique is applied to coordinators. Several comparisons with other controllers are also carried out. From this an Integrated Vehicle Control System is designed, developed and implemented under a virtual environment. A range of manoeuvres is carried out in order to investigate its performance under diverse conditions. The leaming and optimisation method proposed in this thesis shows effective performance being able to learn all the controller and coordinator structures. The proposed approach for IVCS also demonstrates good performance, and is well suited to a 'plug-and-play' philosophy. This research provides a foundation for the implementation of the designed controllers and coordinators in a prototype vehicle.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Expanding the Horizons of Manufacturing: Towards Wide Integration, Smart Systems and Tools

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    This research topic aims at enterprise-wide modeling and optimization (EWMO) through the development and application of integrated modeling, simulation and optimization methodologies, and computer-aided tools for reliable and sustainable improvement opportunities within the entire manufacturing network (raw materials, production plants, distribution, retailers, and customers) and its components. This integrated approach incorporates information from the local primary control and supervisory modules into the scheduling/planning formulation. That makes it possible to dynamically react to incidents that occur in the network components at the appropriate decision-making level, requiring fewer resources, emitting less waste, and allowing for better responsiveness in changing market requirements and operational variations, reducing cost, waste, energy consumption and environmental impact, and increasing the benefits. More recently, the exploitation of new technology integration, such as through semantic models in formal knowledge models, allows for the capture and utilization of domain knowledge, human knowledge, and expert knowledge toward comprehensive intelligent management. Otherwise, the development of advanced technologies and tools, such as cyber-physical systems, the Internet of Things, the Industrial Internet of Things, Artificial Intelligence, Big Data, Cloud Computing, Blockchain, etc., have captured the attention of manufacturing enterprises toward intelligent manufacturing systems
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