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  • Presentation: 2017-09-08 10:15 Desi, A-huset, Linköping
    Larsson, L. Viktor
    Linköping University, Department of Management and Engineering, Fluid and Mechatronic Systems. Linköping University, Faculty of Science & Engineering.
    Control Aspects of ComplexHydromechanical Transmissions: with a Focus on Displacement Control2017Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis deals with control aspects of complex hydromechanical transmissions. The overall purpose is to increase the knowledge of important aspects to consider during the development of hydromechanical transmissions to ensure transmission functionality. These include ways of evaluating control strategies in early design stages as well as dynamic properties and control aspects of displacement controllers, which are key components in these systems.

    Fuel prices and environmental concerns are factors that drive research on propulsion in heavy construction machinery. Hydromechanical transmissions are strong competitors to conventional torque-converter transmissions used in this application today. They offer high efficiency and wide speed/torque conversion ranges, and may easily be converted to hybrids that allow further fuel savings through energy recuperation. One challenge with hydromechanical transmissions is that they offer many different configurations, which in turn makes it important to enable evaluation of control aspects in early design stages. In this thesis, hardware-in-the-loop simulations, which blend hardware tests and standard software-based simulations, are considered to be a suitable method. A multiple-mode transmission applied to a mid-sized construction machine is modelled and evaluated in offline simulations as well as in hardware-in-the-loopsimulations.

    Hydromechanical transmissions rely on efficient variable pumps/motors with fast, accurate displacement controllers. This thesis studies the dynamic behaviour of the displacement controller in swash-plate axial-piston pumps/motors. A novel control approach in which the displacement is measured with an external sensor is proposed. Performance and limitations of the approach are tested in simulations and in experiments. The experiments showed a significantly improved performance with a controller that is slightly more advanced than a standard proportional controller. The implementation of the controller allows simple tuning and good predictability of the displacement response.

    List of papers
    1. Simulation Aided Design and Testing of Hydromechanical Transmissions
    Open this publication in new window or tab >>Simulation Aided Design and Testing of Hydromechanical Transmissions
    2014 (English)In: The 9th JFPS International Symposium on Fluid Power, Matsue, 2014, 2014Conference paper, (Refereed)
    Abstract [en]

    This paper demonstrates the use of high-speed simulation in transmission conceptual design and presents a transmission test bed for hardware-in-the-loop simulations of hydromechanical transmission concepts. Complex transmissions, such as multiple-mode hydromechanical transmissions and hydraulic hybrid transmissions, present new difficulties and costs in the development process. There is today a greater demand for more efficient product development and more work has shifted towards simulation. The Hopsan simulation package allows robust, high-speed simulations suitable for both offline and hardware-in-the-loop simulation. New simulation models for hydromechanical transmissions are developed and used to simulate a known two-mode transmission concept. The same concept is also tested in hardware-in-the-loop simulations in the proposed transmission test bed. Results show good agreement with the hardware tests and highlight the proficiency of the simulation tools.

    Keyword
    Hydromechanical Transmission, Hardware-in-the-loop, Hopsan
    National Category
    Aerospace Engineering
    Identifiers
    urn:nbn:se:liu:diva-126561 (URN)4-931070-10-8 (ISBN)
    Conference
    the 9th JFPS International Symposium on Fluid Power in Matsue, Shimane Japan, on October 28-31, 2014
    Available from: 2016-03-30 Created: 2016-03-30 Last updated: 2017-08-17
    2. Mode Shifting in Hybrid Hydromechanical Transmissions
    Open this publication in new window or tab >>Mode Shifting in Hybrid Hydromechanical Transmissions
    2015 (English)In: ASME/BATH 2015 Symposium on Fluid Power and Motion Control, ASME Press, 2015, 13- p.Conference paper, (Refereed)
    Abstract [en]

    Demands for low cost sustainable solutions have increased the use of and interest in complex hydromechanical transmissions for heavy off-road vehicles. In transmissions with multiplemodes, an important condition is to maintain the tractive force during the mode shifting event. For hybrid hydromechanical transmissions, with a direct connection to a hydraulic accumulator, the impressed system pressure caused by the hydraulic accumulator has not yet been observed to interfere with this condition. In this paper, a black box model approach is used to modify the hydraulic system after obtaining knowledge regarding how it is affected by a mode shift. A comparative study is carried out where a full vehicle model of a mobile working machine is simulated with two different hydraulic systems. The results show that different system solutions imply different demands on the included components, and that the mode shifting event is not a negligible factor in heavy hydraulic hybrid vehicles.

    Place, publisher, year, edition, pages
    ASME Press, 2015
    Keyword
    Mode shifting, hydromechanical transmissions, fluid power, heavy construction machinery
    National Category
    Aerospace Engineering
    Identifiers
    urn:nbn:se:liu:diva-126556 (URN)10.1115/FPMC2015-9583 (DOI)000373970500045 ()978-0-7918-5723-6 (ISBN)
    Conference
    ASME/BATH 2015 Symposium on Fluid Power and Motion Control, Chicago, Illinois, USA, October 12–14, 2015
    Projects
    Research on Hydromechanical Transmissions and Hybrid Motion systems, RHYTHM
    Funder
    Swedish Energy Agency, P39367-1
    Available from: 2016-03-30 Created: 2016-03-30 Last updated: 2017-08-17
    3. Modelling of the Swash Plate Control Actuator in an Axial Piston Pump for a Hardware-In-the-Loop Simulation Test Rig
    Open this publication in new window or tab >>Modelling of the Swash Plate Control Actuator in an Axial Piston Pump for a Hardware-In-the-Loop Simulation Test Rig
    2017 (English)In: Proceedings of the 9th FPNI Ph.D. Symposium on Fluid Power, FPNI2016, ASME Press, 2017, UNSP V001T01A044Conference paper, (Refereed)
    Abstract [en]

    Hydraulic hybrid system solutions are promising in the quest for energy efficiency in heavy construction machines. Hardware-in-the-loop simulations, where hardware is included in software simulations in real time, may be used to facilitate the development process of these systems without the need to build expensive prototypes. In this paper, the displacement actuator of a prototype pump used in a hardware-in-the-loop simulation test rig is modelled and validated against hardware, in order to draw conclusions regarding its dynamic behaviour in a future control design. The results show that the dynamic response of the modelled displacement actuator is mainly determined by the system pressure as well as the response and geometry of the control valve.

    Place, publisher, year, edition, pages
    ASME Press, 2017
    National Category
    Mechanical Engineering
    Identifiers
    urn:nbn:se:liu:diva-133328 (URN)10.1115/FPNI2016-1570 (DOI)000398986900044 ()978-0-7918-5047-3 (ISBN)
    Conference
    The 9th FPNI Ph.D. Symposium on Fluid Power
    Projects
    Research on Hydromechanical Transmissions and Hybrid Motion systems, RHYTHM
    Funder
    Swedish Energy Agency, P39367-1
    Note

    The authors would like to thank the Swedish Energy Agency for contributing funds for the research project. Thanks also go to Bosch Rexroth for providing the prototype machines.

    Available from: 2016-12-20 Created: 2016-12-20 Last updated: 2017-08-17
    4. Displacement Control Strategies of an In-Line Axial-Piston Unit
    Open this publication in new window or tab >>Displacement Control Strategies of an In-Line Axial-Piston Unit
    2017 (English)In: The 15th Scandinavian International Conference on Fluid Power, SICFP’17 / [ed] Krus, Petter, Linköping, 2017Conference paper, (Refereed)
    Abstract [en]

    The need for efficient propulsion in heavy vehicles has led to an increased interest in hybrid solutions. Hydraulic hybrids rely on variable hydraulic pumps/motors to continuously convert between hydraulic and mechanical power. This process is carried out via the implementation of secondary control which, in turn, is dependent on a fast displacement controller response. This paper reports on a study of a prototype axial piston pump of the in-line type, in which the displacement is measured with a sensor and controlled using a software-based controller. A pole placement control approach is used, in which a simple model of the pump is used to parametrise the controller using desired resonance and damping of the closed loop controller as input. The controller’s performance is tested in simulations and hardware tests on the prototype unit. The results show that the pole placement approach combined with a lead-compensator controller architecture is flexible, easy to implement and is able to deliver a fast response with high damping. The results will in the future be used in further research on full-vehicle control of heavy hydraulic hybrids.

    Place, publisher, year, edition, pages
    Linköping: , 2017
    Keyword
    Hydraulic hybrids, displacement control, pole placement
    National Category
    Control Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering Vehicle Engineering Computer Vision and Robotics (Autonomous Systems) Embedded Systems
    Identifiers
    urn:nbn:se:liu:diva-139854 (URN)
    Conference
    The 15th Scandinavian International Conference on Fluid Power, SICFP’17, June 7-9, 2017, Linköping, Sweden
    Available from: 2017-08-17 Created: 2017-08-17 Last updated: 2017-08-17Bibliographically approved
  • Presentation: 2017-09-15 10:15 Alan Turing, E-huset, Linköping
    Andersson, Olov
    Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
    Methods for Scalable and Safe Robot Learning2017Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Robots are increasingly expected to go beyond controlled environments in laboratories and factories, to enter real-world public spaces and homes. However, robot behavior is still usually engineered for narrowly defined scenarios. To manually encode robot behavior that works within complex real world environments, such as busy work places or cluttered homes, can be a daunting task. In addition, such robots may require a high degree of autonomy to be practical, which imposes stringent requirements on safety and robustness. \setlength{\parindent}{2em}\setlength{\parskip}{0em}The aim of this thesis is to examine methods for automatically learning safe robot behavior, lowering the costs of synthesizing behavior for complex real-world situations. To avoid task-specific assumptions, we approach this from a data-driven machine learning perspective. The strength of machine learning is its generality, given sufficient data it can learn to approximate any task. However, being embodied agents in the real-world, robots pose a number of difficulties for machine learning. These include real-time requirements with limited computational resources, the cost and effort of operating and collecting data with real robots, as well as safety issues for both the robot and human bystanders.While machine learning is general by nature, overcoming the difficulties with real-world robots outlined above remains a challenge. In this thesis we look for a middle ground on robot learning, leveraging the strengths of both data-driven machine learning, as well as engineering techniques from robotics and control. This includes combing data-driven world models with fast techniques for planning motions under safety constraints, using machine learning to generalize such techniques to problems with high uncertainty, as well as using machine learning to find computationally efficient approximations for use on small embedded systems.We demonstrate such behavior synthesis techniques with real robots, solving a class of difficult dynamic collision avoidance problems under uncertainty, such as induced by the presence of humans without prior coordination. Initially using online planning offloaded to a desktop CPU, and ultimately as a deep neural network policy embedded on board a 7 quadcopter.

    List of papers
    1. Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization
    Open this publication in new window or tab >>Model-Based Reinforcement Learning in Continuous Environments Using Real-Time Constrained Optimization
    2015 (English)In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI) / [ed] Blai Bonet and Sven Koenig, AAAI Press, 2015, 2497-2503 p.Conference paper, (Refereed)
    Abstract [en]

    Reinforcement learning for robot control tasks in continuous environments is a challenging problem due to the dimensionality of the state and action spaces, time and resource costs for learning with a real robot as well as constraints imposed for its safe operation. In this paper we propose a model-based reinforcement learning approach for continuous environments with constraints. The approach combines model-based reinforcement learning with recent advances in approximate optimal control. This results in a bounded-rationality agent that makes decisions in real-time by efficiently solving a sequence of constrained optimization problems on learned sparse Gaussian process models. Such a combination has several advantages. No high-dimensional policy needs to be computed or stored while the learning problem often reduces to a set of lower-dimensional models of the dynamics. In addition, hard constraints can easily be included and objectives can also be changed in real-time to allow for multiple or dynamic tasks. The efficacy of the approach is demonstrated on both an extended cart pole domain and a challenging quadcopter navigation task using real data.

    Place, publisher, year, edition, pages
    AAAI Press, 2015
    Keyword
    Reinforcement Learning, Gaussian Processes, Optimization, Robotics
    National Category
    Computer Science Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:liu:diva-113385 (URN)978-1-57735-698-1 (ISBN)
    Conference
    Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), January 25-30, 2015, Austin, Texas, USA.
    Funder
    Linnaeus research environment CADICSeLLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsSwedish Foundation for Strategic Research VINNOVAEU, FP7, Seventh Framework Programme
    Available from: 2015-01-16 Created: 2015-01-16 Last updated: 2017-08-16Bibliographically approved
    2. Model-Predictive Control with Stochastic Collision Avoidance using Bayesian Policy Optimization
    Open this publication in new window or tab >>Model-Predictive Control with Stochastic Collision Avoidance using Bayesian Policy Optimization
    2016 (English)In: IEEE International Conference on Robotics and Automation (ICRA), 2016, Institute of Electrical and Electronics Engineers (IEEE), 2016, 4597-4604 p.Conference paper, (Refereed)
    Abstract [en]

    Robots are increasingly expected to move out of the controlled environment of research labs and into populated streets and workplaces. Collision avoidance in such cluttered and dynamic environments is of increasing importance as robots gain more autonomy. However, efficient avoidance is fundamentally difficult since computing safe trajectories may require considering both dynamics and uncertainty. While heuristics are often used in practice, we take a holistic stochastic trajectory optimization perspective that merges both collision avoidance and control. We examine dynamic obstacles moving without prior coordination, like pedestrians or vehicles. We find that common stochastic simplifications lead to poor approximations when obstacle behavior is difficult to predict. We instead compute efficient approximations by drawing upon techniques from machine learning. We propose to combine policy search with model-predictive control. This allows us to use recent fast constrained model-predictive control solvers, while gaining the stochastic properties of policy-based methods. We exploit recent advances in Bayesian optimization to efficiently solve the resulting probabilistically-constrained policy optimization problems. Finally, we present a real-time implementation of an obstacle avoiding controller for a quadcopter. We demonstrate the results in simulation as well as with real flight experiments.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2016
    Series
    Proceedings of IEEE International Conference on Robotics and Automation, ISSN 1050-4729
    Keyword
    Robot Learning, Collision Avoidance, Robotics, Bayesian Optimization, Model Predictive Control
    National Category
    Robotics Computer Science
    Identifiers
    urn:nbn:se:liu:diva-126769 (URN)10.1109/ICRA.2016.7487661 (DOI)000389516203138 ()
    Conference
    IEEE International Conference on Robotics and Automation (ICRA), 2016, Stockholm, May 16-21
    Projects
    CADICSELLIITNFFP6CUASSHERPA
    Funder
    Linnaeus research environment CADICSELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeSwedish Foundation for Strategic Research
    Available from: 2016-04-04 Created: 2016-04-04 Last updated: 2017-08-16Bibliographically approved
    3. Deep Learning Quadcopter Control via Risk-Aware Active Learning
    Open this publication in new window or tab >>Deep Learning Quadcopter Control via Risk-Aware Active Learning
    2017 (English)In: Proceedings of The Thirty-first AAAI Conference on Artificial Intellegence and the twenty-ninth innovative Applications of artificial Intelligence Conference, 2017, 2017Conference paper, (Refereed)
    Abstract [en]

    Modern optimization-based approaches to control increasingly allow automatic generation of complex behavior from only a model and an objective. Recent years has seen growing interest in fast solvers to also allow real-time operation on robots, but the computational cost of such trajectory optimization remains prohibitive for many applications. In this paper we examine a novel deep neural network approximation and validate it on a safe navigation problem with a real nano-quadcopter. As the risk of costly failures is a major concern with real robots, we propose a risk-aware resampling technique. Contrary to prior work this active learning approach is easy to use with existing solvers for trajectory optimization, as well as deep learning. We demonstrate the efficacy of the approach on a difficult collision avoidance problem with non-cooperative moving obstacles. Our findings indicate that the resulting neural network approximations are least 50 times faster than the trajectory optimizer while still satisfying the safety requirements. We demonstrate the potential of the approach by implementing a synthesized deep neural network policy on the nano-quadcopter microcontroller.

    National Category
    Computer Vision and Robotics (Autonomous Systems) Computer Science
    Identifiers
    urn:nbn:se:liu:diva-132800 (URN)
    Conference
    Thirty-First AAAI Conference on Artificial Intelligence (AAAI), 2017, San Francisco, February 4–9.
    Projects
    ELLIITCADICSNFFP6SYMBICLOUDCUGS
    Funder
    Linnaeus research environment CADICSELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsEU, FP7, Seventh Framework ProgrammeCUGS (National Graduate School in Computer Science)Swedish Foundation for Strategic Research
    Available from: 2016-11-25 Created: 2016-11-25 Last updated: 2017-08-17Bibliographically approved