[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Klöpper et al., 2012 - Google Patents

Probabilistic planning for predictive condition monitoring and adaptation within the self-optimizing energy management of an autonomous railway vehicle

Klöpper et al., 2012

View PDF
Document ID
1792637791662630231
Author
Klöpper B
Sondermann-Wölke C
Romaus C
Publication year
Publication venue
Journal of Robotics and Mechatronics

External Links

Snippet

Self-optimizing mechatronic systems are a new class of technical systems. On the one hand, new challenges regarding dependability arise from their additional complexity and adaptivity. On the other hand, their abilities enable new concepts and methods to improve …
Continue reading at pdfs.semanticscholar.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric

Similar Documents

Publication Publication Date Title
Kandidayeni et al. An online energy management strategy for a fuel cell/battery vehicle considering the driving pattern and performance drift impacts
Seybold et al. Towards robust predictive fault-tolerant control for a battery assembly system
Romaus et al. Optimal energy management for a hybrid energy storage system combining batteries and double layer capacitors
Zhao et al. A hybrid particle swarm optimisation algorithm and fuzzy logic for process planning and production scheduling integration in holonic manufacturing systems
Klöpper et al. Probabilistic planning for predictive condition monitoring and adaptation within the self-optimizing energy management of an autonomous railway vehicle
Hoang et al. Infinite-horizon proactive dynamic DCOPs
Patriksson et al. The stochastic opportunistic replacement problem, part II: a two-stage solution approach
Skima et al. Post-prognostics decision making in distributed MEMS-based systems
Choo et al. Adaptive multi-scale prognostics and health management for smart manufacturing systems
Bougacha et al. A review of post-prognostics decision-making in prognostics and health management
Tian et al. A prescriptive Dirichlet power allocation policy with deep reinforcement learning
Naderi et al. A machine learning-based framework for fast prediction of wide-area remedial control actions in interconnected power systems
Zhang et al. A digital twin-driven flexible scheduling method in a human–machine collaborative workshop based on hierarchical reinforcement learning
Zhu et al. Modeling and algorithm for resource-constrained multi-project scheduling problem based on detection and rework
Bole et al. Adaptive load-allocation for prognosis-based risk management
M. Ali et al. Realtime power management of a multi-source HEV using adaptive dynamic programing and probabilistic drive state model
Sharif et al. Context aware-resource optimality in electric vehicle Smart2Charge application: A deep reinforcement learning-based approach
Morse et al. A fuzzy approach to qualification in design exploration for autonomous robots and systems
Klopper et al. Probabilistic planning integrated in a multi-level dependability concept for mechatronic systems
Wilson et al. Nonlinear Power Flow Control Design Methodology for Navy Electric Ship Microgrid Energy Storage Requirements.
Meyer Optimization-based reliability control of mechatronic systems
Klöpper et al. Planning for mechatronics systems—Architecture, methods and case study
Esau et al. Hierarchical hybrid planning for a self-optimizing active suspension system
Timilsina et al. A real-time degradation abatement technique in hybrid electric vehicle using data-driven methods
Stetter et al. Prediction of the remaining useful life for components of automated processes