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    Optimal control of impulsive switched systems with minimum subsystem durations

    253526.pdf (202.6Kb)
    Access Status
    Open access
    Authors
    Blanchard, E.
    Loxton, Ryan
    Rehbock, Volker
    Date
    2013
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Blanchard, E. and Loxton, R. and Rehbock, V. 2014. Optimal control of impulsive switched systems with minimum subsystem durations. Journal of Global Optimization. 60 (4): pp. 737-750.
    Source Title
    Journal of Global Optimization
    DOI
    10.1007/s10898-013-0109-3
    ISSN
    0925-5001
    School
    Department of Mathematics and Statistics
    Remarks

    The final publication is available at Springer via 10.1007/s10898-013-0109-3

    URI
    http://hdl.handle.net/20.500.11937/54856
    Collection
    • Curtin Research Publications
    Abstract

    This paper presents a new computational approach for solving optimal control problems governed by impulsive switched systems. Such systems consist of multiple subsystems operating in succession, with possible instantaneous state jumps occurring when the system switches from one subsystem to another. The control variables are the subsystem durations and a set of system parameters influencing the state jumps. In contrast with most other papers on the control of impulsive switched systems, we do not require every potential subsystem to be active during the time horizon (it may be optimal to delete certain subsystems, especially when the optimal number of switches is unknown). However, any active subsystem must be active for a minimum non-negligible duration of time. This restriction leads to a disjoint feasible region for the subsystem durations. The problem of choosing the subsystem durations and the system parameters to minimize a given cost function is a non-standard optimal control problem that cannot be solved using conventional techniques. By combining a time-scaling transformation and an exact penalty method, we develop a computational algorithm for solving this problem. We then demonstrate the effectiveness of this algorithm by considering a numerical example on the optimization of shrimp harvesting operations.

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