09. Konferenssit, seminaarit ja kokoomateokset / Conferences, Seminars and Compiled works

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https://aaltodoc.aalto.fi/handle/123456789/15
Yliopiston järjestämien konferenssien kokoomateoksia / Conference proceedings of the university's events

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Browsing 09. Konferenssit, seminaarit ja kokoomateokset / Conferences, Seminars and Compiled works by Department "Department of Biotechnology and Chemical Technology"

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    Discrete-time adaptive learning control for parametric uncertainties with unknown periods
    (Institute of Electrical & Electronics Engineers (IEEE), 2013) Miao Yu; Deqing Huang; Biotekniikan ja kemian tekniikan laitos; Department of Biotechnology and Chemical Technology; Research Group of Process Control and Automation; Kemian tekniikan korkeakoulu; School of Chemical Technology
    In this paper, we approach the problem of unknown periods for a class of discrete-time parametric nonlinear systems with nonlinearities which do not necessarily satisfy the sector-bounded condition. The unknown periods hide in the parametric uncertainties, which is difficult to estimate. By incorporating a logic-based switching mechanism, we estimate the period and bound of unknown parameter simultaneously under Lyapunov-based analysis. Rigorous proof is given to demonstrate that a finite number of switchings can guarantee the asymptotic regulation of the nonlinear system considered. The simulation result also shows the efficacy of the proposed switching periodic adaptive control method.
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    A dynamic prognosis algorithm in distributed fault tolerant model predictive control
    (Institute of Electrical & Electronics Engineers (IEEE), 2014) Zakharov, Alexey; Yu, Miao; Jamsa-Jounela, Sirkka-Liisa; Biotekniikan ja kemian tekniikan laitos; Department of Biotechnology and Chemical Technology; Research Group of Process Control and Automation; Kemian tekniikan korkeakoulu; School of Chemical Technology
    This paper presents a dynamic prognosis algorithm in distributed fault tolerant model predictive control (DFTMPC). The dynamic prognosis, which means predicting the trajectories of process variables under distributed model predictive control, is performed when a fault is diagnosed and several candidate reconfigured controls are proposed. Then, the dynamic prognosis is utilized to check whether the candidate reconfigured controls are able to drive the system to the new operating conditions and to evaluate the performance during the transition period. Thus, the most suitable candidate reconfigured controller is selected and its feasibility is ensured without using a Lyapunov function that is difficult to obtain for large-scale systems. On the other hand, the on-line computation burden of the prognosis algorithm is moderate under the assumption that the sets of active constraints in non-faulty subsystems remain the same as they are at the nominal operating conditions. Thus, the dynamic prognosis for DMPC is aimed to improve the applicability of the existing fault tolerant methods to large-scale systems.