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3rd Modeling, Estimation and control Conference (MECC)

作者： Snider, Elias B. Bristow, Douglas A. Missouri Univ Sci & Technol Dept Mech & Aerosp Engn Rolla MO 65401 USA

ISBN: (纸本)9781713872344

Iterative learning control (ILC) and repetitive process control (RPC) are layer-to -layer corrective schemes which are particularly well-suited for additive manufacturing (AM). Direct energy deposition (DED) processes have unique error-propagation dynamics which are best compensated for with RPC structures. Frequently in DED builds, a non-constant reference toolpath is required for geometrical constraints (e.g. rocket nozzles, other aerospace applications);however, current control structures are usually limited to constant toolpaths. This work explores an adaptation to an existing interpolating RPC structure capable of accommodating a broader range of non-constant reference toolpaths with greater stability. Modified equations are developed for this structure and resulting specimens are presented. Specimens are compared to previous interpolating RPC implementations and performance metrics are discussed. Further applications for advancement are explored.

关键词： repetitive process control Iterative Learning control Direct Energy Deposition Additive Manufacturing Layer-to -Layer control

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repetitive process control Interpolation for Varied Thin-Wall Path Lengths in DED processes

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IFAC-PapersOnLine 2023年第3期56卷 1-6页

作者： Elias B. Snider Douglas A. Bristow Department of Mechanical and Aerospace Engineering Missouri University of Science and Technology Rolla MO 65401 USA

Iterative learning control (ILC) and repetitive process control (RPC) are layer-to-layer corrective schemes which are particularly well-suited for additive manufacturing (AM). Direct energy deposition (DED) processes have unique error-propagation dynamics which are best compensated for with RPC structures. Frequently in DED builds, a non-constant reference toolpath is required for geometrical constraints (e.g. rocket nozzles, other aerospace applications); however, current control structures are usually limited to constant toolpaths. This work explores an adaptation to an existing interpolating RPC structure capable of accommodating a broader range of non-constant reference toolpaths with greater stability. Modified equations are developed for this structure and resulting specimens are presented. Specimens are compared to previous interpolating RPC implementations and performance metrics are discussed. Further applications for advancement are explored.

关键词： repetitive process control Iterative Learning control Direct Energy Deposition Additive Manufacturing Layer-to-Layer control

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Particle swarm optimization of the state feedback current controller with oscillatory terms for a three-phase grid-tie converter 20

Particle swarm optimization of the state feedback current co...

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20th European Conference on Power Electronics and Applications (EPE ECCE Europe)

作者： Galecki, Andrzej Michalczuk, Marek Ufnalski, Bartlomiej Grzesiak, Lech M. Warsaw Univ Technol Inst Control & Ind Elect Fac Elect Engn 75 Koszykowa St PL-00662 Warsaw Poland

ISBN: (纸本)9789075815283

The paper presents a feedback current controller with oscillatory terms which has been designed for a three-phase grid-tie voltage source converters (VSCs) operated under distorted grid voltage conditions. The linear quadratic regulator (LQR) or the pole placement technique are typically used for designing this type of multi-variable control systems. However, both of them usually require a lot of expert knowledge. The particle swarm optimization (PSO) is proposed for off-line tuning of the multi-oscillatory current controller. The entries of the state feedback gain matrix are determined in the optimization process. The PSO tuning process is carried out using a linear model of the system in order to reduce the optimization time. Finally, the numerical verification is performed based on a switching model of the converter including pulse width modulation.

关键词： grid-tie converter repetitive process control multi-resonant control repetitive disturbance rejection particle swarm optimization power quality state feedback controller

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20th European Conference on Power Electronics and Applications (EPE ECCE Europe)

作者： Ufnalski, Bartlomiej Galecki, Andrzej Kaszewski, Arkadiusz Grzesiak, Lech Warsaw Univ Technol Inst Control & Ind Elect Fac Elect Engn 75 Koszykowa St PL-00662 Warsaw Poland

ISBN: (纸本)9789075815283

There is an ongoing discussion among repetitive process control engineers whether to use multiresonant controllers or iterative learning ones. Power electronics engineers are often in favour of multiresonant controllers whereas motion control designers more often incorporate iterative learning controllers (ILC) into their systems. Both camps of researchers seem to be entrenched and rarely willing to admit that both techniques are capable of introducing exactly the same generating polynomial to the system. Moreover, both techniques in their basic forms suffer from a lack of robustness to a persevering non-zero control error, e.g. due to the physical limitations on the plant side. To render the techniques practical, several tweaks have to be made. Neither of them is also innately immune to the reference and/or disturbance signal frequency fluctuations. Even small frequency variations at the level of 1% can render the basic ILC impractical in the case of the grid-tied converter discussed in this paper. The paper is supposed to serve as the second chapter of the conversational guide on the similarity and challenges of multiresonant and iterative learning controllers. Alongside sparking the discussion, our main contribution here is a novel conditional learning concept within the ILC, inspired by the variable damping proposed for multiresonant controllers. An improvement gained by augmenting the basic ILC with the conditional learning is illustrated based on numerical simulations. Further possibilities of introducing an adaptation algorithm into the modified controller are suggested.

关键词： repetitive process control multiresonant control iterative learning control repetitive disturbance rejection grid tie converter frequency fluctuations adaptive fractional delay filter adaptive resonant term conditional learning

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Particle swarm optimization of the state feedback current controller with oscillatory terms for a three-phase grid-tie converter

Particle swarm optimization of the state feedback current co...

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European Conference on Power Electronics and Applications

作者： Andrzej Galecki Marek Michalczuk Bartlomiej Ufnalski Lech M. Grzesiak Faculty of Electrical Engineering Warsaw University of Technology Warsaw Poland

关键词： grid-tie converter repetitive process control multi-resonant control repetitive disturbance rejection particle swarm optimization power quality state feedback controller

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European Conference on Power Electronics and Applications

作者： Bartlomiej UFNALSKI Andrzej GALECKI Arkadiusz KASZEWSKI Lech GRZESIAK Faculty of Electrical Engineering Warsaw University of Technology Poland

关键词： repetitive process control multiresonant control iterative learning control repetitive disturbance rejection grid-tie converter frequency fluctuations adaptive fractional delay filter adaptive resonant term conditional learning

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Plug-in direct particle swarm repetitive controller with a reduced dimensionality of a fitness landscape - a multi-swarm approach

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2015年第4期63卷 857-866页

作者： Ufnalski, B. Grzesiak, L. M. Warsaw Univ Technol Fac Elect Engn 75 Koszykowa St PL-00662 Warsaw Poland

The paper describes a modification to the recently developed plug-in direct particle swarm repetitive controller (PDPSRC) for the sine-wave constant-amplitude constant-frequency (CACF) voltage-source inverter (VSI). The original PDPSRC algorithm assumes that the particle swarm optimizer (PSO) takes into account a performance index defined over the whole reference signal period. Each particle stores all the samples of the control signal, e.g. alpha = 200 samples for a controller working at 10 kHz and the reference frequency equal to 50 Hz. Therefore, the fitness landscape (i.e. the performance index) is alpha-dimensional (alpha D), which makes optimization challenging. That solution can be categorized as the single-swarm one. It has been previously shown that the swarm controller does not suffer from long-term stability issues encountered in the classic iterative learning controllers (ILC). However, the convergence of the swarm has to be kept at a relatively low rate to enable successful exploitation in the alpha D search space, which in turn results in slow responsiveness of the PDPSRC. Here a multi-swarm approach is proposed in which we divide a dynamic optimization problem (DOP) among less dimensional swarms. The reference signal period is segmented into shorter intervals and the control signal is optimized in each interval independently by separate swarms. The effectiveness of the proposed approach is illustrated with the help of numerical experiments on the CACF VSI with an output LC filter operating under nonlinear loads.

关键词： repetitive process control dynamic optimization problem particle swarm optimizer repetitive disturbance rejection non-interacting subswarms dimension-reduced fitness functional

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A performance study on synchronous and asynchronous update rules for a plug-in direct particle swarm repetitive controller

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ARCHIVES OF ELECTRICAL ENGINEERING 2014年第4期63卷 635-646页

作者： Ufnalski, Bartlomiej Grzesiak, Lech M. Warsaw Univ Technol Fac Elect Engn Inst Control & Ind Elect Koszykowa 75 PL-00662 Warsaw Poland

In this paper two different update schemes for the recently developed plug-in direct particle swarm repetitive controller (PDPSRC) are investigated and compared. The proposed approach employs the particle swarm optimizer (PSO) to solve in on-line mode a dynamic optimization problem (DOP) related to the control task in the constant-amplitude constant-frequency voltage-source inverter (CACF VSI) with an LC output filter. The effectiveness of synchronous and asynchronous update rules, both commonly used in static optimization problems (SOPs), is assessed and compared in the case of PDPSRC. The performance of the controller, when synthesized using each of the update schemes, is studied numerically.

关键词： repetitive process control particle swarm optimization synchronous and asynchronous update rules dynamic optimization problem repetitive disturbance rejection optimal control

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