An Approach to State Signal Shaping by Limit Cycle Model Predictive Control

A novel nonlinear model predictive control approach for state signal shaping is proposed. The control strategy introduces a residual shape cost kernel based on the dynamics of circular limit cycles from a supercritical Neimark-Sacker bifurcation normal form. This allows the controller to impose a fundamental harmonic state signal shape with a specific frequency and amplitude. An application example for harmonic compensation in distribution grids integrated with renewable energies is presented. The controller is tasked with the calculation of the reference current for an active power filter used for load compensation. The results achieved are successful, reducing the harmonic distortion to satisfactory levels while ensuring the correct frequency and amplitude.Comment: \copyright 2020 Carlos Cateriano Y\'a\~nez, Gerwald Lichtenberg, Georg Pangalos and Javier Sanchis S\'aez. This work has been accepted to IFAC for publication under a Creative Commons Licence CC-BY-NC-N

Efficient linearization of explicit multilinear systems using normalized decomposed tensors

Multilinear systems allow multiplications of states, inputs, and states with inputs, in all possible combinations. Recently, a new normalized decomposed tensor format of explicit multilinear models was introduced. This paper presents a linearization method for the normalized canonical polyadic decomposed tensor format of explicit multilinear models. The proposed method computes the Jacobian matrix to obtain the linear system evaluated at the equilibrium point. An adaption for large-scale sparse systems is outlined. Performance and computational time are evaluated for different number of states and sparsity structures. The results suggest computational advantages of the explicit multilinear format compared to the non-normalized one. The adaptation to large-scale sparse systems shows clear computational advantage.Peer ReviewedPostprint (published version

Active Power Filter Shape Class Model Predictive Controller tuning by Multiobjective Optimization

[EN] In order to compensate the power quality issues that arise in distribution grids with high penetration of renewable energy sources, an active power filter device controlled by a novel model-based predictive controller, i.e. the linear state signal shaping model predictive controller, is implemented. This paper proposes the use of a Multiobjective Optimization evolutionary algorithm, i.e. the Multiobjective Differential Evolution with Spherical Pruning X, for the tuning of this novel controller. An application example for power quality compensation of a grid modeled as a switched system with four modes is given. The model includes nonlinear loads that introduce harmonic distortion and multiple consumer loads that enable the existence of conflicting objectives, typical of multiobjective optimization problems. A decision making strategy is developed in order to find the best controller parameters in a reasonable amount of time that enable the provision of optimal power quality services by balancing multiple objectives that can conflict with each other.This contribution was partly developed within the project NEW 4.0 (North German Energy Transition 4.0) which is funded by the German Federal Ministry for Economic Affairs and Energy (BMWI). This paper was also partly funded by the Free and Hanseatic City of Hamburg (Hamburg City Parliament publication 20/11568).Cateriano Yáñez, C.; Richter, J.; Pangalos, G.; Lichtenberg, G.; Sanchís Saez, J. (2019). Active Power Filter Shape Class Model Predictive Controller tuning by Multiobjective Optimization. En Proceedings 5th CARPE Conference: Horizon Europe and beyond. Editorial Universitat Politècnica de València. 79-86. https://doi.org/10.4995/CARPE2019.2019.10166OCS798

Computing Normal Forms with MAPLE

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Haptische Steuerung eines Operationsmikroskops

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Hybrid Tensor Systems

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