1,721,627 research outputs found
Adjoint-based model predictive control of wind farms: Beyond the quasi steady-state power maximization
No full textIn this paper, we extend our closed-loop optimal control framework for wind farms to minimize wake-induced power losses. We develop an adjoint-based model predictive controller which employs a medium-fidelity 2D dynamic wind farm model. The wind turbine axial induction factors are considered here as the control inputs to influence the overall performance by taking wake interactions of the wind turbines into account. A constrained optimization problem is formulated to maximize the total power production of a given wind farm. An adjoint approach as an efficient tool is utilized to compute the gradient for such a large-scale system. The computed gradient is then modified to deal with the defined final set and practical constraints on the wind turbine control inputs. The performance of the wind farm controller is examined for a more realistic test case, a layout of a 2 x 3 wind farm with dynamical changes in wind direction. The effectiveness of the proposed approach is studied through simulations.Team Jan-Willem van Wingerde
An adaptive approach to cooperative longitudinal platooning of heterogeneous vehicles with communication losses
No full textDespite the progresses in Cooperative Adaptive Cruise Control (CACC), a crucial limitation of the state-of-the-art of this control scheme is that the string stability of the platoon can be proven only when the vehicles in the platoon have identical driveline dynamics (homogeneous platoons). In this paper, we present a novel control strategy that overcomes the homogeneity assumption and that is able to adapt its action and achieve string stability even with uncertain heterogeneous platoons with unknown engine performance losses and inevitable communication losses. Considering a one-vehicle look-ahead topology, we propose an adaptive switched control strategy: the control objective is to switch from an augmented CACC to an augmented Adaptive Cruise Control strategy when communication is lost based on a dwell time characterized switching law. The simulation of the proposed control strategy is conducted to validate the theoretical analysis.Team Bart De Schutte
Input selection in N2SID using group lasso regularization
No full textInput selection is an important and oftentimes difficult challenge in system identification. In order to achieve less complex models, irrelevant inputs should be methodically and correctly discarded before or under the estimation process. In this paper we introduce a novel method of input selection that is carried out as a natural extension in a subspace method. We show that the method robustly and accurately performs input selection at various noise levels and that it provides good model estimates.Team Raf Van de Pla
Real-time performance and safety validation of an integrated vehicle dynamic control strategy
No full textThe state of the art in automotive control has proposed several analytical, simulation and experimental studies of longitudinal adaptive cruise control strategies, and of lateral control strategies. However, methodical integration of these two strategies is to a large extent missing, as well as validation in real-time computing environment of the safety and performance of longitudinal and lateral integrated solutions. This work proposes a real-time validation of an integrated vehicle dynamic control strategy, designed to create safe interaction between longitudinal and lateral controllers: the integrated system is designed, implemented and tested through Dynacar, a real-time simulation environment for the development and validation of vehicle embedded functionalities. The results show that the proposed integrated controller satisfies the performance in terms of real-time computation, path tracking and collision avoidance for various driving situations.Team Bart De Schutte
Kronecker-ARX models in identifying (2D) spatial-temporal systems
No full textIn this paper we address the identification of (2D) spatial-temporal dynamical systems governed by the Vector Auto-Regressive (VAR) form. The coefficient-matrices of the VAR model are parametrized as sums of Kronecker products. When the number of terms in the sum is small compared to the size of the matrix, such a Kronecker representation leads to high data compression. Estimating in least-squares sense the coefficient-matrices gives rise to a bilinear estimation problem, which is tackled using a three-stage algorithm. A numerical example demonstrates the advantages of the new modeling paradigm. It leads to comparable performances with the unstructured least-squares estimation of VAR models. However, the number of parameters in the new modeling paradigm grows linearly w.r.t. the number of nodes in the 2D sensor network instead of quadratically in the full unstructured matrix case.Team Raf Van de Pla
Recursive nuclear norm based subspace identification
No full textNuclear norm based subspace identification methods have recently gained importance due to their ability to find low rank solutions while maintaining accuracy through convex optimization. However, their heavy computational burden typically precludes the use in an online, recursive manner, such as may be required for adaptive control. This paper deals with the formulation of a recursive version of a nuclear norm based subspace identification method with an emphasis on reducing the computational complexity. The developed methodology is analyzed through simulations on Linear Time-Varying (LTV) systems particularly in terms of convergence rate, tracking speed and the accuracy of identification and it is shown to be computationally lighter and effective for such systems, with the considered rate of change of dynamics.Team Raf Van de PlasTeam Jan-Willem van Wingerde
A Jacobi decomposition algorithm for distributed convex optimization in Distributed Model Predictive Control
No full textIn this paper we introduce an iterative distributed Jacobi algorithm for solving convex optimization problems, which is motivated by distributed model predictive control (MPC) for linear time-invariant systems. Starting from a given feasible initial guess, the algorithm iteratively improves the value of the cost function with guaranteed feasible solutions at every iteration step, and is thus suitable for MPC applications in which hard constraints are important. The proposed iterative approach involves solving local optimization problems consisting of only few subsystems, depending on the flexible choice of decomposition and the sparsity structure of the couplings. This makes our approach more applicable to situations where the number of subsystems is large, the coupling is sparse, and local communication is available. We also provide a method for checking a posteriori centralized optimality of the converging solution, using comparison between Lagrange multipliers of the local problems. Furthermore, a theoretical result on convergence to optimality for a particular distributed setting is also provided.Team Tamas KeviczkyTeam Bart De Schutte
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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