3,023 research outputs found
Towards parallelizable sampling-based Nonlinear Model Predictive Control
This paper proposes a new sampling-based nonlinear model predictive control
(MPC) algorithm, with a bound on complexity quadratic in the prediction horizon
N and linear in the number of samples. The idea of the proposed algorithm is to
use the sequence of predicted inputs from the previous time step as a warm
start, and to iteratively update this sequence by changing its elements one by
one, starting from the last predicted input and ending with the first predicted
input. This strategy, which resembles the dynamic programming principle, allows
for parallelization up to a certain level and yields a suboptimal nonlinear MPC
algorithm with guaranteed recursive feasibility, stability and improved cost
function at every iteration, which is suitable for real-time implementation.
The complexity of the algorithm per each time step in the prediction horizon
depends only on the horizon, the number of samples and parallel threads, and it
is independent of the measured system state. Comparisons with the fmincon
nonlinear optimization solver on benchmark examples indicate that as the
simulation time progresses, the proposed algorithm converges rapidly to the
"optimal" solution, even when using a small number of samples.Comment: 9 pages, 9 pictures, submitted to IFAC World Congress 201
Dual Maxwellian-Kappa modelling of the solar wind electrons: new clues on the temperature of Kappa populations
Context. Recent studies on Kappa distribution functions invoked in space
plasma applications have emphasized two alternative approaches which may assume
the temperature parameter either dependent or independent of the power-index
. Each of them can obtain justification in different scenarios
involving Kappa-distributed plasmas, but direct evidences supporting any of
these two alternatives with measurements from laboratory or natural plasmas are
not available yet. Aims. This paper aims to provide more facts on this
intriguing issue from direct fitting measurements of suprathermal electron
populations present in the solar wind, as well as from their destabilizing
effects predicted by these two alternating approaches. Methods. Two fitting
models are contrasted, namely, the global Kappa and the dual Maxwellian-Kappa
models, which are currently invoked in theory and observations. The
destabilizing effects of suprathermal electrons are characterized on the basis
of a kinetic approach which accounts for the microscopic details of the
velocity distribution. Results. In order to be relevant, the model is chosen to
accurately reproduce the observed distributions and this is achieved by a dual
Maxwellian-Kappa distribution function. A statistical survey indicates a
-dependent temperature of the suprathermal (halo) electrons for any
heliocentric distance. Only for this approach the instabilities driven by the
temperature anisotropy are found to be systematically stimulated by the
abundance of suprathermal populations, i.e., lowering the values of
-index.Comment: Submitted to A&
Handbook of linear data-driven predictive control:Theory, implementation and design
Data-driven predictive control (DPC) has gained an increased interest as an alternative to model predictive control in recent years, since it requires less system knowledge for implementation and reliable data is commonly available in smart engineering systems. Several data-driven predictive control algorithms have been developed recently, which largely follow similar approaches, but with specific formulations and tuning parameters. This review aims to provide a structured and accessible guide on linear data-driven predictive control methods and practices for people in both academia and the industry seeking to approach and explore this field. To do so, we first discuss standard methods, such as subspace predictive control (SPC), and data-enabled predictive control (DeePC), but we also include newer hybrid approaches to DPC, such as γ–data-driven predictive control and generalized data-driven predictive control. For all presented data-driven predictive controllers we provide a detailed analysis regarding the underlying theory, implementation details and design guidelines, including an overview of methods to guarantee closed-loop stability and promising extensions towards handling nonlinear systems. The performance of the reviewed DPC approaches is compared via simulations on two benchmark examples from the literature, allowing us to provide a comprehensive overview of the different techniques in the presence of noisy data.</p
Electromagnetic cyclotron instabilities in bi-Kappa distributed plasmas : a quasilinear approach
Anisotropic bi-Kappa distributed plasmas, as encountered in the solar wind and planetary magnetospheres,are susceptible to a variety of kinetic instabilities including the cyclotron instabilities driven by an excess ofperpendicular temperature T⊥ > T∥ (where ∥, ⊥ denote directions relative to the mean magnetic field). Theseinstabilities have been extensively investigated in the past, mainly limiting to a linear stability analysis. Abouttheir quasilinear (weakly nonlinear) development some insights have been revealed by numerical simulationsusing PIC and Vlasov solvers. This paper presents a self-consistent analytical approach, which provides forboth the electron and proton cyclotron instabilities an extended picture of the quasilinear time evolution ofthe anisotropic temperatures as well as the wave energy densities
Handbook of linear data-driven predictive control:Theory, implementation and design
Data-driven predictive control (DPC) has gained an increased interest as an alternative to model predictive control in recent years, since it requires less system knowledge for implementation and reliable data is commonly available in smart engineering systems. Several data-driven predictive control algorithms have been developed recently, which largely follow similar approaches, but with specific formulations and tuning parameters. This review aims to provide a structured and accessible guide on linear data-driven predictive control methods and practices for people in both academia and the industry seeking to approach and explore this field. To do so, we first discuss standard methods, such as subspace predictive control (SPC), and data-enabled predictive control (DeePC), but we also include newer hybrid approaches to DPC, such as γ–data-driven predictive control and generalized data-driven predictive control. For all presented data-driven predictive controllers we provide a detailed analysis regarding the underlying theory, implementation details and design guidelines, including an overview of methods to guarantee closed-loop stability and promising extensions towards handling nonlinear systems. The performance of the reviewed DPC approaches is compared via simulations on two benchmark examples from the literature, allowing us to provide a comprehensive overview of the different techniques in the presence of noisy data.</p
Safety and effectiveness of bariatric surgery: Roux-en-y gastric bypass is superior to gastric banding in the management of morbidly obese patients: a reply to the response by Bhoyrul et al
<p>Abstract</p> <p>Background</p> <p>We have read the letter by Bhoyrul et al. in response to our recently published article "<it>Safety and effectiveness of bariatric surgery: Roux-en-Y gastric bypass is superior to gastric banding in the management of morbidly obese patients"</it>. We strongly disagree with the content of the letter.</p> <p>Results and discussion</p> <p>Bhoyrul et al. base their letter mostly on low level evidence such as single-institutional case series (level IV evidence) and expert opinion (level V evidence). Surprisingly, they do not comment on the randomized controlled trial, which clearly favours gastric bypass over gastric banding.</p> <p>Conclusion</p> <p>The letter by Bhoyrul et al. is based on low level evidence and is itself biased, unsubstantiated, and not supported by the current literature.</p
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