An algorithm for robust explicit/multi-parametric model predictive control

Kouramas, Konstantinos; Panos, Christos; Faísca, Nuno P.; Pistikopoulos, E.N.

doi:10.1016/j.automatica.2012.11.035

Cited by 53 publications

(23 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These simulations were performed on a computer with an Intel Core™ i7-4790 processor and CPU 3.60 GHZ and RAM 16 GB. However, online NMPC methods such as the Continuation/GMRES method (Ohtsuka, 2004), advanced-step NMPC (Zavala and Biegler, 2009), and explicit MPC (Kouramas et al, 2013) can be used to achieve real-time performance. As an example, Mehrabi et al (2016) developed a Newton/GMRES NMPC controller to control the functional electrical stimulation of knee extension.…”

Section: Discussionmentioning

confidence: 99%

Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control

Mehrabi

Razavian

Ghannadi

et al. 2017

Front. Comput. Neurosci.

View full text Add to dashboard Cite

This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics. This study is based on the assumption that the nervous system minimizes the muscular effort during goal-directed movements. The effects of prediction horizon length on the trajectory, velocity profile, and muscle activities of a reaching task are presented. The NMPC predictions of the hand trajectory to reach fixed and moving targets are in good agreement with the trajectories found by dynamic optimization and those from experiments. However, the hand velocity and muscle activations predicted by NMPC did not agree as well with experiments or with those found from dynamic optimization.

show abstract

Section: Discussionmentioning

confidence: 99%

Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control

Mehrabi

Razavian

Ghannadi

et al. 2017

Front. Comput. Neurosci.

View full text Add to dashboard Cite

show abstract

“…Pistikopoulos). 2011; Kouramas, Panos, Faísca, & Pistikopoulos, 2013;Krieger & Pistikopoulos, 2014;Mayne, Raković, & Kerrigan, 2007;Pistikopoulos, 2009;Pistikopoulos, 2012;Rivotti & Pistikopoulos, 2014a;Rivotti & Pistikopoulos, 2014b;Wen, Ma, & Ydstie, 2009). …”

Section: Introductionmentioning

confidence: 97%

Explicit model predictive control: A connected-graph approach

2017

View full text Add to dashboard Cite

“…Multiplicative uncertainty can be incorporated without any changes in the framework. This can be achieved via the consideration of robust multi-parametric Model Predictive Control as described in [188,267,279]. In such case, the use of state-estimators is also necessary.…”

Section: 'High Fidelity' Modelmentioning

confidence: 99%

“…In mp-MPC, e orts have been made to develop a multi-parametric model-based controller that guarantees performance while satisfying the constraints, termed robust mp-MPC. While early approaches focused on (i) additive disturbances [6,259,286,306], (ii) model uncertainties [35,279], (iii) Min-max robust mp-MPC [6,35,81,172] and (iv) linear input/output models [82,259], recent advances point towards a more general approach for robust mp-MPC [188,279]. The key idea is thereby (i) a dynamic programming reformulation of the original problem, (ii) the formulation of the robust counter-part and (iii) the solution of the resulting mp-P problem.…”

Section: Robust Mp-mpcmentioning

confidence: 99%