scholarly journals Automatic Electromechanical Perturbator for Postural Control Analysis Based on Model Predictive Control

2021 ◽  
Vol 11 (9) ◽  
pp. 4090
Author(s):  
Daniel Pacheco Pacheco Quiñones ◽  
Maria Paterna ◽  
Carlo De De Benedictis
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Balance Control ◽  
Control Analysis ◽  
Control Logic ◽  
Human Machine Interaction ◽  
Experimental Protocols ◽  
Electromechanical Device ◽  
High Dynamics ◽  
Base Of Support

Objective clinical analyses are required to evaluate balance control performance. To this outcome, it is relevant to study experimental protocols and to develop devices that can provide reliable information about the ability of a subject to maintain balance. Whereas most of the applications available in the literature and on the market involve shifting and tilting of the base of support, the system presented in this paper is based on the direct application of an impulsive (short-lasting) force by means of an electromechanical device (named automatic perturbator). The control of such stimulation is rather complex since it requires high dynamics and accuracy. Moreover, the occurrence of several non-linearities, mainly related to the human–machine interaction, signals the necessity for robust control in order to achieve the essential repeatability and reliability. A linear electric motor, in combination with Model Predictive Control, was used to develop an automatic perturbator prototype. A test bench, supported by model simulations, was developed to test the architecture of the perturbation device. The performance of the control logic has been optimized by iterative tuning of the controller parameters, and the resulting behavior of the automatic perturbator is presented.

scholarly journals Reproducing Human Arm Strategy and Its Contribution to Balance Recovery Through Model Predictive Control

2021 ◽  
Vol 15 ◽  
Author(s):  
Keli Shen ◽  
Ahmed Chemori ◽  
Mitsuhiro Hayashibe
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Balance Control ◽  
Quiet Standing ◽  
Upper Body ◽  
Supplementary Information ◽  
Balance Recovery ◽  
Walking Motion ◽  
Human Balance ◽  
Balance Rehabilitation

The study of human balance recovery strategies is important for human balance rehabilitation and humanoid robot balance control. To date, many efforts have been made to improve balance during quiet standing and walking motions. Arm usage (arm strategy) has been proposed to control the balance during walking motion in the literature. However, limited research exists on the contributions of the arm strategy for balance recovery during quiet standing along with ankle and hip strategy. Therefore, in this study, we built a simplified model with arms and proposed a controller based on nonlinear model predictive control to achieve human-like balance control. Three arm states of the model, namely, active arms, passive arms, and fixed arms, were considered to discuss the contributions of arm usage to human balance recovery during quiet standing. Furthermore, various indexes such as root mean square deviation of joint angles and recovery energy consumption were verified to reveal the mechanism behind arm strategy employment. In this study, we demonstrate to computationally reproduce human-like balance recovery with and without arm rotation during quiet standing while applying different magnitudes of perturbing forces on the upper body. In addition, the conducted human balance experiments are presented as supplementary information in this paper to demonstrate the concept on a typical example of arm strategy.

scholarly journals Balance control of quadruped robot based on model predictive control

2020 ◽  
Vol 1639 ◽  
pp. 012026
Author(s):  
ChengYe Wu ◽  
Qing Wei ◽  
Cong Zhang ◽  
HongLei An
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Balance Control ◽  
Quadruped Robot

Model predictive control in comparison to elemental balance control in an E. coli fed-batch

2018 ◽  
Vol 191 ◽  
pp. 459-467 ◽  
Author(s):  
Sophia Ulonska ◽  
Daniel Waldschitz ◽  
Julian Kager ◽  
Christoph Herwig
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Balance Control ◽  
Fed Batch ◽  
E Coli ◽  
Elemental Balance

scholarly journals Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3998
Author(s):  
Stefano Dettori ◽  
Alessandro Maddaloni ◽  
Filippo Galli ◽  
Valentina Colla ◽  
Federico Bucciarelli ◽  
...  
Keyword(s):  
Control System ◽  
Model Predictive Control ◽  
Steam Turbine ◽  
Predictive Control ◽  
Nonlinear Model ◽  
Computational Cost ◽  
Soft Sensor ◽  
Nonlinear Model Predictive Control ◽  
Steam Turbines ◽  
Control Logic

The current flexibility of the energy market requires operating steam turbines that have challenging operation requirements such as variable steam conditions and higher number of startups. This article proposes an advanced control system based on the Nonlinear Model Predictive Control (NMPC) technique, which allows to speed up the start-up of steam turbines and increase the energy produced while maintaining rotor stress as a constraint variable. A soft sensor for the online calculation of rotor stress is presented together with the steam turbine control logic. Then, we present how the computational cost of the controller was contained by reducing the order of the formulation of the optimization problem, adjusting the scheduling of the optimizer routine, and tuning the parameters of the controller itself. The performance of the control system has been compared with respect to the PI Controller architecture fed by the soft sensor results and with standard pre-calculated curves. The control architecture was evaluated in a simulation exploiting actual data from a Concentrated Solar Power Plant. The NMPC technique shows an increase in performance, with respect to the custom PI control application, and encouraging results.

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