scholarly journals Bayesian Networks for Supporting Model Based Predictive Control of Smart Buildings

10.5772/58470 ◽  
2014 ◽  
Author(s):  
Alessandro Carbonari ◽  
Massimo Vaccarini ◽  
Alberto Giretti
Keyword(s):  
Bayesian Networks ◽  
Predictive Control ◽  
Smart Buildings ◽  
Model Based

Subspace-based predictive control of Parkinson’s disease: A model-based study

2021 ◽  
Author(s):  
Mahboubeh Ahmadipour ◽  
Mojtaba Barkhordari-Yazdi ◽  
Saeid R. Seydnejad
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Predictive Control ◽  
Model Based

Motion planning of a fish-like piezoelectric actuated robot using model-based predictive control

2021 ◽  
pp. 107754632110482
Author(s):  
Arthur S Barbosa ◽  
Lucas Z Tahara ◽  
Maíra M da Silva
Keyword(s):  
Motion Planning ◽  
Predictive Control ◽  
Fiber Composite ◽  
Beam Theory ◽  
Mode Shapes ◽  
Design Strategies ◽  
Mechanical Coupling ◽  
Model Based ◽  
Input Signals ◽  
Bounded Input

This work proposes a novel methodology for planning the motion of fish-like soft robots actuated by macro-fiber composite (MFC) pairs. These structures should mimic oscillatory and undulation movements, which can be accomplished if the amplitude of the tail motion is larger than that of the head motion. Design strategies, such as the use of concentrated and distributed masses, are addressed to mimic fish-like motion since they guarantee suitable mode shapes for the structure. The motion planning proposal explores a model-based predictive control (MPC) strategy for deriving the input signals for the MFC actuators. This model-based control strategy requires the use of reasonably small-sized models. This is accomplished by extracting modal state-space models based on the free–free Euler–Bernoulli beam theory considering the electro-mechanical coupling of the MFC actuator pairs. Numerical results demonstrate the capability of the proposal for deriving bounded input signals that generate oscillatory and undulation movements even in the presence of disturbances. This general approach can be further extended for other applications.

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