Multiple-model adaptive predictive control of mean arterial pressure and cardiac output

Yu, Clement Kwong-man; Roy, R.J.; Kaufman, H.; Bequette, B. Wayne

doi:10.1109/10.148385

Cited by 170 publications

(54 citation statements)

References 26 publications

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“…Our approach does not require a period of open-loop process identifi cation, so closedloop control can begin immediately with the sedation of the patient. Yu et al (1992) solved an unconstrained optimization problem. A constrained version is presented by Rao et al (2003), while implementation issues are discussed by Aufderheide and Bequette (2003).…”

Section: Multiple Model Predictive Controlmentioning

confidence: 99%

Non‐Linear Model Predictive Control: A Personal Retrospective

Bequette

2007

Can J Chem Eng

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An overview of non-linear model predictive control (NMPC) is presented, with an extreme bias towards the author's experiences and published results. Challenges include multiple solutions (from non-convex optimization problems), and divergence of the model and plant outputs when the constant additive output disturbance (the approach of dynamic matrix control, DMC) is used. Experiences with the use of fundamental models, multiple linear models (MMPC), and neural networks are reviewed. Ongoing work in unmeasured disturbance estimation, prediction and rejection is also discussed.On présente un aperçu général du contrôle prédictif par modèles non linéaires (NMPC), en mettant l'accent en particulier sur les expériences des auteurs et les résultats publiés. Les défi s incluent des solutions multiples (à partir des problèmes d'optimisation non convexes), ainsi que la divergence entre les sorties de modèle et d'installation lorsque la perturbation de sortie additive constante (la méthode du contrôle de matrice dynamique, DMC) est utilisée. Les expériences avec les modèles fondamentaux, les modèles linéaires multiples (MMPC) et les réseaux neuronaux sont examinées. Le travail actuellement mené sur l'estimation, la prédiction et le rejet des perturbations non mesurées est également examiné.

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Section: Multiple Model Predictive Controlmentioning

confidence: 99%

Non‐Linear Model Predictive Control: A Personal Retrospective

Bequette

2007

Can J Chem Eng

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“…One major advantage is that this algorithm is computationally inexpensive. An additional benefit is that the poor models are rejected exponentially and thereby allowing to have a widely varying set of models without necessarily leading to a large drop in controller performance, even during the initial stages [15].…”

Section: A Calculation Of Probability: Bayesian Approachmentioning

confidence: 99%

Application of Multiple-Model Adaptive Control Strategy for Robust Damping of Interarea Oscillations in Power System

Chaudhuri

Majumder

Pal

2004

IEEE Trans. Contr. Syst. Technol.

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Abstract-This paper demonstrates the application of multiple-model adaptive control (MMAC) strategy for robust damping of low-frequency electromechanical oscillation in an interconnected power system. The control algorithm uses a model-based approach to account for the variability and uncertainty involved in the postdisturbance dynamics of the system. Conventional proportional-integral-derivative (PID) controllers are tuned to achieve the desired performance for each of these models. Using a Bayesian approach, the probability of each model representing the actual power system response is computed in each iteration. The resultant control action is derived as a probability-weighted average of the individual control moves of the controllers. This strategy has been used to design and test a damping controller for a thyristor controlled series compensator (TCSC) device installed in a prototype power system. The control scheme worked satisfactorily following possible disturbances without any prior knowledge about the specific postdisturbance dynamics.Index Terms-Bayesian approach, interarea oscillations, multiple-model adaptive control (MMAC), robustness, thyristor controlled series compensator (TCSC).

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“…The controller output is mainly influenced by the controller tuning parameters and error (e) 20,21 . PI control has the integral part to eliminate offset, which is a major disadvantage of Proportional control (P control).…”

Section: Proportional-integral Control (Pi Control)mentioning

confidence: 99%

Model-Based Approach on the Effect of Isoflurane and Control of Critical Hemodynamic Variables

Sowparnika¹,

Thirumarimurugan²,

Sivakumar³

et al. 2017

Int. Res. J. Pharm.

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Monitoring and control of physiological variables during surgeries is a difficult task since the manual infusion of drugs leads to complications in patients. Anaesthesia is mainly used to maintain most the critical physiological parameters. Automatic control of these para meters will help the anaesthetist to focus on other issues that has to be taken care of. This paper presents about the closed loop control of anaesthesia drug that is used to maintain the safety limits of physiological variables such as hypnosis in terms of Bi-Spectral Index (BIS), analgesia in terms of Mean Arterial Pressure (MAP) and Heart Rate (HR). Here isoflurane is anaesthesia that is used as the medicine to administer the parameters. Pharma cokinetic (PK) and Pharmacodynamic (PD) modeling is carried out for isoflurane drug and control action is implemented based on the open loop response of the PK-PD model. The performance of the model is compared for Proportional-Integral (PI) controller and Neural Network Internal Model Control (NN-IMC) and the simulation results are obtained using MATLAB.

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Multiple-model adaptive predictive control of mean arterial pressure and cardiac output

Cited by 170 publications

References 26 publications

Non‐Linear Model Predictive Control: A Personal Retrospective

Non‐Linear Model Predictive Control: A Personal Retrospective

Application of Multiple-Model Adaptive Control Strategy for Robust Damping of Interarea Oscillations in Power System

Model-Based Approach on the Effect of Isoflurane and Control of Critical Hemodynamic Variables

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