Advanced multiparametric optimization and control studies for anaesthesia

Anaesthesia is a reversible pharmacological state of the patient where hypnosis, analgesia and muscle relaxation are guaranteed and maintained throughout the surgery. Analgesics block the sensation of pain; hypnotics produce unconsciousness, while muscle relaxants prevent unwanted movement of muscle tone. Controlling the depth of anaesthesia is a very challenging task, as one has to deal with nonlinearity, inter- and intra-patient variability, multivariable characteristics, variable time delays, dynamics dependent on the hypnotic agent, model analysis variability, agent and stability issues. The modelling and automatic control of anaesthesia is believed to (i) benefit the safety of the patient undergoing surgery as side-effects may be reduced by optimizing the drug infusion rates, and (ii) support anaesthetists during critical situations by automating the drug delivery systems. In this work we have developed several advanced explicit/multi-parametric model predictive (mp-MPC) control strategies for the control of depth of anaesthesia. State estimation techniques are developed and used simultaneously with mp-MPC strategies to estimate the state of each individual patient, in an attempt to overcome the challenges of inter- and intra- patient variability, and deal with possible unmeasurable noisy outputs. Strategies to deal with the nonlinearity have been also developed including local linearization, exact linearization as well as a piece-wise linearization of the Hill curve leading to a hybrid formulation of the patient model and thereby the development of multiparametric hybrid model predictive control methodology. To deal with the inter- and intra- patient variability, as well as the noise on the process output, several robust techniques and a multiparametric moving horizon estimation technique have been design and implemented. All the studies described in the thesis are performed on clinical data for a set of 12 patients who underwent general anaesthesia.Open Acces

Improving Activated Sludge Wastewater Treatment Process Efficiency Using Predictive Control

This paper investigates the performance of a new predictive control approach used to improve the energy efficiency and effluent quality of a conventional Wastewater Treatment Plant (WWTP). A modified variant of the well-known Generalized Predictive Control (GPC) method has been applied to control the dissolved oxygen concentration in the aerobic bioreactor of a WWTP. The quadratic cost function was modified to a positional implementation that considers control signal weighting and not its increments, in order to minimize the control energy. The Activated Sludge Process (ASP) optimization using the proposed variant of the GPC algorithm provides an improved aeration system efficiency to reduce energy costs. The control strategy is investigated and evaluated by performing simulations and analyzing the results. Both the set point tracking and the regulatory performances have been tested. Moreover, the effects of some tuning parameters are also investigated. The results show that this control strategy can be efficiently used for dissolved oxygen control in WWTP

Predictive Adaptive Control of an Activated Sludge Wastewater Treatment Process

This paper presents an application regarding a model based predictive adaptive controller used to improve the effluent quality of a conventionalactivated sludge wastewater treatment process. The adaptive control scheme consists of two modules: a robust parameter estimator and a predictive controller. The controller design is based on the process model obtained by recursive estimation. The performances of the adaptive control algorithm are investigated and compared tothenon-adaptive one. Both the set point tracking and the regulatory performances have been tested. The results show that this control strategy will help overcome the challenge for maintaining the discharged water quality to meet the regulations

A survey of recent advances in fractional order control for time delay systems

Several papers reviewing fractional order calculus in control applications have been published recently. These papers focus on general tuning procedures, especially for the fractional order proportional integral derivative controller. However, not all these tuning procedures are applicable to all kinds of processes, such as the delicate time delay systems. This motivates the need for synthesizing fractional order control applications, problems, and advances completely dedicated to time delay processes. The purpose of this paper is to provide a state of the art that can be easily used as a basis to familiarize oneself with fractional order tuning strategies targeted for time delayed processes. Solely, the most recent advances, dating from the last decade, are included in this review

Event-based fractional order control

The present study provides a generalization of event-based control to the field of fractional calculus, combining the benefits brought by the two approaches into an industrial-suitable control strategy. During recent years, control applications based on fractional order differintegral operators have gained more popularity due to their proven superior performance when compared to classical, integer order, control strategies. However, the current industrial setting is not yet prepared to fully adapt to complex fractional order control implementations that require hefty computational resources; needing highly-efficient methods with minimum control effort. The solution to this particular problem lies in combining benefits of event-based control such as resource optimization and bandwidth allocation with the superior performance of fractional order control. Theoretical and implementation aspects are developed in order to provide a generalization of event-based control into the fractional calculus field. Different numerical examples validate the proposed methodology, providing a useful tool, especially for industrial applications where the event-based control is most needed. Several event-based fractional order implementation possibilities are explored, the final result being an event-based fractional order control methodology. (C) 2020 The Authors. Published by Elsevier B.V. on behalf of Cairo University

Advanced model-based control studies for the induction and maintenance of intravenous anaesthesia

This paper describes strategies toward model-based automation of intravenous anaesthesia employing advanced control techniques. In particular, based on a detailed compartmental mathematical model featuring pharmacokinetic and pharmacodynamics information, two alternative model predictive control strategies are presented: a model predictive control strategy, based on online optimization, the extended predictive self-adaptive control and a multiparametric control strategy based on offline optimization, the multiparametric model predictive control. The multiparametric features to account for the effect of nonlinearity and the impact of estimation are also described. The control strategies are tested on a set of 12 virtually generated patient models for the regulation of the depth of anaesthesia by means of the bispectral index (BIS) using Propofol as the administrated anaesthetic. The simulations show fast response, suitability of dose, and robustness to induce and maintain the desired BIS setpoint

An energy-efficient context aware solution for environmental assessment

The paper focuses on presenting the advantages of context aware cyber-physical systems through an experimental platform capable of assessing its surroundings and self-performing decisions. The context aware paradigm is present in the control law implementation with various advantages such as energy efficiency as well as in the environmental measurements that trigger the robot to perform context-relevant decisions. The platform provides high versatility and the results presented throughout the study can be adapted to a manifold of multidisciplinary fields. Copyright (C) 2020 The Authors

Identification for control of suspended objects in non-Newtonian fluids

This paper proposes a framework for modelling velocity profiles and suspended objects in non-Newtonian fluid environment. A setup is proposed to allow mimicking blood properties and arterial to venous dynamic flow changes. Navier-Stokes relations are employed followed by fractional constitutive equations for velocity profiles and flow. The theoretical analysis is performed under assumptions of steady and pulsatile flow conditions, with incompressible properties. The fractional derivative model for velocity and friction drag effect upon a suspended object are determined. Experimental data from such an object is then recorded in real-time and identification of a fractional order model performed. The model is determined from step input changes during pulsatile flow for velocity in the direction of the flow. Further on, this model can be employed for controller design purposes for velocity and position in pulsatile non-Newtonian fluid flow