R. De Keyser

Robust Predictive Control Strategy Applied for Propofol Dosing Using BIS as a Controlled Variable During Anesthesia

This paper presents the application of predictive control to drug dosing during anesthesia in patients undergoing surgery. The performance of a generic predictive control strategy in drug dosing control, with a previously reported anesthesia-specific control algorithm, has been evaluated. The robustness properties of the predictive controller are evaluated with respect to inter- and intrapatient variability. A single-input (propofol) single-output (bispectral index, BIS) model of the patient has been assumed for prediction as well as for simulation. A set of 12 patient models were studied and interpatient variability and disturbances are used to assess robustness of the controller. Furthermore, the controller guarantees the stability in a desired range. The applicability of the predictive controller in a real-life environment via simulation studies has been assessed.

Extended Prediction Self-Adaptive Control

Abstract Extended Prediction Self-Adaptive Control is a control strategy in which the calculation of the controllers actions is based on an adaptive long-range prediction of the resulting process output. The forecast is made based on a black box model of the process dynamics. Its parameters are identified in real time. The technique seems to be quite robust with respect to modelling errors. Moreover the control objective function has a strong intuitive appeal to the process operator. Therefore the method is especially well suited for application to real-life control problems.

A self-tuning multistep predictor application

A self-tuning multistep predictor is presented. It predicts the output of a stochastic process with unknown, possibly slowly time-varying parameters over a range of several sampling periods in the future. At each sampling instant it is tuned by using a recursive least-squares parameter estimator in real time. By doing this, the combination predictor-estimator converges fast to the optimal predictor for processes with known parameters (self-tuning property). The method seems to have powerful capabilities as an aid in controlling complex industrial processes which are until now only operated under manual control. The predictor can be used by the operator in selecting an appropriate control action (decision making). A typical application, the control of a blast furnace, is extensively dealt with in the paper. The paper opens new perspectives in the domain of self-tuning controllers, and it has practical importance as is indicated by the blast-furnace experiment.

Relations Between Fractional-Order Model Parameters and Lung Pathology in Chronic Obstructive Pulmonary Disease

In this study, changes in respiratory mechanics from healthy and chronic obstructive pulmonary disease (COPD) diagnosed patients are observed from identified fractional-order (FO) model parameters. The noninvasive forced oscillation technique is employed for lung function testing. Parameters on tissue damping and elastance are analyzed with respect to lung pathology and additional indexes developed from the identified model. The observations show that the proposed model may be used to detect changes in respiratory mechanics and offers a clear-cut separation between the healthy and COPD subject groups. Our conclusion is that an FO model is able to capture changes in viscoelasticity of the soft tissue in lungs with disease. Apart from this, nonlinear effects present in the measured signals were observed and analyzed via signal processing techniques and led to supporting evidence in relation to the expected phenomena from lung pathology in healthy and COPD patients.

Mechanical Properties of the Respiratory System Derived From Morphologic Insight

This paper aims to provide the mechanical parameters of the respiratory airways (resistance, inertance, and compliance) from morphological insight, in order to facilitate the correlations of fractional-order models with pathologic changes. The approach consists of taking into account wall thickness, inner radius, tube length, and tissue structure for each airway level to combine them into a set of equations for modeling the pressure drop, flow, wall elasticity, and air velocity (axial and radial). Effects of pulmonary disease affecting the inner radius and elastic modulus of bronchial tree are discussed. A brief comparison with the circulatory system, which poses similarities with the respiratory system, is also given. The derived mechanical parameters can serve as elements in a transmission line equivalent, whose structure preserves the geometry of the human respiratory tree. The mechanical parameters derived in this paper offer the possibility to evaluate input impedance by altering the morphological parameters in relation to the pulmonary disease. In this way, we obtain a simple, yet accurate, model to simulate and understand specific effects in respiratory diseases; e.g., airway remodeling. The final scope of the research is to relate the variations in airway structure with disease to the values of fractional-order model parameters.

A Theoretical Study on Modeling the Respiratory Tract With Ladder Networks by Means of Intrinsic Fractal Geometry

Fractional order modeling of biological systems has received significant interest in the research community. Since the fractal geometry is characterized by a recurrent structure, the self-similar branching arrangement of the airways makes the respiratory system an ideal candidate for the application of fractional calculus theory. To demonstrate the link between the recurrence of the respiratory tree and the appearance of a fractional-order model, we develop an anatomically consistent representation of the respiratory system. This model is capable of simulating the mechanical properties of the lungs and we compare the model output with in vivo measurements of the respiratory input impedance collected in 20 healthy subjects. This paper provides further proof of the underlying fractal geometry of the human lungs, and the consequent appearance of constant-phase behavior in the total respiratory impedance.

Variable Time-Delay Estimation for Anesthesia Control During Intensive Care

The presence of artifacts plays a crucial role in automatic sedation systems and may introduce variable time delays (TDs) in the closed-loop-control structures. This paper presents a successful procedure to estimate the varying TD of the bispectral index (BIS) monitor used in closed-loop control during intensive care. The TD estimation (TDE) is based on the cross-correlation analysis technique and the method is validated with real measured signals of propofol and BIS. Extended prediction self-adaptive control is used in combination with a Smith predictor to reduce the computational burden imposed by the variable TD. The conclusion is that an online TDE of the BIS monitor improves the performance of the closed-loop system for reference tracking, disturbance rejection, and overall stability.

Parametric models for characterizing respiratory input impedance

This study compares a manifold of parametric models reported in the literature with two novel parametric models for identifying human input respiratory impedance. The analysis is carried out on typical patient data obtained with the forced oscillations technique (FOT), on three sets of representative diagnosed subjects: healthy, asthma and COPD. The performance of the models is characterized by the error between the true respiratory impedance of the patient and the estimated impedance by the parametric models. The optimal solutions of the nonlinear estimations are compared. The resulting parameter values are discussed with respect to both model structure and physiological insight.

Improved mould-level control in a continuous steel casting line

Abstract One of the main parameters affecting the surface quality of the final flat products (sheets and plates) in a steel factory is the mould-level control in the continuous casting machine. Variations in the mould-level should be reduced to only some mm for improved steel quality, a specification which is often hard to fulfill with current standard PID-control strategies. This paper introduces new approaches, based on autotuning and predictive control, to improve the mould-level regulation.

Nonlinear Predictive Control of processes with variable time delay. A temperature control case study

Material or fluid transportation is a commonly encountered phenomenon in industrial applications, generating variable time delay that makes the design of feedback control loops more difficult. This paper investigates the applicability of MPC (Model Predictive Control) strategies to this type of processes. The experimental setup consists of a heated tank, of which the outlet temperature (measured at a certain distance from the tank) is controlled by manipulating the outlet flow. The nonlinear EPSAC (Extended Prediction Self-Adaptive Control) approach is used, which reduces the complexity of nonlinear optimization to iterative quadratic programming. It is shown that developing a process model in which dynamics are decoupled from the variable time delay leads to a Smith predictor-like control structure, that allows the proper operation of the control loop with fixed control parameters. The performance of the predictive controller is compared on the pilot plant to the performance of classic control approaches for systems with time delay.