Clara M. Ionescu

A Remote Laboratory as an Innovative Educational Tool for Practicing Control Engineering Concepts

This paper presents the development, structure, implementation, and some applications of a remote laboratory for teaching automatic control concepts to engineering students. There are two applications: formation control of mobile robots and a ball-plate system. In teaching control engineering, there are two main approaches to control design: model-based control and non-model-based control. Students are given insight into: 1) for model-based control: identification of real processes (i.e., dealing with noise, choosing the sampling time, observing nonlinear effects at startup, pairing input-output variables); and 2) for non-model-based control: the advantages and disadvantages of auto-tuning techniques. The paper concludes by presenting an evaluation of these remote labs and discussing the advantages of using them as complementary tools for teaching control engineering at the Bachelors and Masters level.

Fractional-order impulse response of the respiratory system

This paper presents the measurement, frequency-response modeling and identification, and the corresponding impulse time response of the human respiratory impedance and admittance. The investigated adult patient groups were healthy, diagnosed with chronic obstructive pulmonary disease and kyphoscoliosis, respectively. The investigated children patient groups were healthy, diagnosed with asthma and cystic fibrosis, respectively. Fractional order (FO) models are identified on the measured impedance to quantify the respiratory mechanical properties. Two methods are presented for obtaining and simulating the time-domain impulse response from FO models of the respiratory admittance: (i) the classical pole-zero interpolation proposed by Oustaloup in the early 90s, and (ii) the inverse discrete Fourier Transform (DFT). The results of the identified FO models for the respiratory admittance are presented by means of their average values for each group of patients. Consequently, the impulse time response calculated from the frequency response of the averaged FO models is given by means of the two methods mentioned above. Our results indicate that both methods provide similar impulse response data. However, we suggest that the inverse DFT is a more suitable alternative to the high order transfer functions obtained using the classical Oustaloup filter. Additionally, a power law model is fitted on the impulse response data, emphasizing the intrinsic fractal dynamics of the respiratory system.

Robustness evaluation of fractional order control for varying time delay processes

In this paper, we investigate the robustness of a methodology to design fractional order PI controllers combined with Smith Predictors, for varying time delay processes. To overcome the drawback of possible instability associated with Smith Predictor control structures, mainly due to the changes in the time delay, the design focuses on ensuring robustness of the closed loop system against time delay uncertainties. The proposed method is based on time-domain performance specifications—more accessible to the process engineer, rather than the more abstract notions related to the frequency domain. A second advantage of the proposed method relies on additional robustness to plant uncertainties, achieved by maximizing open-loop gain margin. The convergence problems associated with optimization techniques, previously used in fractional order controller designs, are eliminated by an iterative procedure in computing the gain margin. The simulation example provided demonstrates the efficiency of the proposed method, in comparison to classical integer order PI controller.

Tuning algorithms for fractional order internal model controllers for time delay processes

ABSTRACT This paper presents two tuning algorithms for fractional-order internal model control (IMC) controllers for time delay processes. The two tuning algorithms are based on two specific closed-loop control configurations: the IMC control structure and the Smith predictor structure. In the latter, the equivalency between IMC and Smith predictor control structures is used to tune a fractional-order IMC controller as the primary controller of the Smith predictor structure. Fractional-order IMC controllers are designed in both cases in order to enhance the closed-loop performance and robustness of classical integer order IMC controllers. The tuning procedures are exemplified for both single-input-single-output as well as multivariable processes, described by first-order and second-order transfer functions with time delays. Different numerical examples are provided, including a general multivariable time delay process. Integer order IMC controllers are designed in each case, as well as fractional-order IMC controllers. The simulation results show that the proposed fractional-order IMC controller ensures an increased robustness to modelling uncertainties. Experimental results are also provided, for the design of a multivariable fractional-order IMC controller in a Smith predictor structure for a quadruple-tank system.

Evaluation of a Propofol and Remifentanil interaction model for predictive control of anesthesia induction

This paper introduces a simplified linear interaction model between two drugs, namely Propofol and Remifentanil, which will be used in a model based predictive control algorithm for (nonlinear) automatic induction and regulation of DOA. Depth of anesthesia (DOA) is evaluated by means of the Bispectral index (BIS). The simulation tests are performed on a set of 24 virtually generated realistic patient models. The results are promising and the performance of the controller shows a high-efficiency, optimal dosage of the two drugs in order to achieve the desired BIS reference.

APPLICATION of a SMITH PREDICTOR based NONLINEAR PREDICTIVE CONTROLLER to a SOLAR POWER PLANT

Abstract Renewable energies are gaining space in the energy generation panorama, thanks to technological advances and policy support. To take profit of these energies in an optimal and sustained way, research of new control strategies becomes imperative. This work presents the study and application of a nonlinear control strategy, where a Smith Predictor is added to the Nonlinear Extended Prediction Self-Adaptive Control (NEPSAC), for the control of a Solar Power Plant. Different simulations are performed to study the effect of the design parameters in the dynamic behavior of the system.

A Model of the Lungs Based on Fractal Geometrical and Structural Properties

Abstract The respiratory system has specific geometrical and material properties, which allow researchers to classify it as a typical fractal structure. Hitherto, only material properties in animal and human lung parenchyma have been investigated, assuming a power-law behavior of the viscoelastic properties in soft biological tissue. Consequently, lumped, fractional-order parametric models have been used to characterize such power-law behavior, in both healthy and pathologic lungs. This paper attempts to verify if the appearance of the fractional-order operator is also related to the underlying geometry and structure of the respiratory tree. Typical morphologic values are used in an electrical equivalent, based on our previous results. Simulation results show that the dichotomous and recursive, fractal-like properties of the respiratory system lead naturally to the appearance of the fractional-order operators.

Increasing the efficiency of Organic Rankine Cycle Technology by means of Multivariable Predictive Control

Abstract The Organic Rankine Cycle (ORC) technology has become very popular, as it is extremely suitable for waste heat recovery from low-grade heat sources. As the ORC system is a strongly coupled nonlinear multiple-input multiple-output (MIMO) process, conventional control strategies (e.g. PID) may not achieve satisfactory results. In this contribution our focus is on the accurate regulation of the superheating, in order to increase the efficiency of the cycle and to avoid the formation of liquid droplets that could damage the expander. To this end, a multivariable Model Predictive Control (MPC) strategy is proposed, its performance is compared to the one of PI controllers for the case of variable waste-heat source profiles.

Measuring Nonlinear Effects in Respiratory Mechanics: A Proof of Concept for Prototype Device and Method

The forced oscillation technique (FOT) is a lung function test used in clinical practice to evaluate the respiratory impedance. One of the main advantages of FOT over other lung function tests is that it does not require any special breathing maneuvers from the subject, making it one of the simplest tests to evaluate respiratory mechanics. This paper describes the nonlinear effects in the respiratory signals and related measurement instrumentation during the FOT tests. First, this paper discusses some improvements made on a prototype FOT device to allow the generation of multisines below 4 Hz. Second, two methods are evaluated to detect the nonlinear effects: a robust method and a fast method. These methods allow a comparison of the nonlinear distortions in a prototype FOT device and a commercial FOT device. The nonlinear effects are also quantified using a new index definition. FOT lung function tests are performed to obtain two distinct data sets: 1) one mixed group of patients diagnosed with asthma and cystic fibrosis and 2) one group of healthy volunteers. With the extracted nonlinear contributions, a significant difference has been observed between the two groups. This delivers the proof of concept that low-frequency measurements of the respiratory mechanics are useful to evaluate lung pathologies.

Experimental study of Predictive Control strategies for optimal operation of Organic Rankine Cycle systems

In this paper the performance of Model Predictive Control (MPC) and PID based strategies to optimally recover waste heat using Organic Rankine Cycle (ORC) technology is investigated. First the relationship between the evaporating temperature and the output power is experimentally evaluated, concluding that for some given heat source conditions there exists an optimal evaporating temperature which maximizes the energy production. Three different control strategies MPC and PID based are developed in order not only to maximize energy production but to ensure safety conditions in the machine. For the case of the MPC, the Extended Prediction Self-Adaptive Control (EPSAC) algorithm is considered in this study as it uses input/output models for prediction, avoiding the need of state estimators, making of it a suitable tool for industrial applications. The experimental results obtained on a 11kWe pilot plant show that the constrained EPSAC-MPC outperforms PID based strategies, as it allows to accurately regulate the evaporating temperature with a lower control effort while keeping the superheating in a safer operating range.