Robain De Keyser

Developing Networked Control Labs: A Matlab and Easy Java Simulations Approach

The new information technologies provide great opportunities in control education. One of them is the use of remote control labs to teach the behavior of control systems through a network. In this paper, a new approach to create interactive networked control labs is described. Two main software tools are used: Matlab and Easy Java Simulations. The first one is a widely used tool in the control community, whereas the second one is an authoring tool, designed to build interactive applications in Java without special programming skills. The remote labs created by this approach give to students the opportunity to face the effects of network delays on the controlled system and also to specify on the fly their own control algorithm.

FRtool: A frequency response tool for CACSD in Matlab®

A control system can be designed either by applying a mathematical design method (the system theory approach) or by using a controller design tool (the CAD-approach). Graphical visually-interactive Computer Aided Design methods are nowadays quite attractive, especially for non-experts in control engineering. Matlab offers currently a controller design tool based on the root locus technique; time-delay systems have to be handled in a somewhat artificial way by approximating the dead-time with a rational transfer function. However time-delay systems can be tackled easily in the frequency-domain since the only influence of the dead-time is a shift in the phase. This paper thus presents a CAD-software based on the use of frequency charts as a controller design tool.

Assessment of respiratory mechanical properties with constant-phase models in healthy and COPD lungs

This study employs the concept of applying constant-phase models to input respiratory impedance data obtained with the non-invasive Forced Oscillation Technique (FOT) lung function test. Changes in respiratory mechanics from healthy and chronic obstructive pulmonary disease (COPD) diagnosed patients are observed with a four- and a five-parameter constant-phase model. Tissue damping (p<<0.01), tissue elastance (p<0.02) and tissue hysteresivity (p<<0.01) are calculated from the identified model parameters, providing significant separation between healthy and COPD groups. Limitations of the four-parameter constant-phase model are shown in relation to frequency-dependent impedance values within the range 4-48 Hz. The results clearly show that the five-parameter constant-phase model outperforms the four-parameter constant-phase model in this frequency range. The averaged error is 0.02 and 0.04 for healthy subjects in the five-parameter and four-parameter constant-phase models, respectively. The results show that the identified model values are sensitive to variations between healthy and COPD lungs.

Fractional order model parameters for the respiratory input impedance in healthy and in asthmatic children

This paper provides an evaluation of a fractional order model for the respiratory input impedance, using two groups of subjects, respectively healthy and asthmatic children. The purpose is to verify if the model is able to deliver statistically meaningful parameter values in order to classify the two groups. The data are gathered with the non-invasive lung function test of forced oscillations technique, by means of a multisine signal within the 4-48Hz frequency range. Based on our previous work, a fractional order model for this range of frequencies is obtained. Additional parameters are proposed to evaluate the two groups. The results indicate that the model was unable to detect significant changes between the asthmatic children with normal spirometry results (as result of medication) and the healthy children. Due to medication intake during the hours prior to the exam, bronchial challenge did not modify substantially the respiratory parameters. Our findings correspond to similar studies reported in the specialized literature. Combined model parameters, such as the tissue damping and the tissue elastance were significantly different in the two groups (p<0.01). Two extra indexes are introduced: the quality factor and the power factor, providing significantly different results between the two groups (p≪0.01). We conclude that the model can be used in the respective frequency range to characterize the two groups efficiently.

Lessons learned from closed loops in engineering: towards a multivariable approach regulating depth of anaesthesia

In this paper is presented a brief state of art regarding the multivariable formulation for controlling the depth of anaesthesia by means of two intravenously administrated drugs, i.e. propofol and remifentanil. In a feasibility study of determining a suitable variable to quantify analgesia levels in patients undergoing cardiac surgery, the bispectral index and an electromyogram-based surrogate variable are proposed as the controlled variables. The study is carried on in the context of implementing a multivariable predictive control algorithm. The simulation results show that such a paradigm is feasible, although it does not guarantee perfect knowledge of the analgesia level—in other words, the variable is not validated against typical evaluations of the pain levels (e.g. clinical scores).

A novel auto-tuning method for fractional order PI/PD controllers

Fractional order PID controllers benefit from an increasing amount of interest from the research community due to their proven advantages. The classical tuning approach for these controllers is based on specifying a certain gain crossover frequency, a phase margin and a robustness to gain variations. To tune the fractional order controllers, the modulus, phase and phase slope of the process at the imposed gain crossover frequency are required. Usually these values are obtained from a mathematical model of the process, e.g. a transfer function. In the absence of such model, an auto-tuning method that is able to estimate these values is a valuable alternative. Auto-tuning methods are among the least discussed design methods for fractional order PID controllers. This paper proposes a novel approach for the auto-tuning of fractional order controllers. The method is based on a simple experiment that is able to determine the modulus, phase and phase slope of the process required in the computation of the controller parameters. The proposed design technique is simple and efficient in ensuring the robustness of the closed loop system. Several simulation examples are presented, including the control of processes exhibiting integer and fractional order dynamics.

Decentralized and centralized model predictive control to reduce the bullwhip effect in supply chain management

Abstract Mitigating the bullwhip effect is one of crucial problems in supply chain management. In this research, centralized and decentralized model predictive control strategies are applied to control inventory positions and to reduce the bullwhip effect in a benchmark four-echelon supply chain. The supply chain under consideration is described by discrete dynamic models characterized by balance equations on product and information flows with an ordering policy serving as the control schemes. In the decentralized control strategy, a MPC-EPSAC (Extended Prediction Self-Adaptive Control) approach is used to predict the changes in the inventory position levels. A closed-form solution of an optimal ordering decision for each echelon is obtained by locally minimizing a cost function, which consists of the errors between predicted inventory position levels and their setpoints, and a weighting function that penalizes orders. The single model predictive controller used in centralized control strategy optimizes globally and finds an optimal ordering policy for each echelon. The controller relies on a linear discrete-time state-space model to predict system outputs. But the predictions are approached by either of two multi-step predictors depending on whether the states of the controller model are directly observed or not. The objective function takes a quadratic form and thus the resulting optimization problem can be solved via standard quadratic programming method. The comparisons on performances of the two MPC strategies are illustrated with a numerical example.

Theoretical Analysis and Experimental Validation of a Simplified Fractional Order Controller for a Magnetic Levitation System

Fractional order (FO) controllers are among the emerging solutions for increasing closed-loop performance and robustness. However, they have been applied mostly to stable processes. When applied to unstable systems, the tuning technique uses the well-known frequency-domain procedures or complex genetic algorithms. This brief proposes a special type of an FO controller, as well as a novel tuning procedure, which is simple and does not involve any optimization routines. The controller parameters may be determined directly using overshoot requirements and the study of the stability of FO systems. The tuning procedure is given for the general case of a class of unstable systems with pole multiplicity. The advantage of the proposed FO controller consists in the simplicity of the tuning approach. The case study considered in this brief consists in a magnetic levitation system. The experimental results provided show that the designed controller can indeed stabilize the magnetic levitation system, as well as provide robustness to modeling uncertainties and supplementary loading conditions. For comparison purposes, a simple PID controller is also designed to point out the advantages of using the proposed FO controller.

A Remote Laboratory for Mobile Robot Applications

Abstract This paper presents the architecture and the implementation of a remote laboratory for mobile robot applications. The implementation is based on Matlab and Easy Java Simulations (EJS). The aim of the remote laboratory is that students perform – via the Internet – experiments on a group of non-holonomic mobile robots. The robot application presented in this paper is leader-follower formation control using image processing.

Quantifying and mitigating the bullwhip effect in a benchmark supply chain system by an extended prediction self-adaptive control ordering policy

We model a benchmark supply chain system from control engineering perspective.We derive analytic expression of bullwhip effect based on control theoretic concept.We formulate customized MPC to obtain closed-form ordering policy transfer function.We use new bullwhip metric on conventional and MPC ordering policies for comparison. An undesired observation known as the bullwhip effect in supply chain management leads to excessive oscillations of the inventory and order levels. This paper presents how to quantify and mitigate the bullwhip effect by introducing model predictive control (MPC) strategy into the ordering policy for a benchmark supply chain system. Instead of quantifying the bullwhip effect with commonly used statistical measure, we derive equivalently the expression of bullwhip metric via control-theoretic approach by applying discrete Fourier transform and (inverse) z-transform when the demand signal is stationary stochastic. A four-echelon supply chain is formulated and its dynamical features are analyzed to give the discrete model. An extended prediction self-adaptive control (EPSAC) approach to the multi-step predictor is implemented in the development of MPC formulation. The closed-form solution to MPC problem is derived by minimizing a specified objective function. The transfer function for MPC ordering policy is then obtained graphically from an equivalent representation of this closed-form solution. A numerical simulation shows that MPC ordering policy outperforms the traditional ordering policies on reducing bullwhip effect.