Robin De Keyser

A comparative study of self-adaptive long-range predictive control methods

Abstract During the last years a number of interesting control methods were published all having the same common idea: the control action is based on a long-range prediction of the process output. Four of these methods are compared in this paper. At least for three of them industrial applications are known, indicating that long-range predictive control methods are likely to be a useful tool for real-life applications. These adaptive control methods are compared by means of a simulation study. Special attention is paid to their robustness w.r.t. unmodelled dynamics, parameter variations, process noise and changing dead-time. Also dealt with are the number of tuning parameters that remain, as well as their efficiency and interpretation to the user.

Closed Loop Anesthesia: Are We Getting Close to Finding the Holy Grail?

It can be said that when inducing and maintaining anesthesia, anesthesiologists apply a reactive approach. They select initial doses on the basis of a variety of considerations, observe the results, and then make adjustments on the basis of several factors, at irregularly varying intervals. In control engineering terminology, this constitutes a closedloop control system, because of the feedback present in the observations and interventions of the anesthesiologist. This closed-loop control system is, however, of a special nature: (i) it has a human controller in the loop, and (ii) as a consequence, the control actions are intermittent and irregular in time. Control theory is increasingly being applied in the development of computer-controlled drug delivery systems, among others in anesthetic closed-loop control applications. The aim of computer-controlled closed-loop systems is to formalize this process of observation and intervention to provide finer and more accurate control. Such systems use a nearcontinuous signal of drug effect, calculate the error between the observed value and the setpoint value (selected by the user), and use this error in an algorithm to make frequent and regular adjustments to drug administration rates. Even better, some computer-control systems try to predict the future drug effect to make appropriate adjustments well in advance. Several conditions are necessary for accurate feedback control. The process being controlled should be defined, and 1 or more real-time representative measures of the system state should be available. In engineering terms the latter signals are designated as the process output/s, which have to be controlled according to certain agreed specifications. Ideally also, the control actuators or process input/s should, with minimal or known delay, cause predictable, linear changes in the process. We agree with Glass and Rampil, who posited in 2001 that “closed loop systems for anesthesia are more difficult to design and implement than those for aviation.” In aviation, the process of flying is governed by well-known physical laws. The outputs (such as velocity, pitch, and altitude) are clearly defined parameters, and can be measured accurately in real time, and the relationship between the inputs (such as fuel flow, flap, and tail angles) and the outputs is predictable, well-defined, and linear. What is it that makes anesthesia such a challenging control problem? Anesthesia is not a simple, well-understood process. Our understanding of consciousness and the mechanisms of anesthetic-induced loss of consciousness is far from complete. Consciousness is so ethereal that it is difficult to model. At present the best we have are models, such as mean field models of drug action, which describe phenomena in the electroencephalogram associated with different brain states. However, the anesthetic literature is replete with references to “depth of anesthesia,” which implies that anesthesia is a continuous function of effect-site concentration. Unfortunately, reality is more complex, because systems such as the brain are commonly nonlinear, bistable systems. The 2006 ASA practice advisory on awareness and brain function monitoring defines anesthesia as a “drug induced loss of consciousness.” Because consciousness is either absent or present, this advisory reinforced the assertion by Prys-Roberts in 1987 that there “cannot be degrees of anesthesia nor for that matter can there be variable depths of anesthesia.” This assertion is reasonable. Abrupt transitions occur between natural sleep stages, possibly explained by flip-flop states generated by brainstem and hypothalamic nuclei. Because anesthetic agents may act on key elements of these sleep pathways, it is likely that threshold events also typify anesthesia. Clearly, there is some way to go before we will completely understand the underlying process we seek to control. The input for an anesthetic control system, anesthetic agent administration, also has less than ideal properties. Drug administration is an asymmetrical process: we can actively infuse but cannot actively remove the drug. Because the relationship between dose and plasma concentration is so complex, target-controlled infusion (TCI) systems are a logical choice of control actuator, so that the control input is a target concentration rather than an infusion rate. Many assumptions underpin the pharmacokinetic models used in TCI systems, and some are obviously incorrect (such as that of instant mixing of an administered drug within the central compartment). Not surprisingly, the predictive accuracy of current models is imperfect, and the choice of model for propofol is controversial. From the *Department of Anesthesiology, University Medical Center Groningen, University of Groningen, Groningen University, The Netherlands; †Department of Electrical Energy, Systems and Automation, and ‡Department of Anesthesia, Ghent University,Gent, Belgium.

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.

Heuristic approaches in robot path planning

Autonomous navigation of a robot is a promising research domain due to its extensive applications. The navigation consists of four essential requirements known as perception, localization, cognition and path planning, and motion control in which path planning is the most important and interesting part. The proposed path planning techniques are classified into two main categories: classical methods and heuristic methods. The classical methods consist of cell decomposition, potential field method, subgoal network and road map. The approaches are simple; however, they commonly consume expensive computation and may possibly fail when the robot confronts with uncertainty. This survey concentrates on heuristic-based algorithms in robot path planning which are comprised of neural network, fuzzy logic, nature-inspired algorithms and hybrid algorithms. In addition, potential field method is also considered due to the good results. The strengths and drawbacks of each algorithm are discussed and future outline is provided. Autonomous navigation of a robot is a promising research domain due to its extensive applications.This survey concentrates on heuristic-based algorithms in robot path planning which are comprised of neural network, fuzzy logic, nature inspired algorithms and hybrid algorithms.The strengths and drawbacks of each algorithm are discussed and future outline is provided.

Model Identification of a Micro Air Vehicle

This paper is focused on the model identification of a Micro Air Vehicle (MAV) in straight steady flight condition. The identification is based on input-output data collected from flight tests using both frequency and time domain techniques. The vehicle is an in-house 40 cm wingspan airplane. Because of the complex coupled, multivariable and nonlinear dynamics of the aircraft, linear SISO structures for both the lateral and longitudinal models around a reference state were derived. The aim of the identification is to provide models that can be used in future development of control techniques for the MAV.

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.

Identification and path following control of an AR.Drone quadrotor

This paper describes the process of identification and closed-loop control of an Parrot AR.Drone Unmanned Aerial Vehicle (UAV) as well as a path following application based on IMC position controllers. The research issue is to achieve position control of the AR.Drone quadrotor movement via its on-board sensory equipment and external webcam video stream. Firstly, transfer functions are detailed for pitch and altitude movements and a comparison is made between implemented PID and IMC controller performance for both simulation and practice. Furthermore, using IMC controllers, a path following application exhibits controller behavior from a practical point of view. It is concluded that the dynamic model and the controllers implemented on the quadrotor can serve as a reliable basis for more advanced applications.

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.