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Looking back at the history of control engineering, one finds that technology and ideas combine themselves until they reach a successful result, over the timeline of several decades (Bernstein, 2002). Simple dynamical compensators (such as PID, Phase-Lead, Phase-Lag, etc) have done in the past a remarkably good and efficient job in real-life control applications. Theoretical insight in the closed-loop behavior is provided by powerful and well-developed theories – which are considered as basic knowledge for every control engineer – such as the Root Locus and the Frequency Response techniques. In the past, these theories have been extensively used as analysis tools – e.g. the stability analysis of a given control system based on closed loop poles (Root Locus RL) or Nyquist criterion (Frequency Response FR). However, analysis implies that a controller is already available, irrespective of its design method. Nowadays, thanks to the computational and graphical power of modern computers, many of these theories can be implemented as interactive graphical design tools. In this way, control engineering moves away from being an abstract and mathematical-oriented discipline and it evolves gradually towards a mature engineering discipline. An extensive reflection upon the role of information science in control engineering has been given in (Dormido, 2004). This new way of interactive control education provides practical insights into control systems fundamentals (Wittenmark et al, 1998; Dormido et al, 2002). Such combinations of interactive environment and animation bring visualization to a new level and aid learning and active participation by control engineering students (Kheir et al, 1996; Johansson et al, 1998). Recently, a Root Locus RL toolbox has been introduced in Matlab – in this chapter, a Frequency Response toolbox (FRtool) will be presented. Although some other computer aided design (CAD) tools based on frequency response have been developed in the past (Balakrihsnan & Boyd, 1994; Satoh et al, 1994; Piguet & Gillet, 1997; Pouliz & Pouliezos, 1997; Piguet et al, 1999), the one presented in this contribution is highly interactive, graphical, easy-to-use and posing an elegant simplicity (especially for non-experts, as limited control engineering insight is required). The Matlab® controller design tool based on the root locus method (rltool) cannot handle systems with time-delay without approximating the dead5

A fractional-order model predictive control with extended prediction self-adaptive control (FOMPC-EPSAC) strategy is proposed for the AR.Drone quadrotor system. The objective is to achieve an optimal trajectory tracking control for an AR.Drone quadrotor by using a fractional order integral cost function in the conventional MPC-EPSAC algorithm. In addition, a particle swarm optimization (PSO) algorithm is applied to find the optimal weighting matrices, which depend on the terms (\(\alpha \), \(\beta \)) of the fractional order cost function. Some simulation results show the superiority of FOMPC-EPSAC over conventional MPC-EPSAC with respect to trajectory tracking and robustness under wind disturbances.

This article addresses the consensus problem in multi-agent systems modeled with double-integrator dynamics under constant disturbances. Therefore, a fractional control protocol to enhance the convergence speed and robustness of the system is proposed. The communication topology of the system is represented by an undirected connected graph. In addition, the convergence of the system is analyzed with the stability boundary locus method, through which analytical expressions are deduced to ensure the stability of the agents group. Finally, an example is provided to illustrate the validity of the theoretical results.

This paper introduces a direct universal (automatic) tuner for basic loop control in industrial applications. The direct feature refers to the fact that a first-hand model, such as a step response first-order plus dead time approximation, is not required. Instead, a point in the frequency domain and the corresponding slope of the loop frequency response is identified by single test suitable for industrial applications. The proposed method has been shown to overcome pitfalls found in other (automatic) tuning methods and has been validated in a wide range of common and exotic processes in simulation and experimental conditions. The method is very robust to noise, an important feature for real life industrial applications. Comparison is performed with other well-known methods, such as approximate M-constrained integral gain optimization (AMIGO) and Skogestad internal model controller (SIMC), which are indirect methods, i.e., they are based on a first-hand approximation of step response data. The results indicate great similarity between the results, whereas the direct method has the advantage of skipping this intermediate step of identification. The control structure is the most commonly used in industry, i.e., proportional-integral-derivative (PID) type. As the derivative action is often not used in industry due to its difficult choice, in the proposed method, we use a direct relation between the integral and derivative gains. This enables the user to have in the tuning structure the advantages of the derivative action, therefore much improving the potential of good performance in real life control applications.

Drug regulatory paradigms are dependent on the hemodynamic system as it serves to distribute and clear the drug in/from the body. While focusing on the objective of the drug paradigm at hand, it is important to maintain stable hemodynamic variables. In this work, a biomedical application requiring robust control properties has been used to illustrate the potential of an autotuning method, referred to as the fractional order robust autotuner. The method is an extension of a previously presented autotuning principle and produces controllers which are robust to system gain variations. The feature of automatic tuning of controller parameters can be of great use for data-driven adaptation during intra-patient variability conditions. Fractional order PI/PD controllers are generalizations of the well-known PI/PD controllers that exhibit an extra parameter usually used to enhance the robustness of the closed loop system. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

This paper presents a new tuning method for fractional-order (FO)PID controllers to simplify current tuning and make FOPID controllers more convenient for industry, i.e. facilitate transition from state-of-art to state-of-use. The number of tuning parameters is reduced from five to three based on popular specification settings for PID controllers without the need for reduced process models which introduce modeling errors. A test batch of 133 simulated processes and two real-life processes are used to test the presented method. A comparative study between the new method and the established CRONE controller, quantifies the performance. The conclusion states that the new method gives fractional controllers with similar performances as the current methods but with a significantly decreased tuning complexity making FOPID controllers more acceptable to industry.

In large scale ships, the most used controllers for the steam/water loop are still the proportional-integral-derivative (PID) controllers. However, the tuning rules for the PID parameters are based on empirical knowledge and the performance for the loops is not satisfying. In order to improve the control performance of the steam/water loop, the application of a recently developed PID autotuning method is studied. Firstly, a &lsquo, forbidden region&rsquo, on the Nyquist plane can be obtained based on user-defined performance requirements such as robustness or gain margin and phase margin. Secondly, the dynamic of the system can be obtained with a sine test around the operation point. Finally, the PID controller&rsquo, s parameters can be obtained by locating the frequency response of the controlled system at the edge of the &lsquo, To verify the effectiveness of the new PID autotuning method, comparisons are presented with other PID autotuning methods, as well as the model predictive control. The results show the superiority of the new PID autotuning method.

Fractional order proportional integral and proportional derivative controllers are nowadays quite often used in research studies regarding the control of various types of processes, with several papers demonstrating their advantage over the traditional proportional integral/proportional derivative controllers. The majority of the tuning techniques for these fractional order proportional integral/fractional order proportional derivative controllers are based on three frequency-domain specifications, such as the open-loop gain crossover frequency, phase margin and the iso-damping property. The tuning parameters of the controllers are determined as the solution of a system of three nonlinear equations resulting from the performance criteria. However, as with any system of nonlinear equations, it might occur that for a certain process and with some specific performance criteria, the computed parameters of the fractional order proportional integral/fractional order proportional derivative controllers do not fall into a range of values with correct physical meaning. In this article, a study regarding this limitation, as well as the existence conditions for the fractional order proportional integral/fractional order proportional derivative parameters are presented. The method could also be extended to the more complex fractional order proportional–integral–derivative controller. The aim of this research is directed toward demonstrating that when designing fractional order proportional integral/fractional order proportional derivative controllers, the choice of the performance specifications should be done based on some specific design constraints. The article shows that given a specific process and open-loop modulus and phase specifications, the gain crossover frequency (or in general, a certain test frequency used in the design), specified as a performance specification, must be selected such that the process phase fulfills an important condition (design constraint). Once this is met, the proposed approach ensures that the tuning parameters of the fractional order controller will have a physical meaning. Illustrative examples are included to validate the results.

Plant-wide control implies advanced supervisory algorithms to maintain desired performance in the involved coupled sub-systems. The dynamical interactions among these sub-systems can vary with the operating point, material properties and disturbances present in the process. Recirculating loops introduce additional phenomena in the dynamic response, further challenging the control tasks. Complex process dynamics may be linear parameter varying (LPV) and may be difficult, if not impossible, to identify properly. In this context, maintaining global performance is a challenge one must undertake with limited information at hand. This paper investigates the trade-off between the complexity of the implementation and achieved performance, using supervisory predictive control with limited information shared, applied on a test-bench representative for process control industry. The robustness of the proposed algorithms is tested against a nominal scenario in which the prediction model is fully identified, with complete information exchange. Experimental tests are performed on a test-bench process characterized by strong interactions, and the results illustrate the usefulness of this work. (C) 2018 Elsevier Ltd. All rights reserved.

This research presents gain adaptation to provide specimen-specific control of the UGent Knee Rig (UGKR). The UGKR is used to investigate knee kinematics and surgical techniques during any type of motion where loading conditions are controlled. A decoupled control strategy has been developed where decoupler gains are specimen-specific. The gain adaptation procedure is tested on cadaver specimens to show its capabilities. The results show inter-specimen, intra-specimen and conditioning variation of the gains which justifies implementing the gain adaptation procedure for each newly inserted specimen. An investigation of the control performance shows that control specifications are met for each specimen test.

Fractional order systems become increasingly popular due to their versatility in modelling and control applications across various disciplines. However, the bottleneck in deploying these tools in practice is related to their implementation on real-life systems. Numerical approximations are employed but their complexity no longer match the attractive simplicity of the original fractional order systems. This paper proposes a low-order, computationally stable and efficient method for direct approximation of general order (fractional order) systems in the form of discrete-time rational transfer functions, e.g. processes, controllers. A fair comparison to other direct discretization methods is presented, demonstrating its added value with respect to the state of art.

Fractional order PI/PD controllers are generalizations of the well-known PI/PD controllers with an extra parameter usually used to enhance the robustness of the closed loop system. In this paper, an autotuning method, referred to as the fractional order KC autotuner, is presented for tuning fractional order PI/PD controllers. The method is an extension of a previously presented autotuning principle and produces controllers, which are robust to system gain variations. Additionally, the method can also be adapted to obtain robust controllers to time delay, time constant variations, etc. The advantages of this autotuning method reside in the simplicity of the approach: 1) it requires solely one single sine test on the process; 2) it does not need the process model and 3) it eliminates the complex nonlinear equations in the traditional fractional order controller design procedure. Numerical examples are included to validate the method. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

In this paper the application of robust Fractional Order Proportional-Integral (FO-PI) autotuning control strategy is presented and applied to heterogeneous dynamic systems using decentralized control. The automatic tuning of controller gains is based on a single sine test, with user-defined robustness margins guaranteed. Its performance is compared against two other fractional order controllers based on PI gain-crossover autotuning method and Internal Model Control (IMC). The closed loop control simulations applied on the heterogeneous dynamic systems indicate that the proposed method performs properly.

This paper proposes fractional order autotuner controller for the benchmark refrigeration system. The method is an extension of a previously presented autotuning principle and produces a robust fractional order PI controller to gain variations. Fractional order PI controllers are generalizations of the integer order PI controllers, which have a supplementary parameter that is usually used to enhance the robustness of the closed loop system. The method is not restricted to robustness to gain variations and can be adapted to obtain robust fractional order controllers to time delay or time constant variations, for example. The autotuning method presented in this paper has several advantages such as the need for a single sine test to be applied to the process to extract the necessary information and the elimination of complex nonlinear equations in the tuning procedure for fractional order controllers. The results obtained on the benchmark system indicate the method has high potential for real-life applications.

This work revisits the pharmacokinetic models derived from classical differential equations and proposes an extension to fractional differential equations to account for tissue trapping, which modifies the predicted drug concentration profiles. Unlike monotonic decay profiles, an oscillatory behaviour is often observed. The phenomenon may be the result of the recirculation of trapped drug molecules due to the heterogeneity of the tissue combined with the local action of the liver or other organs in depositing part of the drug for later release. The proposed model alleviates this limitation in data fitting profiles, without violating mass balance principles and physiological states. The paper also points to new concepts and techniques in modelling drug pharmacokinetic dynamics to account for short- and long-time recirculation effects. As such, it provides a better characterisation of unexplained secondary effects in patients undergoing treatment. It also establishes a link to unbounded drug accumulation models.

This paper presents a data-driven gain adaptation mechanism in order to make the usability of the UGent Knee Rig (UGKR) more flexible. The design of the UGKR is unique as it is characterized by a movable ankle joint contrary to traditional dynamic knee rigs which are used to investigate knee kinematics and surgical techniques. This feature allows the UGKR to perform both bicycle motions and squat motions while applying a quadriceps force and hamstring forces. The control of the UGKR is a model-based control strategy which requires time-consuming system identification for each new knee specimen. A data-driven gain adaptation mechanism is developed reducing the necessary time for identification creating a flexible usability. Two adaptation mechanisms are implemented and tested on both a mechanical hinge and a cadaver knee: a rectangular sequence and a polar sequence. The results from squat and bicycle tests indicate that rectangular sequence provides good performance for the rigid mechanical hinge, however, it fails for the elastic cadaver knee during full extension. It is shown that the polar sequence results in good performance for the cadaver knee compared to the rectangular mechanism. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Optimal and safe control of drug delivery systems with continuous infusion protocol is of key importance to avoid over- or under-dosing of the patient. By implementing close-loops one is able to optimize the amount of drug given to the patient. In this paper a robust control methodology is presented. Emerging tools from fractional calculus have been considered and a fractional order PI controller for drug dosing during hypnosis has been designed. In this paper a robust fractional order control of hypnosis is proposed. The controller has been evaluated on an artificial data set of 24 patients and the results indicate that such a control strategy is robust to uncertainty stemming from the inter- and intra-patient variability. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Many regulatory loops in drug delivery systems for depth of anesthesia optimization problem consider only the effect of the controller output on the patient pharmacokinetic and pharmacodynamic response. In reality, these drug assist devices are over-ruled by the anesthesiologist for setpoint changes, bolus intake and additional disturbances from the surgical team. Additionally, inter-patient variability imposes variations in the dynamic response and often intra-patient variability is also present. This paper introduces for the first time in literature a study on the effect of both controller and anesthesiologist in the loop for hypnosis regulation. Among the many control loops, model based predictive control is closest to mimic the anticipatory action of the anesthesiologist in real life and can actively deal with issues as time lags, delays, constraints, etc. This control algorithm is here combined with the action of the anesthesiologist. A disturbance signal to mimic surgical excitation has been introduced and a database of 25 patients has been derived from clinical insight. The results given in this paper reveal the antagonist effect in closed loop of the intervention from the anaesthesiologist when additional bolus intake is present. This observation explains induced dynamics in the closed loop observed in clinical trials and may be used as a starting point for next step in developing tools for improved assistance in clinical care. (C) 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

The steam/water loop is a crucial part of a steam power plant. However, satisfying control performance is difficult to obtain due to the frequent disturbance and load fluctuation. A fractional order model predictive control was studied in this paper to improve the control performance of the steam/water loop. Firstly, the dynamic of the steam/water loop was introduced in large-scale ships. Then, the model predictive control with an extended prediction self adaptive controller framework was designed for the steam/water loop with a distributed scheme. Instead of an integer cost function, a fractional order cost function was applied in the model predictive control optimization step. The superiority of the fractional order model predictive control was validated with reference tracking and load fluctuation experiments.

This article studies a leader-follower formation algorithm based on fractional order proportional-derivative (FOPD) controller for multiple unmanned aerial vehicles (UAVs) when tackling an emergency health case. The controller parameters are tuned by using a particle swarm optimization (PSO) algorithm. Its performance is compared with an integer order proportional-derivative (IOPD) controller. Finally, the global path planning for the UAVs swarm is found by using a Dijkstra's algorithm with quintic polynomial trajectory. This provides an optimal global path by considering the physical system dimension and constraints of acceleration and velocity of the UAV. The simulation tests using the virtual environment demonstrate that the proposed approach outperforms the IOPD controller.

When controlling industrial processes, setpoint tracking and disturbance rejection play an important part in the design and tuning of the PID controller parameters, especially since most of these processes exhibit dead-times. Internal Model Control (IMC) algorithms have proven to be quite efficient in setpoint tracking issues. However, the basic tuning rules for IMC lead to PID controllers that cause a sluggish disturbance rejection, especially for delay dominant processes (i.e. with big ratio of dead-time versus the process time constant). In the current paper, the experimental validation of a novel idea for tuning IMC controllers for improved disturbance rejection is presented. The method is based on using a disturbance filter that compensates for the dead-time, provided that the process is affected by stochastic disturbances having their spectral energy in a narrow frequency band (such as quasi-periodic disturbances). Diophantine equations are used to compute the disturbance filter coefficients. The exaperimental case study consists in the Quanser six tanks process.

Waste heat recovery systems are today considered as a valuable solution to increase energy efficiency of industrial applications and heavy-duty vehicles, as it uses a thermodynamic organic Rankine cycle system to recover the heat losses to produce electrical or mechanical power. Optimal performance of such machines is often achieved at conditions where complex time-varying nonlinear dynamics are encountered, making the automatic control strategy a fundamental element to maximise the energy efficiency. In this paper the development of a multiple model predictive controller suitable for industrial implementation is presented, and its effectiveness is experimentally validated for the task of maximising output power of a 11 kW el small-scale ORC power unit used in a waste heat recovery application. The main advantage of the proposed controller is the possibility to use different model structures to describe local dynamics without increasing complexity of the optimisation problem. Additionally, experimental results illustrate that the entire operating range of the system might be classified in two regions, a quasi-linear and a highly nonlinear region for ‘high’ and ‘low’ superheating degrees respectively. Closed-loops tests lead to the conclusion that a single linear model predictive controller might only be used under suboptimal operation of low power production (on the quasi-linear region for ‘high’ superheating), otherwise leading to poor performance or even instability. Alternatively, the proposed strategy keeps the cycle stable over the entire range of conditions and allows to increase the net electrical energy produced by at least 6 % , even under drastic waste heat source variations, when operating closer to the minimum allowed superheating degree.

This paper presents a robust control methodology incorporating an automatic calibration step to compensate dynamic variations in a 6 DoF mechatronic system. The application used to illustrate the efficacy of the proposed approach is the ball and plate system based on the Stewart platform. To emulate dynamic changes in the system, we make use of various types of balls, varying in mass, diameter and surface. An automatic calibration step is introduced to obtain a model based on the type of ball which is placed on the plate. Using a model-based tuning technique, an optimal proportional-differential (PD) controller is designed based on the previous calibration. The resulting controller is tested for robustness by changing the ball without changing the controller parameters. The results indicate that our setup is robust and performs well in the presence of significant changes.

PIDs are the most widely used controllers in industrial applications. This particular interest generates on-going research regarding simplified tuning methods appealing to the industrial user. Such methods refer also to a fast design of PID controllers in the absence of a mathematical model of the process. Autotuners represent one way of achieving such a fast design. In this paper, the experimental validation of a previously presented direct autotuner is presented. The autotuning method requires only one simple sine test on the process to compute the PID controller parameters. The case study consists in the Quanser Six Tanks Process. Comparisons with other popular tuning methods are also presented. The results show that the proposed autotuning method is a valuable option for controlling industrial processes.

Fractional Order Internal Model Control (FO-IMC) is among the newest trends in extending fractional calculus to the integer order control. Approximation of the FO-IMC is one of the key problems. Apart from this, when dealing with time delay systems, the time delay needs also to be approximated. All these approximations can alter the closed loop performance of the controller. In this paper, FO-IMC controllers will be tested in terms of the approximation accuracy. The case study is a first order system with time delay. Several scenarios will be considered, aiming for a conclusion regarding the choice of the approximation method as a function of the process characteristics, closed loop performance and FO-IMC fractional order. To approximate the time delay, two extensively used techniques will be considered, such as the series and Pade approximations. These will be compared to a novel approximation technique. An analysis of the test cases presented show that the series approximation proves more suitable in a single scenario, whereas the novel approximation method produces better results for the rest of the test cases.

In modern steam power plants, the ever-increasing complexity requires great reliability and flexibility of the control system. Hence, in this paper, the feasibility of a distributed model predictive control (DiMPC) strategy with an extended prediction self-adaptive control (EPSAC) framework is studied, in which the multiple controllers allow each sub-loop to have its own requirement flexibility. Meanwhile, the model predictive control can guarantee a good performance for the system with constraints. The performance is compared against a decentralized model predictive control (DeMPC) and a centralized model predictive control (CMPC). In order to improve the computing speed, a multiple objective model predictive control (MOMPC) is proposed. For the stability of the control system, the convergence of the DiMPC is discussed. Simulation tests are performed on the five different sub-loops of steam/water loop. The results indicate that the DiMPC may achieve similar performance as CMPC while outperforming the DeMPC method.

Fractional order controllers are receiving an ever-increasing interest from the research community due to their advantages. However, most of the tuning procedures for fractional order controllers assume a fully known mathematical model of the process. In this paper, an autotuning method for the design of a fractional order PD controller is presented and applied to the vibration suppression in airplane wings. To validate the designed controller, an experimental unit consisting of a smart beam that simulates the behaviour of an airplane wing is used. The experimental results demonstrate the efficiency of the designed controller in suppressing unwanted vibrations.

In this paper, a non-cooperative distributed model predictive control (DMPC) algorithm for tracking constant references is developed and evaluated. As such, an augmented model is employed (i.e. the control loop is embedded with integrators) and the augmented state contains the state increments and the error between the reference and the predicted output. The algorithm is tested in real life experiments on the quadruple tank process with non-minimum phase behaviour. The experimental results show acceptable performance index for the DMPC method when compared with the centralized approach.

This work presents a linear, constrained, multivariable predictive control strategy, i.e. EPSAC (Extended Prediction Self-Adaptive Control) applied to a train of three distillation columns. The columns are used to obtain the carbon isotope 13C used widely in medicine and specific industries. The oversimplified models of the three columns stem from a real-life plant designed and constructed in the National Institute for Research and Development for Isotopic and Molecular Technologies, in Cluj Napoca, Romania. The simulation results suggest the strategy is applicable to this process.

In multi-modular process architectures with independent but interacting subsystems, identification may not be the first choice at hand for closed loop control. A robust relay-based PID autotuning strategy is presented and validated on a quadruple tank system with non-minimum phase dynamics. The controller ensures a specified closed loop robustness, which is of great benefit to the overall performance. The experimental results suggest that the proposed method fulfils the robustness requirement and performs well in various operating conditions of the testbench.

The design of an appropriate inventory control policy for a supply chain (SC) plays an essential role in tempering inventory instability and bullwhip effect. Several constraints are commonly encountered in actual operations so managers are required to take these physical restrictions into account when designing the inventory control policy. Model predictive control (MPC) appears as a promising solution to this issue, due to its capability of finding optimal control actions for a constrained SC system. Therefore, the inventory control problem for a benchmark SC is solved using the extended prediction self-adaptive control approach to MPC. To extend methodologies in our previous work, the control framework relies on generic process model and incorporates the physical constraints arising from practical operations to form the general constrained optimisation problems. The managers can choose from decentralised and centralised control structures according to specific informational and organisational factors of...

This paper addresses the problem of interaction in mechanical multi-body systems and shows that subsystem interaction can be considerably minimized while increasing performance if an efficient disturbance model is used. In order to illustrate the advantage of the proposed intelligent disturbance filter, two linear model based techniques are considered: IMC and the model based predictive (MPC) approach. As an illustrative example, multivariable mass-spring-damper and quarter car systems are presented. An adaptation mechanism is introduced to account for linear parameter varying LPV conditions. In this paper we show that, even if the IMC control strategy was not designed for MIMO systems, if a proper filter is used, IMC can successfully deal with disturbance rejection in a multivariable system, and the results obtained are comparable with those obtained by a MIMO predictive control approach. The results suggest that both methods perform equally well, with similar numerical complexity and implementation effort.

During the voyage of the ship, disturbances from the sea dynamics are frequently changing, and the ship’s operation mode is also varied. Hence, it is necessary to have a good controller for steam/water loop, as the control task is becoming more challenging in large scale ships. In this paper, a robust proportional-integral-derivative (PID) autotuning method is presented and applied to the steam/water loop based on single sine tests for every sub-loop in the steam/water loop. The controller is obtained during which the user-defined robustness margins are guaranteed. Its performance is compared against other PID autotuners, and results indicate its superiority.

This paper presents the structure, functionality and application of an improved Remote Laboratory for engineering students hosted at Ghent University. The Remote Laboratory consists of two setups: Ball and Plate system and Quadruple Water Tank system. These setups introduce basic control aspects such as PID control design and non-minimum phase systems. Also more challenging aspects such as multiple-input-multiple-output control, decoupled and decentralized systems and advanced control strategies such as Internal Model Control or Model Predictive Control can be investigated on the setups. Based on a feedback study that targeted the bachelor degree students, the level of effectiveness of this concept has been shown but also possible functional enhancements that can be applied to the systems have been pointed out. The feedback survey data concluded that the Remote Laboratory has attracted the attention of students and had a positive impact in their training.

This paper proposes a mathematical framework for understanding how the structural changes in the COPD lung reflect in model parameters. The core of the analysis is a correlation between the heterogeneity in the lung as COPD degree changes (GOLD II, III and IV) and the nonlinearity index evaluated using the forced oscillation technique. A low frequency evaluation of respiratory impedance models and nonlinearity degree is performed since changes in tissue mechanics are related to viscoelastic properties. Simulation analysis of our model indicates a good correlation to expected changes in heterogeneity and nonlinear effects. A total of 43 COPD diagnosed patients are evaluated, distributed as GOLD II (18), GOLD III (15) and GOLD IV (10). Experimental data supports the claims and indicate that the proposed model and index for nonlinearity is well-suited to capture COPD structural changes.

Vibrations in airplane wings have a negative impact on the quality and safety of a flight. For this reason, active vibration suppression techniques are of extreme importance. In this paper, a smart beam is used as a simulator for the airplane wings and a fractional order PD controller is designed for active vibration mitigation. To implement the ideal fractional order controller on the smart beam unit, its digital approximation is required. In this paper, a new continuous-to-discrete-time operator is used to obtain the discrete-time approximation of the ideal fractional order PD controller. The efficiency and flexibility, as well as some guidelines for using this new operator, are given. The numerical examples show that high accuracy of approximation is obtained and that the proposed method can be considered as a suitable solution for obtaining the digital approximation of fractional order controllers. The experimental results demonstrate that the designed controller can significantly improve the vibration suppression in smart beams.

SUMMARY The global control of large-scale production machines composed of interacting subsystems is a challenging problem due to the intrinsic presence of high coupling, constraints, nonlinearity, and communication limitations. In this work, a pragmatic approach to distributed nonlinear model predictive control (DNMPC) is presented with guaranteed decrease in cost. Furthermore, in order to tackle time-varying process dynamics, a learning algorithm is developed, thereby improving the performance of the global control. The proposed control framework is experimentally validated on a hydrostatic drivetrain, which exhibits nonlinear dynamics, strongly interacting subsystems. The experimental results indicate that good tracking performance and disturbance rejection can be obtained by the proposed DNMPC. Copyright © 2014 John Wiley & Sons, Ltd.

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.

This paper investigates the control challenges posed by noncollocated mechatronic systems and motivates the need for a model-based control technique towards such systems. A novel way of online constraint handling by penalty adaptation (PAMPC) is proposed and shown to be of particular relevance towards robust control of underdamped, noncollocated systems by exploiting the structure of such systems. Further, a new tunneling approach is proposed for PAMPC to maintain feasibility under uncertainty. The PAMPC is shown to be optimal for control of a benchmark mass–spring–damper system, which poses all the mentioned challenges.