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Model-based (MB) control over rewards is negatively associated with obsessive-compulsive (OC) symptoms. However, the OC phenotype is strongly concerned with punishments and enhanced avoidance. In contrast, continuous substance use (SU) is linked to disrupted punishments integration in future action planning and MB control over rewards was only inconsistently shown to be impaired in SU symptoms. Still, the role of positive versus negative outcomes in the relationship between MB control and OC versus SU symptoms remains to be investigated. We hypothesize increased MB control for punishments versus gains with increasing OC symptoms and decreased MB control for punishments versus gains with increasing SU symptoms. We will collect two independent samples (n=380 per task) online, with each sample completing one version of two valence-dependent 2-step tasks to assess distinct aspects of MB control combined with symptom questionnaires. This study can elucidate inter-individual differences in valence-dependent MB control relevant to unique aspects of psychopathology.

The neurotransmitter dopamine plays a central role in several cognitive domains such as motivation, reward-processing, reinforcement learning, decision-making and executive functions including working memory and attention (Arnsten, Wang, & Paspalas, 2012; Logue & Gould, 2013; Rogers, 2011; Schultz, 2002; Volkow, Wise, & Baler, 2017). Results of our recent studies point towards prominent alterations in these domains in overweight and obesity and suggest the existence of a non-linear relationship between markers of obesity and central dopamine tone (Horstmann, Fenske, & Hankir, 2015). Dopamine tone in the brain depends on both genetic and environmental factors. Genetic differences in important genes within the dopaminergic synapse, i.e. COMT (important for dopamine degradation) and dopamine receptor D2 (A1 allele of Taq1A), lead to altered dopamine signaling and can therefore contribute to higher vulnerability for diet-induced obesity (Annerbrink et al., 2008; Epstein et al., 2007). One important environmental factor that is hypothesized to affect dopamine transmission in obesity is an unhealthy diet. Accumulating evidence from animal research suggests that observed differences in dopamine signaling in obesity may be induced by diet rather than obesity (Geiger et al., 2009; Narayanaswami, Thompson, Cassis, Bardo, & Dwoskin, 2013; van de Giessen et al., 2013; Vucetic, Carlin, Totoki, & Reyes, 2012). However, the evidence for diet-induced alterations in dopamine signal transmission and their effects on dopamine-mediated cognitive processes in humans is scarce. In the proposed study, we aim to address the effect of diet high in fat and sugar on dopamine signal transmission in the brain and on dopamine-mediated cognitive performance.

Page for: An Honors Thesis presented by Tom L. Osborn to The Department of Psychology in Partial Fulfillment of the Requirements for the Degree of Bachelor of Arts with Honors in the Subject of Psychology. Harvard College, Cambridge, Massachusetts. March 12, 2020

With the increasing number of applications for both surface and underwater autonomous vehicles, a great amount of control methods and guidance principles has been developed over the years. This work proposes a review of the most common of these methods. It is mainly focused on model-based nonlinear control methods and guidance principles. Notably, this work details examples and variations of model-based linearizing controllers, applications of line of sight guidance, sliding mode controllers and several other less common control methods for both fully-actuated and underactuated vehicles. Additionally, this work proposes an alternative definition of underactuation with respect to the task allowing for a better understanding of the consequences of underactuation on control. Comparison of fully-actuated and underactuated cases shows how control laws can be used to solve the problems of underactuation and what mechanisms can be used to compensate for the lack of actuation on a degree of freedom. The reviewed methods are compared and discussed with respect to their capabilities, limitations and suitability for typical tasks.

A model-based optimal gait is obtained for the 2-D locomotion of a modular snake robot in a duct. Optimality is considered in the sense of traveling as fast as possible or traveling with minimal energy consumption. The novelty of the work lies in the development of a framework to cast the full dynamic behavior, including contact constraints with simple objects, into an optimization problem which allows for gait parameter, control parameter and/or physical parameter optimization. Optimal gait and control parameters are found via a surrogate optimization procedure which reveals optimal locomotion strategies depending on the duct width and optimization criteria. The framework is tested and illustrated with a number of optimizations of 2-D locomotion of a snake robot where either traveling time or energy consumption is minimized.

Physically motivated parametric models are the basis of several techniques related to control design. Industrial model-based controller tuning methods include pole placement, symmetric optimum and damping optimum. The challenge is that the resulting model-based controller is satisfactory only if the underlying model is appropriate. Typically, a set of potential models is known a priori, but it is not known, which model should be used. So, the critical question in model-based controller tuning is that of model selection. Existing approaches for model selection are mostly based on maximizing accuracy, but there is no reason why the most accurate model should also be the optimal model for control design. Given the overall aim to design a high-performance controller, in this paper the best model is considered as the one that has the potential to give a model-based controller the highest performance. The proposed method identifies parametric candidate models for control design. Then, a nonparametric model is used to predict the actual performance of the various controllers on the real system. A validation with two industry-like testbeds shows success of the method.

In this paper we consider a mathematical continuous-time model for biodegradation of phenol in the presence of sodium salicylate in a chemostat. The model is described by a system of three nonlinear ordinary differential equations. Based on the dynamical systems theory we provide mathematical investigations of the model including local and global analysis of the solutions. The local analysis consist in computation of two equilibrium points—one interior and one boundary (washout) equilibrium—in dependance of the dilution rate as a key model parameter. The local asymptotic stability of the equilibria is also presented. The global analysis of the model solutions comprises proving existence, uniqueness and uniform boundedness of positive solutions, as well as global asymptotic stabilizability of the dynamics. The theoretical investigations are illustrated by some numerical examples. The results in this study can be used in practice as a tool to control and optimize the chemostat performance of simultaneous biodegradation of mixed substrates in wastewater.

We use nonlinear model predictive control to procure a joint control of mobility and transmission to minimize total network communication energy use. The nonlinear optimization problem is solved numerically in a self-triggered framework, where the next control update time depends on the predicted state trajectory and the accuracy of the numerical solution. Solution accuracy must be accounted for in any circumstance where systems are run in open-loop for long stretches of time based on potentially inaccurate predictions. These triggering conditions allow us to place wireless nodes in low energy ‘idle’ states for extended periods, saving over 70% of energy compared to a periodic policy where nodes consistently use energy to receive control updates.

This paper describes an optimization strategy for operating solid-oxide fuel-cell systems at optimal efficiency. Specifically, we present the experimental validation of a real-time optimization (RTO) strategy applied to a commercial solid-oxide fuel-cell system. The proposed RTO scheme effectively pushes the system to higher levels of efficiency and maintains the system there despite perturbations by tracking active constraints. The optimization approach uses either steady-state measurements, or transient measurements in combination with a dynamic model, and can deal effectively with plant-model mismatch. In the reported experiments, the approach drives the system to the desired power demand at optimal efficiency. The experimental fuel-cell system reached 65% DC electrical efficiency. As such, the proposed RTO scheme is a promising candidate for enforcing optimal micro-CHP operation. In addition, the approach can deal with slow drifts such as degradation without compromising on efficiency. Finally, and important from a practical point of view, we suggest guidelines for safe and optimal operation. (C) 2019 Elsevier Ltd. All rights reserved.

Fuel consumption in blast furnaces depends on many factors that are mainly conditioned by the technological level of a given blast furnace, the steel mill in which it operates, and the type and quality of ferrous feed, coke, and additional reducing agents. These are global factors which a furnace crew cannot control during operation. On the other hand, using their own experience and decision-making software, a crew can run a blast furnace with minimal fuel consumption under current batch and process conditions. The paper presents a model-based algorithm for optimizing the operation of blast furnaces to achieve the lowest fuel consumption. The algorithm allows the heat demands to be continuously calculated and highlights any wastage that could be reduced without affecting the stable operation of the blast furnace.

The extensive effort in manual labour for mould manufacturing is the main reason for outsourcing manufacturing resources to low-wage countries. Furthermore, the international mould and die sector faces a lack of skilled workers due to the unattractive working conditions, for example the stressing, monotonous work and a partly hazardous working atmosphere (e.g. nickel dust). Especially, the finishing process (e.g. grinding, lapping, polishing) of freeform surfaces is still less automated in metal processing. Therefore, many efforts are initiated in order to fully automate the finishing process, but the today’s approaches are usually limited on prototypal implemented and cost-intensive machine solutions. This work introduces a finishing control approach for freeform surfaces using a lightweight robot. The robot is used as an actuator as well as a sensor for closed-loop force control. The optimal force trajectory is determined by a CAD-model which describes the geometry of the workpiece. The noisy measured force signal is filtered by a Kalman filter which also estimates the system state for the model predictive controller (MPC). Finally, a simple position controller controls the position of the tool center point (TCP) and the MPC controls the force which occur on the tool. The results show that the force can be controlled with a higher accuracy than with a conventional impedance control, which is commercially provided.

Electrohydraulic drives consist of several nonlinear elements, and the use and design of their control system is difficult. One of the most important parameters of the industrial drive is its settling time, which characterizes their dynamics. An interesting control method, which can be used for the improvement of electrohydraulic drive properties, is the model-following control (MFC). This technique enables to ensure the good control parameters of nonlinear systems in quite an easy way. In this paper, a modification consisting of changing the oil flow factor in the reference model is proposed. This improves both the dynamics of the electrohydraulic servo drive and the adaptation to the load forces, and thus energy consumption. The article describes the application of the MFC method to control an electrohydraulic servo drive in which a special proportional valve, with an permanent magnet synchronous motor (PMSM), is used. To verify the effectiveness of this method, a theoretical description of the drive is proposed. The applied MFC control system is then modeled and investigated in simulations. Lastly, laboratory investigations are made and chosen results are presented and described in the paper. The results obtained in simulation are compared with results obtained in laboratory tests. The investigations show that the usage of MFC shortens the drive settling time and, in this way, improves its dynamics.

Rehabilitation in the form of locomotion assistance and gait training through robotic exoskeletons requires both precision and accuracy to achieve effective results. The essential challenge is to ensure robust tracking of the reference signal, i.e., of the gait or locomotion. This paper presents the design of model-based (MB) and model-free (MF) robust control strategies to achieve desired performance and robustness in terms of transient behavior and steady-state/tracking error, implementable to the locomotion assistance and gait training by exoskeletons. The dynamic responses of the exoskeleton system were investigated with both the control strategies. The study was carried out with a variety of reference signals and performance was evaluated to identify the best suited approach for rehabilitation exoskeletons. In case of the model-based control, a mathematical model of the system was developed using a bond graph modeling technique and a lead compensated H-infinity reference gain controller was designed to ensure robust tracking performance. In the model-free control strategy, however, the system function is approximated using radial basis function neural networks (RBFNNs) and an adaptive proportional-derivative RBFNN controller was designed to achieve the desired results with minimum tracking error. Both strategies make the system robust and stable. However, the MF control strategy is faster for all reference inputs as compared to the MB control strategy i.e., faster to approach the peak value and settle, and rapidly approaches the zero steady-state/tracking error. The rise time in the case of a sinusoidal input for model-free control is 0.4 s faster than the rise time in model-based control. Similarly, the settling time is 3.9 s faster in the case of model-free control, which is a prominent difference and can provide better rehabilitation results.

The dual-fuel engine (DFE) has been focused on in order to improve its efficient use of natural gas. It has been reported that in DFE the combustion efficiency of the natural gas decreases at a low load. In an attempt to tackle this problem, it has been reported that a multi-stage diesel injection can improve the combustion efficiency of natural gas at a low load. The aim of this research is, therefore, to explain the mechanism for improving combustion efficiency with a multi-stage diesel injection and to develop a control model that predicts engines' combustion efficiency by multi-stage injection. Additionally, a controller was developed using the control model to improve the combustion efficiency by changing the diesel injection conditions. Experimental results show that the controller improves combustion efficiency. Furthermore, taking in to account the heat ratio of natural gas to diesel fuel in addition to combustion efficiency, it has been shown that it is possible to design a controller to leverage the advantage of DFE, which reduces the quantity of fossil fuels.

A characteristic of adaptive behavior is its goal-directed nature. An ability to act in a goal-directed manner is progressively refined during development, but this refinement can be impacted by the emergence of psychiatric disorders. Disorders of compulsivity have been framed computationally as a deficit in model-based control, and have been linked also to abnormal frontostriatal connectivity. However, the developmental trajectory of model-based control, including an interplay between its maturation and an emergence of compulsivity, has not been characterized. Availing of a large sample of healthy adolescents (n = 569) aged 14 to 24 y, we show behaviorally that over the course of adolescence there is a within-person increase in model-based control, and this is more pronounced in younger participants. Using a bivariate latent change score model, we provide evidence that the presence of higher compulsivity traits is associated with an atypical profile of this developmental maturation in model-based control. Resting-state fMRI data from a subset of the behaviorally assessed subjects (n = 230) revealed that compulsivity is associated with a less pronounced change of within-subject developmental remodeling of functional connectivity, specifically between the striatum and a frontoparietal network. Thus, in an otherwise clinically healthy population sample, in early development, individual differences in compulsivity are linked to the developmental trajectory of model-based control and a remodeling of frontostriatal connectivity.

Significance Goal-directed behavior is impaired in disorders of compulsivity. Here, we characterize the developmental trajectory of model-based control and show a progressive strengthening from adolescence to early adulthood. We found that the presence of compulsivity traits impacts on this trajectory as well as on the degree of remodeling in functional connectivity within frontostriatal circuits. These findings have implications for understanding the interplay between compulsivity, the developmental trajectory of model-based planning, and functional connectivity in frontostriatal circuits., A characteristic of adaptive behavior is its goal-directed nature. An ability to act in a goal-directed manner is progressively refined during development, but this refinement can be impacted by the emergence of psychiatric disorders. Disorders of compulsivity have been framed computationally as a deficit in model-based control, and have been linked also to abnormal frontostriatal connectivity. However, the developmental trajectory of model-based control, including an interplay between its maturation and an emergence of compulsivity, has not been characterized. Availing of a large sample of healthy adolescents (n = 569) aged 14 to 24 y, we show behaviorally that over the course of adolescence there is a within-person increase in model-based control, and this is more pronounced in younger participants. Using a bivariate latent change score model, we provide evidence that the presence of higher compulsivity traits is associated with an atypical profile of this developmental maturation in model-based control. Resting-state fMRI data from a subset of the behaviorally assessed subjects (n = 230) revealed that compulsivity is associated with a less pronounced change of within-subject developmental remodeling of functional connectivity, specifically between the striatum and a frontoparietal network. Thus, in an otherwise clinically healthy population sample, in early development, individual differences in compulsivity are linked to the developmental trajectory of model-based control and a remodeling of frontostriatal connectivity.

SUMMARYThis paper addresses the trajectory tracking control problem for a quadrotor aerial vehicle, equipped with a robotic manipulator (aerial manipulator). The controller is organized in two layers: in the top layer, an inverse kinematics algorithm computes the motion references for the actuated variables; in the bottom layer, a motion control algorithm is in charge of tracking the motion references computed by the upper layer. To the purpose, a model-based control scheme is adopted, where modelling uncertainties are compensated through an adaptive term. The stability of the proposed scheme is proven by resorting to Lyapunov arguments. Finally, a simulation case study is proposed to prove the effectiveness of the approach.

In this paper we propose a model-based scheme to control the depth of hypnosis in anesthesia that uses the BIS signal as controlled variable. In particular, the control scheme exploits the propofol pharmacokinetics/pharmacodynamics model of the patient so that the estimated effect-site concentration is used as a feedback signal for a standard PID controller, which compensates for the model uncertainties. The tuning of the parameters is performed off-line using genetic algorithms to minimize a performance index over a given data set of patients. The effectiveness of the proposed method is verified by means of a Monte Carlo method that takes into account both the intra-patient and inter-patient variability. In general, we obtain a fast induction phase with limited overshoot and a good disturbance rejection during maintenance of anesthesia.

text, 学術雑誌論文 / Journal Article, Premixed charge compression ignition (PCCI) combustion has a potential to achieve the low emission level while the thermal efficiency remains high. It requires more precise combustion control due to the low robustness of the premixed combustion. In this study, a model-based control system for both air path system and combustion is developed for realizing the ideal PCCI combustion in future. For the air path system control, a feedback (FB) controller based on H∞ control theory is used. In this controller, the nonlinearity of the plant is considered as the perturbation and robustness is ensured by H∞ control. For the combustion control, a feedforward (FF) controller is designed as an inverse model of a discretized combustion model. In this controller, the inverse model is updated cycle by cycle on-board to consider the variation of combustion characteristics depending on operation condition. The performance of the overall control system including both controllers is evaluated by the reference following test under the transient mode operation in both the simulation and the experiment for PCCI-like combustion with advanced fuel injection timing. The control system showed good tracking performance both in air path and combustion systems and the availability of the model-based control system is shown.

Smart canes are usually developed to alert visually challenged users of any obstacles beyond the canes' physical lengths. The accuracy of the sensors and their actuators' positions are equally crucial to estimate the locations of the obstacles with respect to the users so as to ensure only correct signals are sent through the associated audio or tactile feedbacks. For implementations with low-cost sensors, however, the users are very likely to get false alerts due to the effects from noise and their erratic readings, and the performance degradation will be more noticeable when the positional fluctuations of the actuators get amplified. In this paper, a multi-sensor obstacle detection system for a smart cane is proposed via a model-based state-feedback control strategy to regulate the detection angle of the sensors and minimize the false alerts to the user. In this approach, the overall system is first restructured into a suitable state-space model, and a linear quadratic regulator (LQR)-based controller is then synthesized to further optimize the actuator's control actions while ensuring its position tracking. We also integrate dynamic feedback compensators into the design to increase the accuracy of the user alerts. The performance of the resulting feedback system was evaluated via a series of real-time experiments, and we showed that the proposed method provides significant improvements over conventional methods in terms of error reductions.

This paper describes a case study carried out to quantify the benefits achieved from the application of model predictive control (MPC) to regulate the pH in a brine dechlorination process. A mechanistic model of the process was developed and used to design a PI controller with feedforward compensation. The model was also used to quantify the potential benefits of commissioning an MPC controller on the process. Following the analysis with the mechanistic model, the designed MPC scheme was applied to the actual process. Through real-time results, it is shown that the deployment of this controller led to substantial improvements in disturbance rejection capabilities when compared to the existing PI controller with lead- lag feedforward compensator. The improved disturbance rejection led to a reduction in pH variation which resulted in reduced operational costs, more reliable production, and improvement in the efficiency of the dechlorination process.

The Brushless Doubly Fed Reluctance Machine (BDFRM) is an emerging alternative for variable speed drive systems, providing a significant downsizing of the power electronics converter. This paper proposes a new view on the machine equations, allowing the reuse of the standard control system design for conventional synchronous and asynchronous machines: a cascade control system with an inner current control- and outer speed control loop. The assumptions and simplifications made on the machine model allow for a simple, model-based approach to set the controller gains in a brushless doubly fed machine drive system. The cascade control scheme is combined with an Unscented unscented Kalman Filter filter as a state observer, capable of estimating the load torque and losses. The performance of the proposed control system design is checked in simulation and tested in real-time on a low power BDFRM prototype.

This paper discusses the stability of systems controlling patient body weight support systems which are used in gait re-education. These devices belong to the class of underactuated mechanical systems. This is due to the application of elastic shock-absorbing connections between the active part of the system and the passive part which impacts the patient. The model takes into account properties of the system, such as inertia, attenuation and susceptibility to the elements. Stability is an essential property of the system due to human–device interaction. In order to demonstrate stability, Lyapunov’s theory of stability, which is based on the model of system dynamics, was applied. The stability of the control system based on a model that requires knowledge of the structure and parameters of the equations of motion was demonstrated. Due to inaccuracies in the modeling of the rope (one of the basic elements of the device), an adaptive control system was introduced and its stability was also proved. The authors conducted simulation and experimental tests that illustrate the functionality of the analyzed control systems.

Hierarchical control architectures are a common approach when hydraulic systems are under study; provided their multi-domain nature, the control scheme is commonly split into different hierarchical levels each one associated with a particular physical domain. This paper presents the application of a model-based control structure called Embedded Model Control (EMC) when a hierarchical scheme is implemented on an electro-hydraulic proportional valve. The overall control consists of two hierarchical loops: the inner loop is the solenoid current regulator with a closed loop bandwidth close to 1 kHz. The outer loop is a position tracking control, in charge of the accurate positioning of the spool with respect to valve openings. The paper addresses the outer loop, i.e., the tracking of mechanical spool position by using the EMC. Analysis and synthesis are presented as well as experimental results obtained from a test rig provided by an industrial manufacturer.

BACKGROUND: Currently, control of exoskeletons in rehabilitation focuses on imposing desired trajectories to promote relearning of motions. Furthermore, assistance is often provided by imposing these desired trajectories using impedance controllers. However, lower-limb exoskeletons are also a promising solution for mobility problems of individuals in daily life. To develop an assistive exoskeleton which allows the user to be autonomous, i.e. in control of his motions, remains a challenge. This paper presents a model-based control method to tackle this challenge. METHODS: The model-based control method utilizes a dynamic model of the exoskeleton to compensate for its own dynamics. After this compensation of the exoskeleton dynamics, the exoskeleton can provide a desired assistance to the user. While dynamic models of exoskeletons used in the literature focus on gravity compensation only, the need for modelling and monitoring of the ground contact impedes their widespread use. The control strategy proposed here relies on modelling of the full exoskeleton dynamics and of the contacts with the environment. A modelling strategy and general control scheme are introduced. RESULTS: Validation of the control method on 15 non-disabled adults performing sit-to-stand motions shows that muscle effort and joint torques are similar in the conditions with dynamically compensated exoskeleton and without exoskeleton. The condition with exoskeleton in which the compensating controller was not active showed a significant increase in human joint torques and muscle effort at the knee and hip. Motor saturation occurred during the assisted condition, which limited the assistance the exoskeleton could deliver. CONCLUSIONS: This work presents the modelling steps and controller design to compensate the exoskeleton dynamics. The validation seems to indicate that the presented model-based controller is able to compensate the exoskeleton. ispartof: JOURNAL OF NEUROENGINEERING AND REHABILITATION vol:16 issue:1 ispartof: location:England status: published

During 2016–2018 experimental campaigns, the plasma magnetic control architecture of the EAST tokamak was revised in order to achieve improved performances, with the final aim of feedback control of alternative divertor configurations (i.e. with multiple X-points). This paper reports on the results obtained with the Multi-Input–Multi-Output (MIMO) plasma shape controller tested during the last experimental campaign, which prompted a considerable improvement of the control performances. Simulation results are also discussed.

Symbolic models are becoming more and more popular in the research community working on hybrid systems because they provide a systematic approach to enforce logic specifications on purely continuous or hybrid systems while fulfilling the constraints at the hardware/software implementation level. This paper contributes to this research line and proposes symbolic models approximating possibly unstable time-delay systems with quantized measurements of the outputs. An application of the proposed results to the glucose control problems for the Artificial Pancreas is discussed in the paper.

Nonlinear model predictive control (NMPC) is an attractive control approach to regulate batch processes reliant on an accurate dynamic model. Most dynamic models however are affected by significant uncertainties, which may lead to worse control performance and infeasibilities, considering the tendency of NMPC to drive the system to its constraints. This paper proposes a novel NMPC framework to mitigate this issue by explicitly taking into account time-invariant stochastic uncertainties. Parametric uncertainties are assumed to be given by so-called polynomial chaos expansions (PCE), which constitutes a flexible approach to depict arbitrary probability distributions. It is assumed that at each sampling time only noisy output measurements are available. The proposed procedure uses a sparse Gauss-Hermite sampling rule to formulate an efficient scenario-based NMPC algorithm based on the PCE, while a stochastic nonlinear filter is employed to update the PCE given the available measurements. The framework is shown to be effective on a challenging semi-batch fermentation process simulation case study.

Optimal management of thermal energy storage in a building is essential to provide predictable energy flexibility to a smart grid. Active technologies such as Electric Thermal Storage (ETS) can assist in building heating load management and can complement the building’s passive thermal storage capacity. The presented paper outlines a methodology that utilizes the concept of Building Energy Flexibility Index (BEFI) and shows that implementing Model Predictive Control (MPC) with dedicated thermal storage can provide predictable energy flexibility to the grid during critical times. When the utility notifies the customer 12 h before a Demand Response (DR) event, a BEFI up to 65 kW (100% reduction) can be achieved. A dynamic rate structure as the objective function is shown to be successful in reducing the peak demand, while a greater reduction in energy consumption in a 24-hour period is seen with a rate structure with a demand charge. Contingency reserve participation was also studied and strategies included reducing the zone temperature setpoint by 2∘C for 3 h or using the stored thermal energy by discharging the device for 3 h. Favourable results were found for both options, where a BEFI of up to 47 kW (96%) is achieved. The proposed methodology for modeling and evaluation of control strategies is suitable for other similar convectively conditioned buildings equipped with active and passive storage.

This paper presents an image-based controller to perform the guidance of a free-floating robot manipulator. The manipulator has an eye-in-hand camera system, and is attached to a base satellite. The base is completely free and floating in space with no attitude control, and thus, freely reacting to the movements of the robot manipulator attached to it. The proposed image-based approach uses the system's kinematics and dynamics model, not only to achieve a desired location with respect to an observed object in space, but also to follow a desired trajectory with respect to the object. To do this, the paper presents an optimal control approach to guiding the free-floating satellite-mounted robot, using visual information and considering the optimization of the motor commands with respect to a specified metric along with chaos compensation. The proposed controller is applied to the visual control of a four-degree-of-freedom robot manipulator in different scenarios. Research supported by the Spanish Ministry of Economy through the research project DPI2015-68087-R.

This paper provides a survey of the main advanced control techniques currently adopted in factory automation. In particular, it focuses on five classes of control approaches, namely: model-based control, control based on computational intelligence, adaptive control, discrete event systems-based control and finally event-triggered and self-triggered control. A particular focus is put on the most significant and recent contributions in these areas with attention to their application in the factory automation domain. Finally, open issues, challenges and the requirements of further research efforts for each class are pointed out.

Background: Stress-induced hyperglycemia is common in critically ill patients. A few forms of model-based glycemic control have been introduced to reduce this phenomena and among them is the automated STAR protocol which has been used in the Christchurch and Gyulá hospitals' intensive care units (ICUs) since 2010. Methods: This article presents the pilot trial assessment of STAR protocol which has been implemented in the International Islamic University Malaysia Medical Centre (IIUMMC) Hospital ICU since December 2017. One hundred and forty-two patients who received STAR treatment for more than 20 hours were used in the assessment. The initial results are presented to discuss the ability to adopt and adapt the model-based control framework in a Malaysian environment by analyzing its performance and safety. Results: Overall, 60.7% of blood glucose measurements were in the target band. Only 0.78% and 0.02% of cohort measurements were below 4.0 mmol/L and 2.2 mmol/L (the limitsfor mild and severe hypoglycemia, respectively). Treatment preference-wise, the clinical staff were favorable of longer intervention options when available. However, 1 hourly treatments were still used in 73.7% of cases. Conclusion: The protocol succeeded in achieving patient-specific glycemic control while maintaining safety and was trusted by nurses to reduce workload. Its lower performance results, however, give the indication for modification in some of the control settings to better fit the Malaysian environment.

[Resumen] La estimación de los estados no observables de un proceso es importante cuando se usan técnicas de control que suponen dichos valores conocidos apriori. Los controladores basados en el espacio de estados presentan un buen comportamiento y eficiencia, aún cuando la dinámica del proceso es no lineal. Uno de los procesos con una dinámica no lineal y en la que no todos los estados son observables es el caso de las plantas solares cilindro- parabólicas. En este trabajo se presenta un observador, basado en un sistema de inferencia borroso, para la estimación de los perfiles de temperatura de los lazos que componen el campo solar. [Abstract] The estimation of the unobservable states of a process is important when using control techniques that assume, a priori, these values are known. The controllers based on the state space are very efficient and even more when the dynamics of the process are strongly non-linear. One of the processes with very non-linear dynamics, and in which not all the states are observable, is the case of parabolic trough solar plants. In this work an observer based on Fuzzy Logic is presented to estimate the temperature profiles of the loops that form the solar field Ministerio de Economía, Industria y Competitividad; DPI2016-78338-R.

System identification is a common tool for estimating (linear) plant models as a basis for model-based predictive control and optimization. The current challenges in process industry, however, ask for data-driven modelling techniques that go beyond the single unit/plant models. While optimization and control problems become more and more structured in the form of decentralized and/or distributed solutions, the related modelling problems will need to address structured and interconnected systems. An introduction will be given to the current state of the art and related developments in the identification of linear dynamic networks. Starting from classical prediction error methods for open-loop and closed-loop systems, several consequences for the handling of network situations will be presented and new research questions will be highlighted.

The method of characteristic equations tries to describe the part load behaviour of sorption heat converters (e.g. ab- and adsorption chillers, heat pumps and transformers) in a simple but precise manner. Nevertheless, during the application of the established method some disagreements and inaccuracies occur. For example, the analytically determined parameters of the characteristic equation do either not agree well with values derived from measurements or they are not constant. By means of a more precise specification for normally sub-cooled or super-heated inlet conditions of the solution in ab- and desorber these effects can by explained and explicitly accounted for. Thus no iteration or regression analysis is necessary and the calculation procedure can be easily implemented into industry controllers for the control of absorption chillers., Die Methode der charakteristischen Gleichungen hat zum Ziel das Teillastverhalten von Sorptionswärmewandlern (d.h. Ab- und Adsorptionskälteanlagen /-wärmepumpen und -transformatoren) möglichst einfach aber dennoch präzise zu beschreiben. Bei der praktischen bzw. quantitativen Anwendung der herkömmlichen Methode kommt es jedoch zu Unstimmigkeiten bzw. Ungenauigkeiten. Hierzu zählt z.B., dass die analytisch berechneten Parameter der charakteristischen Gleichung nicht mit den Werten aus Messungen übereinstimmen bzw. nicht konstant sind. Durch eine Präzisierung der Methode mit Berücksichtigung der i.A. überhitzt oder unterkühlt eintretenden Lösung in den Ab- und Desorber können diese Effekte erklärt und explizit ermittelt werden. Damit sind keine Iterationen oder Regressionsanalysen erforderlich und das Berechnungsverfahren kann gut in Industrie-Controller zur Regelung von Absorptionskälteanlagen implementiert werden.

The DLR project ALLFlight (Assisted Low-Level Flight and Landing on Unprepared Landing Sites) deals with the development of a helicopter pilot assistance system, to provide pilot assistance during all phases of flight. The research is particularly focused on the landing on unprepared sites in the presence of obstacles and in a degraded visual environment. The goal is to compute a safe flight path in real time that is displayed to the pilot. Several technical components such as a sensor suite to scan the environment, path planning algorithms, head-down and helmet-mounted displays, active inceptors, and the model-based flight control system are set up to form the overall assistance system. Throughout the development, pilot surveys generate requirements to design individual components such as path planning algorithms, or the synthetic view of the helmet-mounted display. Prior surveys accelerate the design and adjustment of the respective components, meaning that the flight tests benefit from well-designed components, minimizing the requirement for further “re-designs”. During ALLFlight, the successfully flight-tested manual landing with path planning and obstacle avoidance in real time for an unknown terrain has been demonstrated. This paper describes each assistance component, the pilot surveys, and the results obtained in almost six years of research.

Performance of any model-based control scheme depends on the quality of model. When these schemes deliver poor loop performance due to model-plant mismatch (MPM), a detection of the same needs to be in place. A recently introduced plant model ratio (PMR) not only detects MPM but also facilitates a unique identification of the source of mismatch, namely, gain, dynamics (time constant) and delay mismatches. The prime objective of this work is to improve the PMR approach in a few key aspects, namely, estimation and experimental effort, and assessment procedure by taking a fresh perspective of PMR and conducting a detailed theoretical study of its signatures. A rigorous assessment procedure based on the theoretical properties of PMR is devised. Three threshold-based hypotheses tests are proposed for significance testing of PMR. A key contribution of this work is the design of set-point with minimal excitation for diagnosis of MPM, based on the features of PMR. The revised methodology is demonstrated and compared with the existing method through simulation examples. The study also demonstrates the potential of the proposed method in serving as a prelude to full/partial model re-identification. (C) 2014 Elsevier Ltd. All rights reserved

Programa de Doctorat: Automàtica, Robòtica i Visió. [EN]: In recent years, the PEM fuel cell technology has been incorporated to the R&D plans of many key companies in the automotive, stationary power and portable electronics sectors. However, despite current developments, the technology is not mature enough to be significantly introduced into the energy market. Performance, durability and cost are the key challenges. The performance and durability of PEM fuel cells significantly depend on variations in the concentrations of hydrogen and oxygen in the gas channels, water activity in the catalyst layers and other backing layers, water content in the polymer electrolyte membrane, as well as temperature, among other variables. Such variables exhibit internal spatial dependence in the direction of the fuel and air streams of the anode and cathode. Highly non-uniform spatial distributions in PEM fuel cells result in local over-heating, cell flooding, accelerated ageing, and lower power output than expected. Despite the importance of spatial variations of certain variables in PEM fuel cells, not many works available in the literature target the control of spatial profiles. Most controloriented designs use lumped-parameter models because of their simplicity and convenience for controller performance. In contrast, this Doctoral Thesis targets the distributed parameter modelling and control of PEM fuel cells. In the modelling part, the research addresses the detailed development of a non-linear distributed parameter model of a single PEM fuel cell, which incorporates the effects of spatial variations of variables that are relevant to its proper performance. The model is first used to analyse important cell internal spatial profiles, and it is later simplified in order to decrease its computational complexity and make it suitable for control purposes. In this task, two different model order reduction techniques are applied and compared. The purpose of the control part is to tackle water management and supply of reactants, which are two major PEM fuel cell operation challenges with important degradation consequences. In this part of the Thesis, two decentralised control strategies based on distributed parameter model predictive controllers are designed, implemented and analysed via simulation environment. State observers are also designed to estimate internal unmeasurable spatial profiles necessary for the control action. The aim of the first strategy is to monitor and control observed water activity spatial profiles on both sides of the membrane to appropriate levels. These target values are carefully chosen to combine proper membrane, catalyst layer and gas diffusion layer humidification, whilst the rate of accumulation of excess liquid water is reduced. The key objective of this approach is to decrease the frequency of water removal actions that cause disruption in the power supplied by the cell, increased parasitic losses and reduction of cell efficiency. The second strategy is a variation of the previous water activity control strategy, which includes the control of spatial distribution of gases in the fuel and air channels. This integrated solution aims to avoid starvation of reactants by controlling corresponding concentration spatial profiles. This approach is intended to prevent PEM fuel cell degradation due to corrosion mechanisms, and thermal stress caused by the consequences of reactant starvation. The results show increased fuel cell performance considering the spatial control approaches proposed in this Thesis, in comparison to non-spatial control strategies. Moreover, the decentralised feature of the control scheme, combined with the use of order-reduced models within the model predictive controllers, has important impact on the overall control performance. [ES]: A pesar de los avances actuales, la tecnología de celdas de hidrógeno tipo PEM no está suficientemente preparada para ser ampliamente introducida en el mercado energético. Rendimiento, durabilidad y costo son los mayores retos. El rendimiento y la durabilidad de las celdas dependen significativamente de las variaciones en las concentraciones de hidrógeno y oxígeno en los canales de alimentación de gases, la humedad relativa en las capas catalizadoras, el contenido de agua de la membrana polimérica, así como la temperatura, entre otras variables. Dichas variables presentan dependencia espacial interna en la dirección del flujo de gases del ánodo y del cátodo. Distribuciones espaciales altamente no uniformes en algunas variables de la celda resultan en sobrecalentamiento local, inundación, degradación acelerada y menor potencia de la requerida. Muy pocos trabajos disponibles en la literatura se ocupan del control de perfiles espaciales. La mayoría de los diseños orientados a control usan modelos de parámetros concentrados que ignoran la dependencia espacial de variables internas de la celda, debido a la complejidad que añaden al funcionamiento de controladores. En contraste, esta Tesis Doctoral trata la modelización y control de parámetros distribuidos en las celdas de hidrógeno tipo PEM. En la parte de modelización, esta tesis presenta el desarrollo detallado de un modelo no lineal de parámetros distribuidos para una sola celda, el cual incorpora las variaciones espaciales de todas las variables que son relevantes para su correcto funcionamiento. El modelo se usa primero para analizar importantes perfiles espaciales internos, y luego se simplifica para reducir su complejidad computacional y adecuarlo a propósitos de control. En esta tarea se usan y se comparan dos técnicas de reducción de orden de modelos. El propósito de la parte de control es abordar la gestión de agua y el suministro de reactantes, que son dos grandes retos en el funcionamiento de las celdas con importantes consecuencias para su vida útil. En esta parte de la tesis, dos estrategias de control descentralizadas, basadas en controladores predictivos de modelos de referencia con parámetros distribuidos, son diseñadas, implementadas y analizadas en un entorno de simulación. Estas tareas incluyen también el diseño de observadores de estado que estiman los perfiles espaciales internos necesarios para la acción de control. El objetivo de la primera estrategia es monitorear y controlar perfiles espaciales observados de la humedad relativa en las capas catalizadoras para mantenerlos en niveles apropiados. Estos niveles son escogidos cuidadosamente para combinar la correcta humidificación de la membrana y las capas catalizadoras, reduciendo la velocidad de acumulación de agua líquida. El objetivo clave de este enfoque es disminuir la frecuencia de las acciones de remoción de agua dentro de la celda, ya que estas acciones causan interrupción en la potencia suministrada, aumento de las cargas parasitarias y disminución de la eficiencia. La segunda estrategia es una variación de la estrategia anterior que considera adicionalmente el control de la distribución espacial de los gases en los canales del ánodo y cátodo. Esta solución integrada tiene como objetivo evitar la ausencia local de reactantes mediante el control de perfiles espaciales de concentración de gases. Este enfoque pretende prevenir la degradación de las celdas debido a mecanismos de corrosión. Los resultados muestran un mayor rendimiento de la celda considerando los enfoques de control de perfiles espaciales propuestos en esta tesis, en comparación con técnicas de control que ignoran dichos perfiles. Además, la característica descentralizada de los esquemas de control, combinada con el uso de modelos reducidos dentro de los controladores predictivos, tiene un impacto positivo importante en el rendimiento general del control. This work was partially funded by Spanish MEC-FPU Grant number AP-2010-3969, the Spanish national projects MESPEM (DPI2011-25649) and MICAPEM (DPI2015-69286-C3-2-R, MINECO/FEDER) and the European project PUMA-MIND (FP7-FCH-JU-2011-1- 303419).

Solenoid current regulation is well-known and standard in any proportional electro-hydraulic valve. The goal is to provide a wide-band transfer function from the reference to the measured current, thus making the solenoid a fast and ideal force actuator within the limits of the power supplier. The power supplier is usually a Pulse Width Modulation (PWM) amplifier fixing the voltage bound and the Nyquist frequency of the regulator. Typical analog regulators include three main terms: a feedforward channel, a proportional feedback channel and the electromotive force compensation. The latter compensation may be accomplished by integrative feedback. Here the problem is faced through a model-based design (Embedded Model Control), on the basis of a wide-band embedded model of the solenoid which includes the effect of eddy currents. To this end model parameters must be identified. The embedded model includes a stochastic disturbance dynamics capable of estimating and correcting the electromotive contribution together with parametric uncertainty, variability and state dependence. The embedded model which is fed by the measured current and the supplied voltage becomes a state predictor of the controllable and disturbance dynamics. The control law combines reference generator, state feedback and disturbance rejection to dispatch the PWM amplifier with the appropriate duty cycle. Modeling, identification and control design are outlined together with experimental result. Comparison with an existing analog regulator is also provided.

To realize stable production in the steel industry, it is important to control molten steel temperature in a continuous casting process. The present work aims to provide a general framework of gray-box modeling and to develop a gray-box model that predicts and controls molten steel temperature in a tundish (TD temp) with high accuracy. Since the adopted first-principle model (physical model) cannot accurately describe uncertainties such as degradation of ladles, their overall heat transfer coefficient, which is a parameter in the first-principle model, is optimized for each past batch separately, then the parameter is modeled as a function of process variables through a statistical modeling method, random forests. Such a model is termed as a serial gray-box model. Prediction errors of the first-principle model or the serial gray-box model can be compensated by using another statistical model; this approach derives a parallel gray-box model or a combined gray-box model. In addition, the developed gray-box models are used to determine the optimal molten steel temperature in the Ruhrstahl–Heraeus degassing process from the target TD temp, since the continuous casting process has no manipulated variable to directly control TD temp. The proposed modeling and control strategy is validated through its application to real operation data at a steel work. The results show that the combined gray-box model achieves the best performance in prediction and control of TD temp and satisfies the requirement for its industrial application.

The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 321132, project TRAMAN21. Summarization: The presence and exploitation of Vehicle Automation and Communication Systems (VACS) while defining optimal control strategies in motorway traffic flow control is addressed in this paper. VACS are supposed to act both as sensors (providing information on traffic conditions) and as actuators, allowing the application of ramp metering, variable speed limit control, and lane changing control. A quadratic programming problem is defined on the basis of a novel first-order traffic flow model for multi-lane motorways. An example is presented in order to illustrate the effectiveness of the proposed optimisation problem. Παρουσιάστηκε στο: 19th IFAC World Congress

The application of closed-loop control systems in biomedicine unlocks prospects for optimized drug delivery based on the measurement of patients’ physiological variables. However, inter-individual variability and narrow therapeutic indexes are issues that must be carefully considered. We propose an in silico study of a model-based controller of anesthesia and analgesia with propofol and remifentanil, based on bispectral index (BIS) and mean arterial pressure (MAP) measurements. A physiologically-based pharmacokinetic (PBPK) model, combined with a suitable pharmacodynamic model, allows describing and differentiating the dose-effect dependency for the virtual patients. The controller delivers a safe and fast induction of anesthesia, with mean rise times below 3 min and controlled variables within the clinical safe ranges. The PBPK model allows gaining complementary information about the dynamics of the drugs absorption, distribution, metabolism, and elimination in the body. Special attention is devoted to simulate realistic intraoperative surgical stimuli and noise on the controlled variables. The controller successfully rejects disturbances on BIS and MAP related to nociceptive stimuli (e.g., intubation and incision) via a robust control action, and is not diverted by noise.

This paper presents a comparison of two different adaptive control schemes for improving the performance of a hybrid wind-diesel power system in an islanded microgrid configuration against the baseline controller, IEEE type 1 automatic voltage regulator (AVR). The first scheme uses a model reference adaptive controller (MRAC) with a proportional-integral-derivative (PID) controller tuned by a genetic algorithm (GA) to control the speed of the diesel engine (DE) for regulating the frequency of the power system and uses a classical MRAC for controlling the voltage amplitude of the synchronous machine (SM). The second scheme uses a MRAC with a PID controller tuned by a GA to control the speed of the DE, and a MRAC with an artificial neural network (ANN) and a PID controller tuned by a GA for controlling the voltage amplitude of the SM. Different operating conditions of the microgrid and fault scenarios in the diesel engine generator (DEG) were tested: 1) decrease in the performance of the diesel engine actuator (40% and 80%), 2) sudden connection of 0.5 MW load, and 3) a 3-phase fault with duration of 0.5seconds. Dynamic models of the microgrid components are presented in detail and the proposed microgrid and its fault-tolerant control (FTC) are implemented and tested in the Simpower Systems of MATLAB/Simulink® simulation environment. The simulation results showed that the use of ANNs in combination with model-based adaptive controllers improves the FTC system performance in comparison with the baseline controller.

This paper presents a multi-level approach to the problem of modelling different thermal zones in a house for control applications. This problem has been treated before by modelling the whole house with a single, all-inclusive RC circuit which may have different levels of resolution. The core of the proposed methodology lies in the possibility of allowing the user to switch back and forth between models representing different control levels according to the modelling objectives. For the development of specific control algorithms for each zone, the house can be treated as a collection of interconnected zonal models, as opposed to a single, large model. This modelling approach has the advantage of maintaining a simple structure for each zone, while also taking into account the heat transfer between zones; at this control level, issues such as occupancy, thermal comfort or setpoint profiles can be examined in detail. On the other hand, if the user is interested in a quick estimate of global variables (e.g., overall thermal load over the next 24 h) then different zones or even the entire house may be combined into a single low-order model. In summary, this multi-level approach allows the user to “zoom in and out” so that models at each control level remain manageable, easy to calibrate and easy to physically interpret. This paper uses data from an existing unoccupied test house, representative of a typical family home in Québec, as a case study. Four zones are considered: basement, main floor, upper floor and the attached garage. For the most detailed analysis, these zones are modelled with four interconnected zone models. Alternative ways of combining zones are investigated. A global low-order house model is used to calculate the thermal load of the building. Results of thermal load calculations are compared and discussed.

The aim of this paper is to illustrate the application of the previously proposed [1] Runge-Kutta (RK) model based nonlinear model predictive control method to the Continuously Stirred Tank Reactor (CSTR) system. The method utilizes the so-called Runge-Kutta model of a continuous-time nonlinear system as an approximate discrete model and employes it in the Model Predictive Control (GPC) loop. In the proposed method, the RK model is not only used for control purposes, but also it is utilized for state estimation in the Extended Kalman Filter (EKF) framework and on-line parameter adaptation. The results show the effectiveness of the method in control, state estimation and parameter adaptation tasks. © 2012 IFAC.

Selective catalytic reduction (SCR) is a promising diesel aftertreatment technology that enables low nitrogen oxides $({rm NO}_{rm x})$ tailpipe emissions with relatively low fuel consumption. Future emission legislation is pushing the boundaries for SCR control systems to achieve high ${rm NO}_{rm x}$ conversion within a tailpipe ammonia $({rm NH}_{3})$ slip constraint, and to provide robustness to meet in-use compliance requirements. This work presents a new adaptive control strategy that uses an ammonia feedback sensor and an online ammonia storage model. Experimental validation on a 12-liter heavy-duty diesel engine with a 34-liter Zeolite SCR catalyst shows good performance and robustness against urea under- and over-dosage for both the European steady-state and transient test cycles. The new strategy is compared with a ${rm NO}_{rm x}$ sensor-based control strategy with cross-sensitivity compensation. It proved to be superior in terms of transient adaptation and taking an ${rm NH}_{3}$ slip constraint into account.

The numerical evaluation and optimization of the feedback controller parameters of the model-based control implemented in the flying helicopter simulator is subject of this paper. The German Aerospace Center operates this helicopter as a flying testbed for numerous applications, e.g., pilot assistance and in-flight simulation. Initially, the elements of the model-based control are presented. A genetic algorithm and the Nelder–Mead simplex method used for optimization are described. Two simple objective functions to rate parameter sets in the time domain are presented, and a Simulink® model of the helicopter dynamics and the controller structure are used to find optimized sets. The first function, called “Delta Rating”, consists of a normalized integral of the absolute error between commanded and measured states. The second function incorporates the Delta Rating, but is enhanced by a penalty on overshoots. The controllers found are further evaluated using a frequency domain approach consisting of a weighted sum of the differences in amplitude and phase, also considering the coherence at the corresponding frequency. Apart from the Simulink® model, a ground-based simulator is used to evaluate the standard and the optimized controllers.