Search

This paper addresses the control of a nonlinear system affected by deadzone effects, using a constrained actuator. The system itself incorporates a second-order oscillatory dynamic actuator, with an unknown nonlinear input-output relationship. The proposed algorithm not only accommodates the deadzone constraints on control inputs but also considers the actuator's saturation limits in control input calculations. It introduces a trajectory tracking mechanism that, instead of directly following the primary trajectory, adheres to an alternative trajectory capable of stable tracking, gradually converging to the main trajectory while accounting for operational constraints. In practical control systems, the actuator's input-output relationship is often nonlinear and unknown, requiring inversion for model-based control. This paper employs an offline-trained neural network trained on synthetic data to identify and approximate the actuator's behavior. To optimize the control system's performance and ensure stability during sudden error changes, the control input operates in two modes: position and velocity control. This dual-mode control allows for continuous switching between the two, facilitated by an innovative optimization technique based on the gradient descent method with a variable step size. Simulation results validate the effectiveness of the proposed algorithm in controlling systems constrained by hard limits and featuring nonlinear oscillatory actuators, providing a valuable contribution to the field of control systems. • Developing a dual mode non-predictive control algorithm based on discrete-time system equations. • Utilizing an offline-trained neural network for estimating the inverse of the nonlinear actuator relationship. • Stable desired trajectory mapping based on inherent and actuation constraints while maintaining asymptotic stability. • Substituting the trajectory within a fixed range outside of the constant deadzone and inside the variable saturation. • Optimized balancing position and velocity control modes through a weighted average of two control forces. [ABSTRACT FROM AUTHOR]

Model-based control shows promising potential for engine performance improvement and future aero-propulsion requirements. In this paper, an auto-updating thrust variation mitigation (AuTVM) control approach using on-board model strategies is proposed for gas turbine aero-engines under in-service degradation effects, which aims at active thrust regulation and acceleration protection in a simultaneous way. The AuTVM control is integrated with an on-line block, based on a reliable on-board engine model, and an off-line part for the periodical update of control parameters via post-flight engine monitoring data. The core feature of the AuTVM control is a set of auto-updating loops within the on-line part, including thrust regulation loop, surge margin loop, turbine entry temperature loop, and the steady loop, whose control parameters are periodically adjusted with increasing flight cycles. Meanwhile, an industrial sensor-based baseline controller and two tailored model-based controllers, i.e., a thrust variation mitigation (TVM) controller with fixed gains and a self-enhancing active transient protection (SeATP) controller with pro-active transient protection and passive thrust control, are also developed as comparison bases. Numerical simulations for idle to full-power acceleration tests are carried on a validated aero-thermal turbofan engine model using publicly available degradation data. Simulation results demonstrate that both new engines and severely degraded engines regulated by the AuTVM controller show significant thrust response enhancement, compared to the baseline controller. Moreover, thrust variation at the maximum steady state of degraded engines, which exists within the SeATP controller and the baseline controller, is suppressed by the proposed AuTVM controller. Robustness analysis against degradation uncertainties and sensor accuracy confirms that the AuTVM controller owns a closer maximum steady-state thrust distribution to the desired value than those of the SeATP and the baseline controller while utilizing transient margins of controlled engines more effectively. Hence, the control performance of the AuTVM controller for in-service engines is guaranteed.

K-Maryblyt has been developed for the effective control of secondary fire blight infections on blossoms and the elimination of primary inoculum sources from cankers and newly emerged shoots early in the season for both apple and pear trees. This model facilitates the precise determination of the blossom infection timing and identification of primary inoculum sources, akin to Maryblyt, predicting flower infections and the appearance of symptoms on various plant parts, including cankers, blossoms, and shoots. Nevertheless, K-Maryblyt has undergone significant improvements: Integration of Phenology Models for both apple and pear trees, Adoption of observed or predicted hourly temperatures for Epiphytic Infection Potential (EIP) calculation, incorporation of adjusted equations resulting in reduced mean error with 10.08 degree-hours (DH) for apple and 9.28 DH for pear, introduction of a relative humidity variable for pear EIP calculation, and adaptation of modified degree-day calculation methods for expected symptoms. Since the transition to a model-based control policy in 2022, the system has disseminated 158,440 messages related to blossom control and symptom prediction to farmers and professional managers in its inaugural year. Furthermore, the system has been refined to include control messages that account for the mechanism of action of pesticides distributed to farmers in specific counties, considering flower opening conditions and weather suitability for spraying. Operating as a pivotal module within the Fire Blight Forecasting Information System (FBcastS), K-Maryblyt plays a crucial role in providing essential fire blight information to farmers, professional managers, and policymakers.

Copyright of BHM Berg- und Hüttenmännische Monatshefte is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Digital twin (DT) is a virtual and digital representation of physical objects or processes. In this paper, this concept is applied to dynamic control of the collection window in the ion exchange chromatography (IEC) toward sample variations. A possible structure of a feedforward model‐based control DT system was proposed. Initially, a precise IEC mechanistic model was established through experiments, model fitting, and validation. The average root mean square error (RMSE) of fitting and validation was 8.1% and 7.4%, respectively. Then a model‐based gradient optimization was performed, resulting in a 70.0% yield with a remarkable 11.2% increase. Subsequently, the DT was established by systematically integrating the model, chromatography system, online high‐performance liquid chromatography, and a server computer. The DT was validated under varying load conditions. The results demonstrated that the DT could offer an accurate control with acidic variants proportion and yield difference of less than 2% compared to the offline analysis. The embedding mechanistic model also showed a positive predictive performance with an average RMSE of 11.7% during the DT test under >10% sample variation. Practical scenario tests indicated that tightening the control target could further enhance the DT robustness, achieving over 98% success rate with an average yield of 72.7%. The results demonstrated that the constructed DT could accurately mimic real‐world situations and perform an automated and flexible pooling in IEC. Additionally, a detailed methodology for applying DT was summarized. [ABSTRACT FROM AUTHOR]

This work concerns the application of physics‐informed neural networks to the modeling and control of complex robotic systems. Achieving this goal requires extending physics‐informed neural networks to handle nonconservative effects. These learned models are proposed to combine with model‐based controllers originally developed with first‐principle models in mind. By combining standard and new techniques, precise control performance can be achieved while proving theoretical stability bounds. These validations include real‐world experiments of motion prediction with a soft robot and trajectory tracking with a Franka Emika Panda manipulator. [ABSTRACT FROM AUTHOR]

It is desirable that batch coffee roasters be fully automated. This article offers an automation solution for industrial practitioners in the coffee industry, namely a control strategy for rotating-drum batch coffee roasters. The control strategy can be applied to standard PI-PID controller systems to automate artisan coffee roasters. It can consequently be implemented with minor adjustments to artisan batch coffee roasters that are currently in use. Controller parameters were experimentally determined using IMC, Cohen-Coon, and ITAE-based parameter techniques, and they were validated using Simulink®. Bode plots confirmed controller stability. The roasting process was classified as a lag-dominant, first-order plus time delay process and approximated; therefore, it was modeled as a pure integrating system. The batch roasting process was successfully controlled using a novel controlled variable, namely the roast profile derivative, and a final control strategy that switches between two loops is proposed for real-world application.

Safety and stability considerations play a crucial role in the development of learning based strategies for control design of systems that require high levels of safety. Safe reinforcement learning (RL) based approaches traditionally seek learning of the control laws that are optimal with respect to system performance whilst ensuring system stability and safety. In this article, an off‐policy safe RL based approach is proposed for nonlinear systems affine in control in continuous time. In this novel work, safety and stability are guaranteed during initialization and exploration phases by adjusting the control input with the solution of a quadratic programming problem combining both input to state stable‐control Lyapunov function and robust control barrier function (R‐CBF) conditions. Moreover, the safety of the learned policy is assured by augmenting the cost function with a CBF to maintain safety and optimize performance simultaneously. Novel mathematically rigorous proofs are provided to establish the stability and safety guarantees, offering a sound theoretical foundation for the approach. To demonstrate the effectiveness of the algorithm, two examples are presented: engine surge and stall dynamics, and an unstable nonlinear system. [ABSTRACT FROM AUTHOR]

In this paper, the problem of model-based finite-time bounded event-triggered control for distributed fuzzy T-S systems is presented. For this purpose, the whole network model is embedded locally in both the controller and the remote telemetry unit. In order to estimate the states of the plant between two consecutive events, a fuzzy observer has been used. Model-based state estimation reduces the state error and consequently leads to reduction of data transmission instants. By the network model and event triggering block which are placed locally in each remote telemetry unit, the time of data transmission on the distributed network is determined. Finally, the finite-time boundedness of the closed-loop system has been investigated using MATLAB software for a centralized and a distributed system, respectively. [ABSTRACT FROM AUTHOR]

Nowadays, electric motors are common in many precision instrument industries that require high-precision control. If a suitable model of a servo system is available, accurate results can be obtained using a model-based control algorithm. The DC motor is one of the most widely used systems in precision industries, for which a high-precision controller can be designed using its mathematical model with feedback on position and velocity. In such systems and other rotating systems, it is necessary to consider friction in the control system design. Friction enters the system in the form of resistant force or torque in both static and kinetic states, which causes uncertainty in the dynamic modeling of the system, the stick-slip phenomenon, and creates a deadzone in the actuation system. In this article, considering a switching dynamic model for different states of a system subject to friction which has a saturation limit on the control input and an actuation system dependent on the angular velocity feedback, first, the stable reaching laws in two modes of position control and velocity control for different dynamic model switch modes are defined. Then, the control input that forces the tracking error to follow the corresponding reaching law for each control mode is calculated. In the next step, according to the limitations of the actuation system, such as saturation and deadzone, using the filtering mechanism and optimal search for each control mode, the desired values are filtered and it is guaranteed that the control input will be chosen within the allowed range. Finally, using the optimal switching mode selection mechanism, the optimal control mode is selected and the voltage is calculated as the next step input. [ABSTRACT FROM AUTHOR]

This study presents a model-based deep reinforcement learning (MB-DRL) controller for the fluidized bed biomass gasification (FBG) process. The MB-DRL controller integrates a deep neural network (DNN) model and a reinforcement learning-based optimizer. The DNN model is trained with operational data from a pilot-scale FBG plant to approximate FBG process dynamics. The reinforcement learning-based optimizer employs a specially designed reward function, determining optimal control policies for FBG. Moreover, the controller includes an online learning component, ensuring periodic updates to the DNN model training. The performance of the controller is evaluated by testing its control accuracy for regulating synthetic gas composition, flow rate, and CO concentration in the FBG. The evaluation also includes a comparison with a model predictive controller. The results demonstrate the superior control performance of MB-DRL, surpassing MPC by over 15% in regulating synthetic gas composition and flow rate, with similar effectiveness observed in synthetic gas temperature control. Additionally, this study also includes systematic investigations into factors like DNN layer count and learning update intervals to provide insights for the practical implementation of the controller. The results, presenting a 50% reduction in control error with the addition of a single layer to the DNN model, highlight the significance of optimizing MB-DRL for effective implementation. [ABSTRACT FROM AUTHOR]

This work concerns the application of physics‐informed neural networks to the modeling and control of complex robotic systems. Achieving this goal requires extending physics‐informed neural networks to handle nonconservative effects. These learned models are proposed to combine with model‐based controllers originally developed with first‐principle models in mind. By combining standard and new techniques, precise control performance can be achieved while proving theoretical stability bounds. These validations include real‐world experiments of motion prediction with a soft robot and trajectory tracking with a Franka Emika Panda manipulator.

In this paper, a multistage DC-DC buck converter with a high reduction ratio, based on the concept of switched inductance, is presented. The proposed converter offers a significantly higher reduction ratio than conventional step-down converters. The analysis and design of the proposed multistage converter are considered for three operating regions, i.e., continuous conduction mode, boundary conduction mode, and discontinuous conduction mode. Besides, an averaged model is obtained, based on the analysis of the conduction modes. Also, a model-based control strategy is proposed to meet the proposed control objectives. Thus, a control law is designed that results in a two-control loop controller, i.e., a current control loop and a voltage control loop are proposed. A comparison is carried out in terms of efficiency, number of components, and type of control to highlight the advantages and disadvantages of the proposed converter. Finally, an experimental prototype is built to assess the proposal performance, reducing from an input voltage of 200 V to an output voltage of 12 V. The closed-loop converter is evaluated in an experimental setup under steady-state and transient conditions.

This paper investigates the high-precision sliding mode based tracking control of robot manipulators with uncertain dynamics and external disturbances. Different from most currently used fast nonsingular terminal sliding mode surfaces (FNTSMs) which use linear sliding mode (LSM) to avoid singularity, a new recursive terminal sliding mode surface (RTSM) is firstly constructed with a recursive structure to avoid the singularity problem in this paper. The RTSM can improve convergence precision and speed near the equilibrium point compared with FNTSMs. Then a sliding mode based controller has been designed to stabilize the closed-loop system. To cope with model uncertainties, frictions and external disturbances, a novel adaptive sliding mode disturbance observer (ASMDO) has been constructed, which can estimate lumped uncertainties and feed them to the controller to achieve disturbance-rejection control. Compared with traditional disturbance observer with asymptotic stability, the observation error of ASMDO can be driven into a sufficiently small set centered on zero within a predefined fixed time, which means fast observation speed and high observation accuracy. The upper bounds of uncertainties and their derivatives are not needed in observer design. Abundant simulations and experiments also verified the effectiveness and superior properties of the proposed scheme. [ABSTRACT FROM AUTHOR]

The safety, health, and well-being of human workers are crucial for socially sustainable production systems, especially in Industry 5.0. Occupational exoskeletons, particularly back-support devices, are increasingly being adopted to reduce musculoskeletal disorders and human fatigue. To reduce costs and weight, optimized exoskeleton design is being explored. A 3D-printed cycloidal reduction stage for the actuation unit is proposed, focusing on an interaction torque observer and an impedance-based controller for human-robot interaction. The device's dynamic characterization and control are analyzed to evaluate its applicability to a sensorless back-support occupational exoskeleton. [ABSTRACT FROM AUTHOR]

Control technology for engines and/or powertrains is becoming increasingly important for achieving higher energy efficiency and lower CO2 emissions in the real world in terms of the carbon neutrality of automobiles. CO2 emissions can be reduced using advanced combustion technologies and/or powertrain systems including hybrid powertrains. Such advanced combustion technologies show low robustness to changes in operation conditions, and advanced powertrain systems are difficult to optimize for complicated systems and uncertainty in the real road driving. Therefore, control technology is essential for taking full advantage of such hardware capabilities. Furthermore, the effective use of various information that is currently available, such as that on traffic signals, maps, and other cars, could lead to further reductions in CO2 emissions and energy consumption. This review summarizes the research on control technology, especially for advanced combustion and powertrain systems, and discusses the role of powertrain control and its future prospects. [ABSTRACT FROM AUTHOR]

This work proposes an Arduino-based real-time adaptive speed control of a 3D-printed permanent magnet synchronous motor (PMSM). The PMSM is designed and printed in PETG using a nine-pair poles structure with a Halbach array approach for the magnets. A modularity strategy is used in the design that facilitates that every part of the PMSM can be printed separately. The PMSM speed control is designed based on an identification stage used to model the PMSM. As a result, an adaptive transfer function is obtained from the measurements by assuming that the input is the pulse-width modulation (PWM) and the output is the rotational speed of the PMSM. Finally, the embedded model-based controller is designed based on the identified system. The system is evaluated in simulation and over a real prototype by considering speed reference changes in settling time and tracking error. Additional evaluation of the real 3D-printed PMSM performance is obtained by considering the dead zone of the system and the control effort. Finally, a thermographic test is applied over the 3D-printed PMSM to validate the material’s performance under steady-state behavior. It is worth noting that the system reaches the speed reference around nine seconds, and the temperature delta in steady state is around two Celsius degrees, which validates the performance of the 3D-printed PMSM prototype and its model-based speed controller. [ABSTRACT FROM AUTHOR]

This paper presents the analysis of bilinear force/position control (BFC) schemes for the guaranteed force tracking performance of a robot manipulator under unknown environment. Borrowing the concept of impedance force control and hybrid force control, BFC schemes are formulated by combining two force control algorithms. The proposed BFC scheme guarantees the desired force/position tracking performance for any environment with the help of a model-based control method by achieving independent axis control. Guaranteed force tracking control performances of three different bilinear functions are presented and analysed. Their performances are tested and compared without knowing any information on the environment such as position and stiffness a priori. Simulation studies of BFC tracking performances for a robot manipulator to follow the sinusoidal trajectory while regulating a desired force on the environment are performed to verify the practical force tracking control performance. [ABSTRACT FROM AUTHOR]

Copyright of Engineering Mechanics / Gongcheng Lixue is the property of Engineering Mechanics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper introduces a model-based control scheme for controlling the position of a suspended cable-driven parallel robot. The robot is designed to have a fixed frame base with four cables. The cables are attached to winches on one end, driven by stepper motors, and to a moving platform at the other end. The control scheme consists of two systems: the reference model and the implemented control. The implemented control hosts the stepper motor to drive the winch based on the requirements derived from the reference model. The reference model converts the desired Cartesian trajectory into joint spaces, which are then translated into the number of required steps. The number of steps will act as a set point for the stepper motor. Three trajectories are generated to test the compliance of the controller with its position. The error compensation scheme is introduced to increase the positional accuracy of the previous controller, especially on the z-axis. This algorithm uses the nature of discrete stepper motor movement to estimate the actual cable length, which is then fed back to the control system as an error. The control simulation results indicate a significant improvement in control performance, i.e. reduced position error, was achieved.

Colorectal cancer (CRC) is the third most common cancer worldwide and responsible for approximately 1 million deaths annually. Early screening is essential to increase the chances of survival, and it can also reduce the cost of treatments for healthcare centres. Colonoscopy is the gold standard for CRC screening and treatment, but it has several drawbacks, including difficulty in manoeuvring the device, patient discomfort, and high cost. Soft endorobots, small and compliant devices thatcan reduce the force exerted on the colonic wall, offer a potential solution to these issues. However, controlling these soft robots is challenging due to their deformable materials and the limitations of mathematical models. In this Review, we discuss model-free and model-based approaches for controlling soft robots that can potentially be applied to endorobots for colonoscopy. We highlight the importance of selecting appropriate control methods based on various parameters, such as sensor and actuator solutions. This review aims to contribute to the development of smart control strategies for soft endorobots that can enhance the effectiveness and safety of robotics in colonoscopy. These strategies can be defined based on the available information about the robot and surrounding environment, control demands, mechanical design impact and characterization data based on calibration.

Three-dimensional concrete printing technology is currently a very topical and developing subject. There is a large number of applications worldwide where this technology can be used. In connection with this technology, the development of custom industrial robotic systems and their control is essential. Conventional closed-loop control system platforms do not provide sufficiently flexible solutions. This paper presents a control system for a unique printing robot that, thanks to its openness and unified platform, will enable simple and fast analysis and testing of key aspects in terms of control and guidance of the printing robot for additive manufacturing applications in the construction industry. The aim of this paper is to introduce the concept of an open PLC-based control system and to demonstrate its usefulness in the task of designing and implementing model-based control. All steps, from the analysis of the printing robot itself and identification of inertial parameters to the actual design and implementation of the control, can be executed in a unified Matlab/Simulink environment using various add-ons and toolboxes thanks to the open control system platform. This solution brings significant savings in terms of programming and prototyping time. The open control system was used to control an experimental model of a printing robot, serving as a test bed for the final version of the printing robot, and the results obtained were evaluated. [ABSTRACT FROM AUTHOR]

Artificial intelligence and mechanical engineering are two mature fields of science that intersect more and more often. Computer-aided mechanical analysis tools, including multibody dynamics software, are very versatile and have revolutionalized many industries. However, as shown by the literature presented in this review, combining the advantages of multibody system dynamics and machine learning creates new and exciting possibilities. For example, the multibody method can assist machine learning by providing synthetic data, while machine learning can provide fast and accurate subsystem models. The intersection of both approaches results in surrogate and hybrid modeling techniques, advanced control algorithms, and optimal design applications. A notable example is the development of autonomous systems for vehicles, robots, and mobile machinery. In our review we have found nontrivial, innovative, and even surprising applications of machine learning and multibody dynamics. This review focuses on applying neural networks, mainly deep learning, in connection with the multibody system method. Over one hundred and fifty papers are covered, and three main research areas are identified and introduced: data-driven modeling, model-based control and estimation, and data-driven control. The paper starts with a primer on machine learning and concludes with future research directions. The main goal is to provide a comprehensive and up-to-date review of existing literature to inspire further research. [ABSTRACT FROM AUTHOR]

This paper presents an investigation into the potential use of subspace identification methods (SIMs) for model-based control of urban drainage systems (UDS) that play a crucial role in collecting and transporting stormwater runoff and domestic sewage to Water Resource Recovery Facilities (WRRF) in urban areas. To evaluate the feasibility of level control using model-based algorithms in UDS, a pilot tunnel system was constructed. Three linear state-space models were identified using the system identification toolbox in MATLAB and an open-source module in Python named SIPPY. The study finds that the identified models can predict the system output with acceptable accuracy thus for model-based control of the system. The findings of this study aim to contribute to the development of more efficient and effective control strategies for UDS.

Autonomous driving has the ability to reshape mobility and transportation by reducing road accidents, traffic jams, and air pollution. This can yield energy efficiency, convenience, and more productivity as significant driving time will be gained and used in other activities instead. Autonomous vehicles are complex systems consisting of several modules that perform perception, decision‐making, planning, and control. Control is essential for achieving automatic driving; it is basically divided into longitudinal control that handles speed tracking and lateral control which ensures accurate steering. The latter is primordial in path tracking applications and recent research has witnessed a huge leap in this field. The aim of this paper is to provide a technical survey of the latest research on the lateral control of autonomous vehicles as well as to highlight technical challenges and limits for further developments. [ABSTRACT FROM AUTHOR]

Injection molding is a critical component of modern industrial operations, and achieving fast and stable control of injection molding machines (IMMs) is essential for producing high-quality plastic products. This paper focuses on solving an optimal tracking control problem of the injection velocity that arises in a typical nonlinear IMM. To this end, an efficient optimal robust controller is proposed and designed. The nonlinear injection velocity servo system is first approximately linearized at iteration points using the first-order Taylor expansion approach. Then, at each time node in the optimization process, the relevant algebraic Riccati equation is introduced, and the solution is used to construct an optimal robust feedback controller. Furthermore, a rigorous Lyapunov theorem analysis is employed to demonstrate the global stability properties of the proposed feedback controller. The results from numerical simulations show that the proposed optimal robust control strategy can successfully and rapidly achieve the best tracking of the intended injection velocity trajectory within a given time. [ABSTRACT FROM AUTHOR]

Robots operating in human environments require a diverse set of skills, including slow and fast walking, turning, side-stepping, and more. However, developing robot controllers capable of exhibiting such a broad range of behaviors is a challenging problem that necessitates meticulous investigation for each task. To address this challenge, we introduce a trajectory optimization method that resolves the kinematic infeasibility of reference animal motions. This method, combined with a model-based controller, results in a unified data-driven model-based control framework capable of imitating various animal gaits without the need for expensive simulation training or real-world fine-tuning. Our framework is capable of imitating a variety of motor skills such as trotting, pacing, turning, and side-stepping with ease. It shows superior tracking capabilities in both simulations and the real world compared to other imitation controllers, including a model-based one and a learning-based motion imitation technique. [ABSTRACT FROM AUTHOR]

This paper demonstrates a multiple-input multiple-output (MIMO) controller design framework and a controller switching algorithm for MIMO controllers in their state-space form, which together achieve robust, efficient control of turbocharged lean-burn engines over a wide operating space. The controller design framework requires a linearized plant model, and uses the μ -synthesis and DK-iteration algorithms while considering state and output uncertainties and actuator bandwidths to synthesize a robust H ∞ controller. A controller switching methodology using slow-fast controller decomposition and also incorporating hysteresis at switching points is utilized to smoothly transfer control authority between several MIMO controllers. The approach is applied to a high-fidelity truth-reference GT-Power engine model for a lean-burn natural gas-fueled engine to evaluate the closed-loop controller performance. The multi-tracking control problem targets engine speed, differential pressure across throttle as well as air-to-fuel ratio to achieve satisfactory engine performance and emissions without compressor surge. The engine response obtained using the robust MIMO controller is compared with that obtained using a state-of-the-art benchmark controller to evaluate the additional benefits of the MIMO controller. [ABSTRACT FROM AUTHOR]

This paper introduces a model-based control scheme for controlling the position of a suspended cable-driven parallel robot. The robot is designed to have a fixed frame base with four cables. The cables are attached to winches on one end, driven by stepper motors, and to a moving platform at the other end. The control scheme consists of two systems: the reference model and the implemented control. The implemented control hosts the stepper motor to drive the winch based on the requirements derived from the reference model. The reference model converts the desired Cartesian trajectory into joint spaces, which are then translated into the number of required steps. The number of steps will act as a set point for the stepper motor. Three trajectories are generated to test the compliance of the controller with its position. The error compensation scheme is introduced to increase the positional accuracy of the previous controller, especially on the z-axis. This algorithm uses the nature of discrete stepper motor movement to estimate the actual cable length, which is then fed back to the control system as an error. The control simulation results indicate a significant improvement in control performance, i.e. reduced position error, was achieved. [ABSTRACT FROM AUTHOR]

Voltage control is critical for the performance of proton exchange membrane fuel cells. However, accurately controlling voltage is challenging, specifically during the current variation. The present study proposes a novel multiple-input single-output (MISO) control structure for a proton exchange membrane fuel cell system to improve performance. Also, this study focuses on airflow optimization and hydrogen consumption optimization, as the literature focuses more on airflow optimization for compressor or pump performance. Firstly, using a genetic algorithm optimization technique, the fractional order model is realized from the existing integer order fuel cell model. Then, the proposed control structure aims to control the output cell voltage by regulating the air and hydrogen inlet rates by designing various model-based controllers for integer and fractional order models. The control performance is evaluated for set point tracking, disturbance rejection, inverse response rejection and time delay compensation. The simulation results show that the fractional order system is observed to give better results than the integer order system, with model predictive controller showing the best results for controlling the stack voltage. [ABSTRACT FROM AUTHOR]

Primary drying is the bottleneck of pharmaceutical lyophilization, as it represents the most time and energy-consuming stage and largely determines the quality of the final product. The common practice runs the process under pre-calculated input profiles while considering average drying dynamics for the batch. This results in conservative and underperforming cycles. In this regard, the paper proposes and experimentally validates an in-line control strategy for primary drying. It explicitly accounts for intra-lot drying heterogeneity using three models, each one calibrated for a reference vial position in the chamber. Two separate experiments evaluate the proposed framework: offline and in-line process control. Validation on a pilot-scale unit shows a cycle time reduction of the proposed approaches compared to standard drying protocol at constant operating conditions. The input profiles obtained by offline control lead to a 16% shorter cycle time; however, the lack of feedback during the cycle impedes addressing potential parametric variations and model mismatches. Conversely, in-line control yields a reduction of 13% while allowing careful monitoring of the product status, ensuring a more dependable cycle. In both cases, the visual quality inspection confirms the acceptability of the final product without visible degradation. [Display omitted] • A control strategy for multi-vial control of primary dying that can accommodate any number of vials. • Three representative vial positions considered: center, edge and corner. • Application of offline control yields a 16% cycle reduction but can be susceptible to disturbances. • Application of in-line control in a pilot unit yields the safest cycle and a time reduction of 13%. [ABSTRACT FROM AUTHOR]

In this paper, a model-based control scheme for a DC-DC power converter as a battery charger is presented. The proposed DC-DC converter for this task is a high-gain step-down converter based on the switched inductor concept. Thus, the considered topology is able to highly reduced the output voltage on a single stage compared to traditional step-down converters. The proposed control strategy is aimed at fulfilling the requirements of the constant current and constant voltage algorithm widely used for battery charging purposes. Given the structure of the system model, the proposed control scheme results in a multi-loop controller, which is structured by a PI current control loop and a PI voltage control loop. During the constant current stage, the system is required to inject constant current into the battery while the voltage freely increases. Thus, the current control loop maintains the input battery current at a constant reference value. On the other hand, in the constant voltage stage, the battery is regulated to a desired constant value. Hence, in the end, both control loops act on the system to fulfill the objective. An external battery management system is aimed at controlling the transition between both modes, considering the physical thresholds of current and voltage required by the battery to guarantee the successful charging process. Finally, the performance of the proposed control strategy for battery charging is assessed on an experimental setup.

Researchers have a special fondness for continuum robots (CRs) due to their various applications. CRs have been modeled in different ways. One of these methods is called lumped model. Although the lumped modeling of CRs needs multiple degrees of freedom, researchers have considered only a few degrees of freedom. But considering such structures led to some issues in the accuracy of the controller. Therefore, in this paper, the dynamic modeling of a CR which is based on the lumped model is developed in a general form. Additionally, a control strategy based on sliding mode back-stepping control is proposed after introducing the first and second Lyapunov functions for stability proof. Moreover, a new function in the control law is used to avoid chattering phenomena. The proposed controller can reduce the settling time, which is one of the most important factors in controlling such robots. To demonstrate the efficiency of the proposed method, three different case studies are conducted for a planar 8-DOF continuum manipulator and the simulations are compared with the feedback linearization method (FL). The simulations show the effectiveness of the proposed method for controlling the continuum robot.

Cognition and brain structure undergo significant maturation from adolescence into adulthood. Model-based (MB) control is known to increase across development, which is mediated by cognitive abilities. Here, we asked two questions unaddressed in previous developmental studies. First, what are the brain structural correlates of age-related increases in MB control? Second, how are age-related increases in MB control from adolescence to adulthood influenced by motivational context? A human developmental sample (n = 103; age, 12-50, male/female, 55:48) completed structural MRI and an established task to capture MB control. The task was modified with respect to outcome valence by including (1) reward and punishment blocks to manipulate the motivational context and (2) an additional choice test to assess learning from positive versus negative feedback. After replicating that an age-dependent increase in MB control is mediated by cognitive abilities, we demonstrate first-time evidence that gray matter density (GMD) in the parietal cortex mediates the increase of MB control with age. Although motivational context did not relate to age-related changes in MB control, learning from positive feedback improved with age. Meanwhile, negative feedback learning showed no age effects. We present a first report that an age-related increase in positive feedback learning was mediated by reduced GMD in the parietal, medial, and dorsolat-eral prefrontal cortex. Our findings indicate that brain maturation, putatively reflected in lower GMD, in distinct and partially overlapping brain regions could lead to a more efficient brain organization and might thus be a key developmental step toward age-related increases in planning and value-based choice. [ABSTRACT FROM AUTHOR]

The delta robot can reach higher speeds than traditional serial-axis machines used in fused filament fabrication 3D printing. However, like serial machines, delta printers suffer from undesirable vibration at high speeds which degrades the quality of fabricated parts. This undesirable vibration has been suppressed in serial printers using linear model-inversion feedforward control methods like the filtered B-splines (FBS) approach. However, techniques like the FBS approach are computationally challenging to implement on delta 3D printers because of their coupled, position-dependent dynamics. In this paper, we propose a methodology to address the computational bottlenecks by (1) parameterizing the position-dependent portions of the dynamics offline to enable efficient computation of the model online, (2) computing models at sampled points (instead of every point) along the given trajectory, and (3) employing QR factorization to reduce the number of floating-point arithmetic operations associated with matrix inversion. In simulations, we report a computation time reduction of up to 23× using the proposed method when compared to using the computationally expensive exact LPV model—all while maintaining high tracking accuracy. Accordingly, we demonstrate significant quality improvements on parts printed at various positions on a commercial delta 3D printer using our proposed controller compared to a baseline alternative, which uses an LTI model from one position. Acceleration measurements during printing show that the improvement in print quality of the proposed controller is due to vibration reductions of up to 39% when compared to the baseline controller. [ABSTRACT FROM AUTHOR]