Model-based Feedback Research Articles

A model-based microfluidic feedback control system for replicating blood pressure and wall shear stress on the inner surface of common carotid artery induced by external counterpulsation

Replicating the hemodynamic signal of endothelial cells (ECs) induced by an external counterpulsation (ECP) using a microfluidic system is crucial for further studying EC mechanobiology associated with ECP theraphy. However, there is currently no suitable method to construct a microfluidic system that can accurately reproduce the hemodynamic signals including blood pressure (BP) and wall shear stress (WSS) on the inner surface of the common carotid artery following ECP intervention. In this study, we propose a model-based approach to build a microfluidic feedback control system (MFCS), which consists of a microfluidic chip and a pump system (PS) to resolve the above issue. An integrated transfer function (TF) model formed by the expression of the input impedance and the proportion integration differentiation (PID) controller is used to design the microfluidic chip and PS. The performance of the proposed MFCS for replicating the hemodynamic signals is evaluated by flow-field analysis, and calcium ion (Ca2+) response experiments are conducted to validate the functionality of the proposed MFCS for investigating mechanobiological mechanisms.

Brain-inspired biomimetic robot control: a review.

Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these Brain-Inspired control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired control methods to tackle the intricacies associated with controlling complex robotic systems.

Redundant Space Manipulator Autonomous Guidance for In-Orbit Servicing via Deep Reinforcement Learning

The application of space robotic manipulators and heightened autonomy for In-Orbit Servicing (IOS) represents a paramount pursuit for leading space agencies, given the substantial threat posed by space debris to operational satellites and forthcoming space endeavors. This work presents a guidance algorithm based on Deep Reinforcement Learning (DRL) to solve for space manipulator path planning during the motion-synchronization phase with the mission target. The goal is the trajectory generation and control of a spacecraft equipped with a 7-Degrees of Freedom (7-DoF) robotic manipulator, such that its end effector remains stationary with respect to the target point of capture. The Proximal Policy Optimization (PPO) DRL algorithm is used to optimize the manipulator’s guidance law, and the autonomous agent generates the desired joint rates of the robotic arm, which are then integrated and passed to a model-based feedback linearization controller. The agent is first trained to optimize its guidance policy and then tested extensively to validate the results against a simulated environment representing the motion synchronization scenario of an IOS mission.

Control-oriented modelling of wind direction variability

Abstract. Wind direction variability significantly affects the performance and lifetime of wind turbines and wind farms. Accurately modelling wind direction variability and understanding the effects of yaw misalignment are critical towards designing better wind turbine yaw and wind farm flow controllers. This review focuses on control-oriented modelling of wind direction variability, which is an approach that aims to capture the dynamics of wind direction variability for improving controller performance over a complete set of farm flow scenarios, performing iterative controller development and/or achieving real-time closed-loop model-based feedback control. The review covers various modelling techniques, including large eddy simulations (LESs), data-driven empirical models, and machine learning models, as well as different approaches to data collection and pre-processing. The review also discusses the different challenges in modelling wind direction variability, such as data quality and availability, model uncertainty, and the trade-off between accuracy and computational cost. The review concludes with a discussion of the critical challenges which need to be overcome in control-oriented modelling of wind direction variability, including the use of both high- and low-fidelity models.

To Use or Not to Use: Exploring Therapists' Experiences with Pre-Treatment EMA-Based Personalized Feedback in the TheraNet Project.

Using idiographic network models in psychotherapy has been a growing area of interest. However, little is known about the perceived clinical utility of network models. The present study aims to explore therapists' experiences with network model-based feedback within the context of the TheraNet Project. In total, 18 therapists who had received network-based feedback for at least 1 patient at least 2 months prior were invited to retrospective focus groups. The focus group questions related to how participation in the study influenced the therapeutic relationship, how the networks were used, and what might improve their clinical utility. The transcribed focus groups were analyzed descriptively using qualitative content analysis. Most therapists mentioned using the feedback to support their existingtheir case concept, while fewer therapists discussed the feedback directly with the patients. Several barriers to using the feedback were discussed, as well as various suggestions for how to make it more clinically useful. Many therapists reported skepticism with regards to research in the outpatient training center in general, though they were also all pleasantly surprised by being involved, having their opinions heard, and showing a readiness to adapt research to their needs/abilities. This study highlights the gap between researchers' and therapists' perceptions about what useful feedback should look like. The TheraNet therapists' interest in adapting the feedback and building more informative feedback systems signals a general openness to the implementation of clinically relevant research. We provide suggestions for future implementations of network-based feedback systems in the outpatient clinical training center setting.

Data-driven model based adaptive feedback-feed forward control schemes for open cycle - OTEC process

Open Cycle - Ocean Thermal Energy Conversion (OC-OTEC) is one of the most important renewable energy sources that generate electricity and fresh water from seawater utilizing the temperature gradient between the warm surface seawater and the cold deep seawater. This paper aims to develop non-linear data-driven model-based adaptive Feedback Control (FBC) schemes for the OC-OTEC process to track the output power and a dynamic Feed-Forward Control (FFC) scheme to reject the effects of temperature disturbance on power caused by climate variations in OC-OTEC. The experiments are conducted on a laboratory-scale OC-OTEC experimental setup at the National Institute of Ocean Technology, Chennai. Firstly, linear data-driven models are developed using system identification techniques. Based on the developed models, gain scheduling-based adaptive control schemes are developed: Proportional Integral (PI) control and Model Predictive Control (MPC). Secondly, the closed-loop performances of the developed FBC schemes are analysed under servo and regulatory operations. Furthermore, it is observed from the sensitivity analysis results that the temperature disturbance highly influences output over the manipulated variable. Hence, a dynamic FFC scheme is implemented to reject known disturbance of 1 °C variation in temperature gradient from 18 °C to 19 °C due to Sea Surface Temperature (SST) changes in temperature gradient. The results show that the proposed MPC-based FBC-FF scheme effectively enhances the tracking and disturbance rejection performance compared to the PI-based FBC-FF control scheme with a minimum Integral Square Error (ISE) of 3.055 and Control Effort (CE) of 7.505. This experimental data-based, data-driven modelling and adaptive control research provides a new direction for automating OC-OTEC. Further, these studies will help to develop a rugged and automated upscaling OC-OTEC plant control system with proposed feasible control schemes.

Model-Based Feedback Control for an Automated Micro Liquid Dispensing System Based on Contacting Droplet Generation through Image Sensing.

Over the past few decades, micro liquid dispensing technology has been widely used in biology, chemistry, material and environmental sciences due to its efficacy in processing multiple samples. For practical applications, precise and effective droplet generation is very important. Despite numerous droplet generation methods, the implementation of droplet-on-demand still faces challenges concerning system complexity, precision, cost, and robustness. In this work, a novel on-demand contacting droplet generation method incorporated with model-based feedback control with an image processing unit as a sensor was proposed. By studying droplet identification using image processing techniques, the model of droplet formation was simplified. Then model-based feedback control was implemented using volumes of dispensed samples as sensing signals by tuning related parameters adaptively to resist disturbances. The proposed method was integrated and applied to a homebuilt automated micro liquid dispensing system with droplets ranging from 20 nanoliter to 200 nanoliter. The experimental results demonstrated a high degree of accuracy and precision. Additionally, the proposed system's practical utility was evaluated by analyzing mutations in genes associated with sensorineural hearing loss, verifying its effectiveness.

Real-time, model-based magnetic field correction for moving, wearable MEG

Most neuroimaging techniques require the participant to remain still for reliable recordings to be made. Optically pumped magnetometer (OPM) based magnetoencephalography (OP-MEG) however, is a neuroimaging technique which can be used to measure neural signals during large participant movement (approximately 1 m) within a magnetically shielded room (MSR) (Boto et al., 2018; Seymour et al., 2021). Nevertheless, environmental magnetic fields vary both spatially and temporally and OPMs can only operate within a limited magnetic field range, which constrains participant movement. Here we implement real-time updates to electromagnetic coils mounted on-board of the OPMs, to cancel out the changing background magnetic fields. The coil currents were chosen based on a continually updating harmonic model of the background magnetic field, effectively implementing homogeneous field correction (HFC) in real-time (Tierney et al., 2021). During a stationary, empty room recording, we show an improvement in very low frequency noise of 24 dB. In an auditory paradigm, during participant movement of up to 2 m within a magnetically shielded room, introduction of the real-time correction more than doubled the proportion of trials in which no sensor saturated recorded outside of a 50 cm radius from the optimally-shielded centre of the room. The main advantage of such model-based (rather than direct) feedback is that it could allow one to correct field components along unmeasured OPM axes, potentially mitigating sensor gain and calibration issues (Borna et al., 2022).

EFL learners’ perceptions and their association with the effectiveness of model texts as a feedback tool

ABSTRACT This quasi-experimental study investigated the effect of using model texts as a written corrective feedback (WCF) strategy on second language learners’ writing, in both rewritten drafts and new drafts. It further examined learners’ perceptions of model-based feedback and how those perceptions affected model effectiveness. Sixty-six English learners were equally divided into either a model group (i.e. an experiment group) or a control group. They wrote an argumentative essay, rewrote the same text, and then wrote a new text using a different prompt. Before rewriting their first draft, the model group received model texts with which they compared their initial writing, whereas the control group self-corrected their errors instead. The model group also completed a perception questionnaire on the usefulness of model-based feedback. The model group outperformed the control group, but only in the rewriting task. Furthermore, in the rewriting task there was a significant correlation between learners’ perceptions of models and the effectiveness of model-based feedback, but not in the new writing task.

Simultaneous stabilization and control of the n = 1 and n = 2 resistive wall mode

DIII-D experiments demonstrate simultaneous stability measurements and control of resistive wall modes (RWMs) with toroidal mode numbers n = 1 and n = 2. RWMs with n > 1 are sometimes observed on DIII-D following the successful feedback stabilization of the n = 1 mode, motivating the development of multi-n control. A new model-based multi-mode feedback algorithm based on the VALEN physics code has been implemented on the DIII-D tokamak using a real-time GPU installed directly into the DIII-D plasma control system. In addition to stabilizing RWMs, the feedback seeks to control the stable plasma error field response, enabling compensation of the typically unaddressed DIII-D n = 2 error field component. Experiments recently demonstrated this algorithm’s ability to simultaneously control n = 1 and n = 2 perturbed fields for the first time in a tokamak, using reactor relevant external coils. Control was maintained for hundreds of wall-times above the n = 1 no-wall pressure limit and approaching the n = 1 and n = 2 ideal-wall limits. Furthermore, a rotating non-zero target was set for the feedback, allowing stability to be assessed by monitoring the rotating plasma response (PR) while maintaining control. This novel technique can be viewed as a closed-loop extension of active MHD spectroscopy, which has been used to validate stability models through comparisons of the PR to applied, open-loop perturbations. The closed-loop response measurements are consistent with open-loop MHD spectroscopy data over a wide range of β N approaching the n = 1 ideal-wall limit. These PR measurements were then fit to produce both VALEN and single-mode stability models. These models allowed for important plasma stability information to be determined and have been shown to agree with experimentally observed RWM growth rates.

Effect of a Cognitive Diagnosis Model-Based Feedback in an English Writing Class: Focusing on Korean English Learners*

Effect of a Cognitive Diagnosis Model-Based Feedback in an English Writing Class: Focusing on Korean English Learners*

Hidden Markov model-based finite-time H∞ control for singular Markovian jump delay systems with input saturation

This article considers the finite-time [Formula: see text] control problem for singular Markovian jump systems with time-varying delay subject to input saturation. Considering the discontinuities caused by the mode-dependent singular matrix and Markovian jump switching behavior, the result of this article is more practical. First, a hidden model-based state feedback controller is designed to guarantee that the considered system is mean-square locally finite-time [Formula: see text] stabilizable by employing an appropriate Lyapunov function. Second, some sufficient conditions for the solution to this problem are derived in terms of linear matrix inequalities which considers the time-varying delay and input saturation. Finally, the correctness of the proposed method is verified by three numerical examples.

Hybrid Dynamic Event-Triggered Load Frequency Control for Power Systems With Unreliable Transmission Networks.

In this article, we consider the load frequency control problem for a class of power systems based on the dynamic event-triggered control (ETC) approach. The transmission networks are unreliable in the sense that malicious denial-of-service (DoS) attacks may arise in the power system. First, a model-based feedback controller is designed, which utilizes estimated states, and thus can compensate the error between plant states and the feedback data. Then, a dynamic event-triggered mechanism (DETM) is proposed by introducing an internal dynamic variable and a timer variable with jump dynamics. The proposed (DETM) can exclude Zeno behavior by regularizing a prescribed strictly positive triggering interval. Incorporated in the ETC scheme, a novel hybrid model is established to describe the flow and jump dynamics of the power system in the presence of DoS attacks. Based on the hybrid dynamic ETC scheme, the power system stability can be preserved if the attacks frequency and duration sustain within an explicit range. In addition, the explicit range is further maximized based on the measurement trigger-resetting property. Finally, a numerical example is presented to show the effectiveness of our results.

Energy-Optimal Unmanned Aerial Vehicles Motion Planning and Control Based on Integrated System Physical Dynamics

Abstract Electric vertical-take-off-and-landing multirotor aircraft has been emerging as a revolutionary transportation mode for both manned and unmanned applications, but this technology is limited by flight time and range restrictions. In this work, an energy-efficient model-based trajectory planning and feedback control framework is developed to improve the energy performance of a multirotor unmanned aerial vehicle. Target vehicle trajectories are planned by solving a formulated energy consumption optimization problem based on a system-level model, which accommodates the integrated dynamics of key vehicle subsystems. In order to implement the generated target trajectories, the framework also includes a PID feedback control architecture for real-time trajectory following. The framework is first verified under simulation, and shows an average reduction of 10.7% in energy consumption over a range of typical hover-to-hover operations, compared to the commonly used baseline flight control architecture. Through model-based analysis, key relationships that contribute to the improvements are identified and analyzed. These results demonstrate the importance of considering and coordinating all relevant system dynamics for efficient and holistic trajectory planning and control, which is absent in existing literature. The framework also demonstrates similar performance improvement under experimental validation, with an average energy reduction of 10.2% over the baseline controller despite the presence of significant real-world disturbances including wind effect.

Nonlinear Model-Based Control of a Pneumatically Driven Robot

In robotics, pneumatic drives are an interesting alternative to classic electric drives, as their physical properties enable the design of safe, lightweight and intuitively operable robots. In this work, a nonlinear model-based feedback control concept for a robot with 4 compressed-air-driven rotary joints is presented. The robot model comprising kinetics and pressure dynamics is formulated. For this drive type, significant nonlinear friction effects are present, which is known to be challenging for controller design, especially since there is no output-side measurement of the effective drive torque after subtracting friction. A control concept based on differential flatness, here with the particular case of feedback linearization, is derived, which is particularly well suited for a system with such effects. The algorithm contains both kinetics and pressure dynamics in one central controller, and its working principle and practical implications are discussed. With experimental results, the effectiveness of the trajectory tracking controller is demonstrated and insights into the components of this controller are provided.

Dealing with a multiple access congested suburban freeway using a dynamic toll pricing approach

In this paper we resume a novel approach for alleviating traffic congestion in suburban freeways with multiple access locations through the use of dynamic toll pricing. The pricing strategy is determined using model-based feedback control, in combination with a traffic flow model and a driver behavior model. The case study is a suburban freeway segment with multiple access locations. A model derived from the cell transmission one was utilized to develop the traffic flow model, with past traffic information from on-road sensors utilized to derive the model parameters. The drivers’ decisions concerning the choice of whether or not to enter the freeway segment is modeled using utility theory and the Value of Time (VOT) relative to the toll value. The proposed toll-pricing scheme is tested with traffic data from Portuguese freeway A5 and with different assumptions on the drivers’ VOT distribution, showing a significant improvement of the overall traffic conditions. The algorithm illustrated here provides an opportunity to improve on existing toll policies, guaranteeing more stable traffic conditions for the freeway users and optimizing the overall traffic throughput.

New Nonlinear Control Based on Polynomial Approach for Islanded DC Microgrid Robustness and Voltage Stability

This paper aims at developing a novel robust polynomial decentralized control approach for an islanded hybrid DC MicroGrid (MG) with saturation constraint. The investigated MG system consists of a PV unit, a battery and a SuperCapacitor (SC). Each source is controlled by a DC-DC power converter. The challenge is to design a new control approach to enhance the robustness and the stability of the DC MG subject to high nonlinear variations. The main nonlinear control objectives are to: 1) maintain the DC bus voltage to a reference value, 2) ensure the power balance in the system, and 3) warranty the system robustness against perturbations and parametric uncertainties. The Polynomial Control (PC) theory offers an adequate framework to meet the required performances. The resulting control algorithms are simple, and do not need important calculating resources. An extended version of the PC controller is also proposed to make the system Fault Tolerant (FT) w.r.t severe eventual faults, such as a short circuit case at the DC bus level. The design conditions of the developed polynomial controllers are solved using the Sum Of Squares (SOS) approach while verifying the saturation constraint. Stability conditions are ensured based on polynomial Lyapunov functions. Finally, simulations are carried out on MATLAB/Simulink to prove the pertinence and the robustness of the developed control architecture while considering variable loads and solar irradiance. Results are compared to model-based feedback linearization control and sliding mode control techniques.

Digitalization of an experimental electrochemical reactor via the smart manufacturing innovation platform

The exponential increase in data produced over the last two decades has revolutionized the way we collect, store, process, analyze, model, and interpret information to improve profitability. Manufacturing is no exception. However, Smart Manufacturing, the digital practice, organization, workforce, and infrastructure transformation for collection and deployment of data and models at scale and at all levels of manufacturing, is a complex, costly, and labor-intensive journey that is still seeing slow adoption. The Clean Energy Smart Manufacturing Innovation Institute (CESMII), a national Manufacturing USA public-private partnership sponsored by the Department of Energy, is addressing this scaled use of data and modeling in manufacturing. CESMII has focused on how to collect and use operating data for numerous applications that improve productivity, precision, and performance of manufacturing operations from factory floor to supply chain using process simulation, predictive analytics, monitoring and control, and real-time optimization. Because contextualized data are key, CESMII has developed the Smart Manufacturing Innovation Platform (SMIP) to lower the barriers to the data that are needed to accelerate data-based model building, improve data visualization, and more quickly gain insights. Reusable, standards-based ways of doing data collection, ingestion, and contextualization are particularly important for scaling access and use of data. The SMIP uses a standards-based definition and construct for reusable information models called an SM Profile. When an SM Profile is used in conjunction with the SMIP, the SMIP ensures the availability of contextualized, operational data for model building. The present work demonstrates Smart Manufacturing and the application of the SMIP for building several data-centered models for the operation and control of an experimental electrochemical reactor that reduces carbon dioxide (CO2) gas to valuable liquid and gas chemicals, such as alcohols, olefins, and syngas. We describe how the SMIP plays a central role in more effective model building and we demonstrate how the electochemical reactor can be controlled and optimized for the desired products. Use of the SMIP involves the transmission of real-time sensor measurements to a cloud resource so that the operating data are available to all model building experts. The data collection and transmission process is fully automated to greatly reduce the need for manual manipulation of the data. Data-driven machine learning models are used for advanced real-time state estimation, real-time optimization, and model-based feedback control for the reactor. The application models are implemented as a system to monitor the data flow and control the electrochemical reactor with a single visualization interface. SM Profiles are used to demonstrate reusability of the information models for the reactor and the instrumentation. The application packages, algorithms, and user interfaces developed are cast as Docker images in a library to facilitate reusability of the application models.

Nonlinear feedback control of bimodality in the wake of a three-dimensional bluff body

The turbulent wake behind a square-back Ahmed body in close proximity to the ground exhibits bimodal switching. This manifests as the center of the wake randomly switching side-to-side between one of two asymmetric positions. This work is the first attempt to employ nonlinear model-based feedback control to suppress this wake bimodality. High fidelity simulations are used to develop and implement the control strategy and investigate the resulting effect on the wake and aerodynamic drag.

Spectral Richness PSO algorithm for parameter identification of dynamical systems under non-ideal excitation conditions

This work proposes a new Particle Swarm Optimization (PSO) algorithm specifically designed for parameter identification of physical systems. The key feature of the proposed algorithm is that it takes into consideration the Spectral Richness of the signal used for exciting the system during the identification procedure. The Spectral Richness is essentially the number of spectral lines in the Fast Fourier Transform of a signal, and if this number is large, the variability of the parameter estimates produced by well-known PSO algorithms is small. However, low values of Spectral Richness may cause large variability of the parameter estimates. The proposed approach, which is termed as the Spectral Richness Particle Swarm Optimization (SR-PSO) algorithm, reduces the variability of the parameter estimates when using an excitation signal with a low value of Spectral Richness compared with state-of-the-art PSO algorithms, and maintains the same performance if the Spectral Richness is high. In a first case study, a set of real-time experiments applying excitation signals with different levels of Spectral Richness have been performed on a servomechanism in order to obtain several data sets. The acquired data from each excitation signal has been used to perform a parameter identification process using various algorithms including the classic PSO, the Linear Variable Weight Particle Swarm Optimization (LVW-PSO), the Linear Variable Constriction Factor Particle Swarm Optimization (LVCF-PSO), the state-of-the-art Fractional Order Regulated Particle Swarm Optimization (FOR-PSO) and the proposed Spectral Richness Particle Swarm Optimization (SR-PSO). The pertinence of the parameter estimates is also evaluated by designing and experimentally applying a model-based feedback control to the servomechanism.The proposed SR-PSO algorithm is also tested in a thermoelectric cooler model using numerical simulations. It is shown that the values of the identified parameters heavily depends on the kind of excitation signal applied to this system.