Closed-loop Methods Research Articles

Neuromodulation techniques for modulating cognitive function: enhancing stimulation precision and intervention effects.

Neuromodulation techniques effectively intervene in cognitive function, holding considerable scientific and practical value in fields such as aerospace, medicine, life sciences, and brain research. These techniques utilize electrical stimulation to directly or indirectly target specific brain regions, modulating neural activity and influencing broader brain networks, thereby regulating cognitive function. Regulating cognitive function involves an understanding of aspects such as perception, learning and memory, attention, spatial cognition, and physical function. To enhance the application of cognitive regulation in the general population, this paper reviews recent publications from the Web of Science to assess the advancements and challenges of invasive and non-invasive stimulation methods in modulating cognitive functions. This review covers various neuromodulation techniques for cognitive intervention, including deep brain stimulation, vagus nerve stimulation, and invasive methods using microelectrode arrays. The non-invasive techniques discussed include transcranial magnetic stimulation, transcranial direct current stimulation, transcranial alternating current stimulation, transcutaneous electrical acupoint stimulation, and time interference stimulation for activating deep targets. Invasive stimulation methods, which are ideal for studying the pathogenesis of neurological diseases, tend to cause greater trauma and have been less researched in the context of cognitive function regulation. Non-invasive methods, particularly newer transcranial stimulation techniques, are gentler and more appropriate for regulating cognitive functions in the general population. These include transcutaneous acupoint electrical stimulation using acupoints and time interference methods for activating deep targets. This paper also discusses current technical challenges and potential future breakthroughs in neuromodulation technology. It is recommended that neuromodulation techniques be combined with neural detection methods to better assess their effects and improve the accuracy of non-invasive neuromodulation. Additionally, researching closed-loop feedback neuromodulation methods is identified as a promising direction for future development.

Kinematic calibration and feedforward control of a heavy-load manipulator using parameters optimization by an ant colony algorithm

AbstractMost of the currently available three-degree-of-freedom manipulators are light load and cannot achieve full continuous rotation; given this, we designed a heavy-load manipulator that achieves unrestricted and continuous rotation. Due to manufacturing and assembly errors, parameter deviations between the real manipulator and its underlying theoretical model were unavoidable. Because of the lack of high-precision, high-frequency, and real-time closed-loop detection methods, we proposed a type of kinematics calibration of parameterized ant colony optimization and feedforward control methods. This was done to achieve high-precision motion control. First, an error model combining structural parameters and joint output angles was established, and the global sensitivity of each error source was analyzed to distinguish both primary and secondary sources. Based on the measured data of a laser tracker, the ant colony optimization was then used to identify six error sources. This resulted in both link length and joint driving errors of the designed manipulator. As it is a type of systematic error, the rounding error of the theoretical trajectory was carefully analyzed, and feedforward control methods with different coefficients were designed to further improve positioning accuracy based on the kinematic calibration. Experimental results showed that the proposed kinematic calibration and feedforward control methods achieved relatively precise motion control for the designed manipulator.

Kinematic Calibration of an Industrial Manipulator without External Measurement Devices

This paper presents a practical approach to fully automated kinematic calibration of an industrial manipulator. The approach is based on the principle of plane constraint. The electrical signal is used to fix the moment of contact between the conductive tool and the flat surface. The measurement data are manipulator configurations (joint angles) at the moment of contact. A modification of the algorithm to deal with the scaling problem is also proposed. This approach provides both high calibration accuracy and lower cost of the experimental setup compared to coordinate measuring machines (CMMs), laser trackers, and vision systems. The article examines the impact of various methods of kinematic parameterization of manipulators: the Denavit – Hartenberg agreement (DH), product of exponentials (POE), as well as the complete and parametrically continuous model (CPC) on the calibration accuracy. A comparison is made of the open-loop and the proposed closed-loop calibration methods on the Puma 560 model known in the literature. POE parameters were converted to DH and CPC to compare accuracy after calibration based on these parameterizations. The method of computing POE-CPC transformation as a solution to a certain optimization problem is proposed. The problem of identifying geometric parameters in the presence of restrictions is solved by gradient optimization methods. Experiments have been carried out on an ABB IRB 1600 industrial manipulator with an installed conductive probe and an ABB IRBP A-500 robotic positioner with a conductive metal flat surface. A technique for indirectly checking the accuracy of calibration of kinematic parameters is proposed based on a study of the accuracy of manipulation when using these parameters.

Efficiency of EEG-Guided Adaptive Neurostimulation Increases with the Optimization of the Parameters of Preliminary Resonant Scanning

The development and improvement of closed-loop methods for non-invasive brain stimulation is an actual and rapidly developing area of neuroscience. An innovative version of this approach, in which a person is presented with audiovisual therapeutic stimulation, automatically modulated by the rhythmic components of his electroencephalogram (EEG), is EEG-guided adaptive neurostimulation. The present study aims to experimentally test the assumption that the effectiveness of EEG-guided adaptive neurostimulation can be increased by optimizing the parameters of preliminary resonance scanning, which consists of LED photostimulation with stepwise increasing frequency in the range of θ-, α-, and β EEG-rhythms. In order to test this assumption, we compared the effects of two types of resonance scanning, which differ in the step length of the gradually increasing frequency of LED photostimulation. The experiments involved two equal groups of university students in a state of exam stress. Before EEG-guided adaptive stimulation, one of the groups underwent resonance scanning with a short (3 s), and the other with a long (6 s) step of a gradual increase in the frequency of photostimulation. Changes in the EEG and psychophysiological parameters were analyzed under the influence of combined (resonance scanning plus EEG-guided adaptive neurostimulation) interventions relative to the initial level. It was found that only with a short (3 s) step of increasing the frequency of photostimulation, significant increases in the power of EEG-rhythms are observed, accompanied by significant changes in subjective indicators – a decrease in the number of errors in the word recognition test, a decrease in the level of emotional maladaptation, and an increase in well-being scores. The revealed positive effects are already observed after single therapeutic procedures due to the optimal conditions for the involvement of the resonant and integration mechanisms of the brain and the mechanisms of neuroplasticity in the processes of normalization of body functions. The developed combined approach to neurostimulation after additional experimental studies can be used in a wide range of rehabilitation procedures.

A robust model predictive control with constraint modification for gas lift allocation optimization

This paper presents a novel approach for implementing a robust real-time optimization framework under the presence of parametric uncertainty. Conservativeness is an inevitable drawback of a robust control approach. Therefore we aimed to provide a simple and efficient method to mitigate the conservativeness while the robust fulfillment of the constraints is still preserved. The proposed method in this paper is based on the worst-case realization of the uncertainties, however, with constraint modification. The mismatch between measured and predicted output is used directly to modify the active constraint in the optimization problem. The superiority of the method in terms of conservativeness and computational time has been demonstrated in comparison with the other robust optimization counterparts, such as traditional min–max and multi-stage MPC. The promising advantage of the proposed method is that not only it reduces the conservativeness significantly, but also the computational price for this achievement is considerably cheaper than closed-loop optimization methods such as multi-stage MPC.

The Design of Brainstem Interfaces: Characterisation of Physiological Artefacts and Implications for Closed-loop Algorithms.

Surgical neuromodulation through implantable devices allows for stimulation delivery to subcortical regions, crucial for symptom control in many debilitating neurological conditions. Novel closed-loop algorithms deliver therapy tailor-made to endogenous physiological activity, however rely on precise sensing of signals such as subcortical oscillations. The frequency of such intrinsic activity can vary depending on subcortical target nucleus, while factors such as regional anatomy may also contribute to variability in sensing signals. While artefact parameters have been explored in more 'standard' and commonly used targets (such as the basal ganglia, which are implanted in movement disorders), characterisation in novel candidate nuclei is still under investigation. One such important area is the brainstem, which contains nuclei crucial for arousal and autonomic regulation. The brainstem provides additional implantation targets for treatment indications in disorders of consciousness and sleep, yet poses distinct anatomical challenges compared to central subcortical targets. Here we investigate the region-specific artefacts encountered during activity and rest while streaming data from brainstem implants with a cranially-mounted device in two patients. Such artefacts result from this complex anatomical environment and its interactions with physiological parameters such as head movement and cardiac functions. The implications of the micromotion-induced artefacts, and potential mitigation, are then considered for future closed-loop stimulation methods.

Model Predictive Control—A Stand Out among Competitors for Fed-Batch Fermentation Improvement

The fed-batch cultivation is in many ways a benchmark for fermentation processes, and it has been an attractive choice for the biotechnological production of various products in the past decades. The majority of biopharmaceuticals that are presently undergoing clinical trials or are available on the market are manufactured through fed-batch fermentations. A crucial process parameter in fed-batch cultivations is the substrate feed rate, which directly influences the overall process productivity, product quality and process repeatability; henceforth, effective control of this parameter is imperative for a successful fed-batch fermentation process. Two distinct control strategies can be distinguished—open-loop and closed-loop (feedback) control. Each of these methods has its own set of benefits, limitations and suitability for specific bioprocesses. This article surveys and compares the most popular open- and closed-loop methods for substrate feed rate control in fed-batch fermentations. Emphasis is placed on model-predictive feed rate control (MPC)—a stand out among other methods that offers a promising application perspective. The authors also demonstrate a practical example of the implementation of a robust, flexible MPC solution that is suitable for various cultures and runs on standard computer hardware, thus overcoming one of the main reported MPC drawbacks—high computational requirements.

Research on the Single-Double Interval Closed-Loop Method of Resonant Fiber Optic Gyro

Resonant fiber optic gyro (RFOG) is a type of inertial sensor with great application potential. Its closed-loop signal detection methods include single closed-loop, double closed-loop and other methods to ensure high system stability and high-precision signal output. In view of the respective advantages of single and double closed-loop systems, a single-double interval closed-loop scheme for RFOG is proposed in this letter. By introducing the threshold trigger mechanism, the advantages of low control signal noise of the single closed-loop system and large dynamic range of the double closed-loop system are fully combined. In addition, in order to improve the linearity of the demodulation curve, a linear fitting method is applied to the system. The experiment shows that the short-term and long-term stability of the system have been improved effectively.

Real-Time Adjustment of Impulse Stimulation Parameters Using a Closed-Loop Deep Brain Stimulation System

The closed-loop deep brain stimulation system is a new research that provides real-time adjustment of impulse stimulation parameters. This system belongs to the implantation medical equipment technical field, which is designed for gathering physiological signals of patients. It includes a host computer that displays data in real-time, data storage, design optimization algorithms, and renewal embedded programs. The system has two modes of operation: one is where the embedded nerve stimulator carries out the formation closed-loop control of Embedded algorithm processing data, and the other is where the data collection reaches the host computer by wireless communication module. The stimulation parameter is controlled by the wireless communication module after host computer algorithm process, forming a closed loop. This research aimed to investigate the feasibility of using the closed-loop deep brain stimulation system for electronic stimulation of the nervous system disease and its potential for clinical research or zooscopy for closed-loop stimulation methods. The study provides a good platform for the research of closed-loop stimulation methods.

CONFIG: Constrained Efficient Global Optimization for Closed-Loop Control System Optimization with Unmodeled Constraints

In this paper, the CONFIG algorithm, a simple and provably efficient constrained global optimization algorithm, is applied to optimize the closed-loop control performance of an unknown system with unmodeled constraints. Existing Gaussian process based closed-loop optimization methods, either can only guarantee local convergence (e.g., SafeOPT), or have no known optimality guarantee (e.g., constrained expected improvement) at all, whereas the recently introduced CONFIG algorithm has been proven to enjoy a theoretical global optimality guarantee. In this study, we demonstrate the effectiveness of CONFIG algorithm in the applications. The algorithm is first applied to an artificial numerical benchmark problem to corroborate its effectiveness. It is then applied to a classical constrained steady-state optimization problem of a continuous stirred-tank reactor. Simulation results show that our CONFIG algorithm can achieve performance competitive with the popular CEI (Constrained Expected Improvement) algorithm, which has no known optimality guarantee. As such, the CONFIG algorithm offers a new tool, with both a provable global optimality guarantee and competitive empirical performance, to optimize the closed-loop control performance for a system with soft unmodeled constraints. Last, but not least, the open-source code is available as a python package to facilitate future applications.

Research Hotspots and Frontiers of Transcranial Direct Current Stimulation in Stroke: A Bibliometric Analysis.

Background: Over the past decade, many studies in the field of transcranial direct current stimulation (tDCS) in stroke have been published in scholarly journals. However, a scientometric analysis focusing on tDCS after stroke is still missing. The purpose of this study is to deliver a bibliometric analysis to investigate the global hotspots and frontiers in the domain of tDCS in stroke from 2012 to 2021. Methods: Articles and reviews related to tDCS in stroke were retrieved and obtained from the Web of Science core collection database from 2012 to 2021. Data visualization and analysis were conducted by using CiteSpace, VOSviewer, and Microsoft Excel 2019. Results: Finally, 371 publications were included in the scientometric analysis, including 288 articles and 83 reviews. The results showed that the number of publications per year increased from 15 to 68 in the last 10 years. Neurosciences was the main research hotspot category (n = 201). Frontiers in Human Neuroscience was the most published journal with 14 papers. The most productive author, institution, and country were Fregni F (n = 13), the League of European Research Universities (n = 37), and the United States of America (n = 98), respectively. A burstness analysis of keywords and the literature indicated that current studies in the field of tDCS in stroke focused on poststroke aphasia, tDCS combined with robotic therapy, and anatomical parameters. Conclusion: The research of tDCS in stroke is predicted to remain a research hotspot in the future. We recommend investigating the curative effect of other different tDCS closed-loop rehabilitation methods for different stroke dysfunctions. In conclusion, this bibliometric study presented the hotspots and trends of tDCS in stroke over the last decade, which may help researchers manage their further studies.

A simplified electro-chemical lithium-ion battery model applicable for in situ monitoring and online control

The penetration of lithium-ion batteries (LIBs) in transport, energy, and communication systems is increasing rapidly. A meticulous but simplified LIB model for non-uniform internal state monitoring and online control is sought in practice. Based on the pseudo-two-dimensional (P2D) model, a simplified electro-chemical model for LIBs is proposed. Specifically, a rigorous model of the non-uniform reaction rates inside the battery is derived. Sub-models that capture the non-uniformity of current densities, potentials and concentrations are developed synchronously. Time-variant parameters and a lumped thermal model are incorporated as well. A full-cycle simulation framework, including the discretization, initialization, stabilization and closed-loop correction methods, is designed for ease of online control. Numerical experiments on the widely used NCM and LFP 18650 batteries under standard charge and discharge protocols and dynamic protocols during the peak-shaving or regulation service are conducted for validation. Generally, the speed of the proposed model increases hundreds of times compared to the P2D model. The estimation accuracy of internal and external states increases around 10% to 100% compared to state-of-art electro-chemical models. In addition, the correction speed and accuracy of the closed-loop framework increase around ten times and around 100% respectively compared to the widely used ensemble Kalman filter.

Experimental Study on the Closed-Loop Control of a Soft Ring-Shaped Actuator for Gastric Simulator

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">In-vitro</i> gastric simulators are soft robotic systems that reproduce the change of the food or drug by emulating the stomach's motility functions. The control of gastric simulators traditionally lies in tuning the actuators via open-loop approaches. However, the lack of precise position control, external disturbances, sensitivity to model uncertainties, and continues retuning are solid motivations for employing closed-loop methods to control gastric simulators. A bellows-driven viscoelastic soft ring-shaped pneumatic actuator (RiSPA), including five spring-like bellows, was developed for stomach simulators’ primary actuation system. This article studies the implementation of closed-loop methods equipped with the online feedback system on the RiSPA for the first time. All technical aspects for implementing the closed-loop control and the sensor-actuator integration are discussed in detail. A model-based closed-loop method has been implemented and compared with a classic model-free method to show the closed-loop system's ability to reject model uncertainties. The experimental results in both the tracking trajectory and the regulation performances are thoroughly compared to the open-loop approach, where the superiority of closed-loop techniques is demonstrated. A mechanical setup is designed to show the ability of the closed-loop approach in dealing with the disturbances and external loads as well.

Flight Control Technology Advancements and Challenges for Future Rotorcraft 40th Alexander A. Nikolsky Honorary Lecture

The goal of my 40th Alexander A. Nikolsky Honorary Lecture and journal paper is to highlight the key flight control technology advances of the past 50 years and demonstrate how these advances are being applied and extended to the new family of rotorcraft: modern high-speed military rotorcraft, eVTOL urban air mobility, and advanced air mobility aircraft. The first part of this journal paper reviews flight control technologies drivers that are unique to rotorcraft and highlights key advances of the past 50 years in the areas of handling-qualities requirements (ADS-33), physics-based models, system identification, and flight control. A central theme is the shift from time-domain to frequency-domain based characterization of the closed-loop response and design methods for rotorcraft that have become increasingly dependent on sophisticated feedback control systems to achieve closed-loop stability, disturbance rejection, and most importantly closed-loop handlingqualities response for all-weather operations. Frequency-domain analysis, design, and test methods of the past 50 years are highlighted relating key advances in each discipline and two integrated example success stories. In the second part of this paper, we consider the key challenges, advancements, and needed future research for four new classes of rotorcraft: the military future vertical lift family of high-speed rotorcraft, unmanned autonomous systems/urban air mobility based on fielded conventional helicopters, small electric VTOL unmanned aerial vehicle rotorcraft, and larger eVTOL urban air mobility rotorcraft. The next sections look across the challenges and solution spaces that are common to these four new classes of rotorcraft as a blueprint for needed research advances. Finally, this paper takes a step back and considers the lessons-learned and key takeaways from the author's perspective as a career-long flight control engineer/researcher, Flight Control Technology Group leader, and senior technologist.

Sim-to-real transfer and reality gap modeling in model predictive control for autonomous driving

The main challenge for the adoption of autonomous driving is to ensure an adequate level of safety. Considering the almost infinite variability of possible scenarios that autonomous vehicles would have to face, the use of autonomous driving simulators is becoming of utmost importance. Simulation suites allow the used of automated validation techniques in a wide variety of scenarios, and enable the development of closed-loop validation methods, such as machine learning and reinforcement learning approaches. However, simulation tools suffer from a standing flaw in that there is a noticeable gap between the simulation conditions and real-world scenarios. Although the use of simulators powers most of the research around autonomous driving, and is generally used within all domains it is divided into, there is an inherent source of error given the stochastic nature of activities performed in real world, which are unreplicable in computer environments. This paper proposes a new approach to assess the real-to-sim gap for path tracking systems. The aim is to narrow down the sources of error between simulation results and real-world conditions, and to evaluate the performance of the simulation suite in the design process by employing the information extracted from gap analysis, which adds a new dimension of development against other approaches for autonomous driving. A real-time model predictive controller (MPC) based on adaptive potential fields was developed and validated using the CARLA simulator. Both the path planning and vehicle control systems where tested in real traffic conditions. The error between the simulator and the real data acquisition was evaluated using the Pearson correlation coefficient (PCC) and the max normalized cross-correlation (MNCC). The controller was further evaluated on a process of sim-to-real transfer, and was finally tested both in simulation and real traffic conditions. A comparison was performed against an optimal-control ILQR-based model predictive controller was carried out to further showcase the validity of this approach.

A New Strategy for PI Tuning in Photovoltaic Irrigation Systems Based on Simulation of System Voltage Fluctuations Due to Passing Clouds

One of the greatest challenges in stand-alone photovoltaic irrigation systems (PVIS) without batteries is the tuning of PID controllers and the evaluation of their performance once the system is tuned. Tuning method must be applied in clear days (constant irradiance) while performance must be evaluated in the most unfavourable circumstances, which occur when the passage of a cloud causes a sudden drop in available power. In short, tuning and testing must be done under different weather conditions. To solve this problem, a tuning method that is complemented by a method to simulate voltage fluctuations due to cloud passage has been developed. This allows tuning and evaluation of the system’s performance in the same session. Furthermore, the new PI tuning method achieves a better adjustment of the parameters and solves the instability problems that arise when applying traditional closed-loop tuning methods. Both methods use the feedforward input that most variable frequency drivers have. A signal generator is used to carry out the simulation of the clouds. This input is also used to introduce a triangular signal used for the tuning of the PI controller. The results show that the performance of the system, characterized by the voltage of the PV generator, with simulated clouds is similar to the response with real clouds. With regard to the tuning, the new method achieves better performance than previous methods. These methods can be applied on clear days, under conditions of constant irradiance, which greatly simplifies its implementation and greatly reduces the time required for commissioning the system.

Artificial Intelligence-Empowered 3D and 4D Printing Technologies toward Smarter Biomedical Materials and Approaches.

In the last decades, 3D printing has played a crucial role as an innovative technology for tissue and organ fabrication, patient-specific orthoses, drug delivery, and surgical planning. However, biomedical materials used for 3D printing are usually static and unable to dynamically respond or transform within the internal environment of the body. These materials are fabricated ex situ, which involves first printing on a planar substrate and then deploying it to the target surface, thus resulting in a possible mismatch between the printed part and the target surfaces. The emergence of 4D printing addresses some of these drawbacks, opening an attractive path for the biomedical sector. By preprogramming smart materials, 4D printing is able to manufacture structures that dynamically respond to external stimuli. Despite these potentials, 4D printed dynamic materials are still in their infancy of development. The rise of artificial intelligence (AI) could push these technologies forward enlarging their applicability, boosting the design space of smart materials by selecting promising ones with desired architectures, properties, and functions, reducing the time to manufacturing, and allowing the in situ printing directly on target surfaces achieving high-fidelity of human body micro-structures. In this review, an overview of 4D printing as a fascinating tool for designing advanced smart materials is provided. Then will be discussed the recent progress in AI-empowered 3D and 4D printing with open-loop and closed-loop methods, in particular regarding shape-morphing 4D-responsive materials, printing on moving targets, and surgical robots for in situ printing. Lastly, an outlook on 5D printing is given as an advanced future technique, in which AI will assume the role of the fifth dimension to empower the effectiveness of 3D and 4D printing for developing intelligent systems in the biomedical sector and beyond.

Classical and Modern gain estimation approach of PID controller for the pitch control of the RCTA aircraft

In the current age of globalization, the development of autopilots is superficial as the standard of living is improved through aerial surveillance, defense applications, and door- to-door transportation. To design the PID controller, a mathematical model is created to address the system identification for estimating longitudinal derivatives and to study the handling qualities of aircraft in order to improve piloting performance. This paper exhibits a comparative assessment between the classical closed-loop PID tuning methods like ZN, Modified ZN, Tyreus-luyben, Astrom- Hagglund and the modern control techniques like pole placement, LQR for the pitch controller design. The simulation results are displayed in the time-domain, which demonstrates the effectiveness of the approach used to design a robust controller.

A Survey on Capacitor Voltage Control in Neutral-Point-Clamped Multilevel Converters

Neutral-point-clamped multilevel converters are currently a suitable solution for a wide range of applications. It is well known that the capacitor voltage balance is a major issue for this topology. In this paper, a brief summary of the basic topologies, modulations, and features of neutral-point-clamped multilevel converters is presented, prior to a detailed description and analysis of the capacitor voltage balance behavior. Then, the most relevant methods to manage the capacitor voltage balance are presented and discussed, including operation in the overmodulation region, at low frequency-modulation indexes, with different numbers of AC phases, and with different numbers of levels. Both open- and closed-loop methods are discussed. Some methods based on adding external circuitry are also presented and analyzed. Although the focus of the paper is mainly DC–AC conversion, the techniques for capacitor voltage balance in DC–DC conversion are discussed as well. Finally, the paper concludes with some application examples benefiting from the presented techniques.

Seismic Inversion Based on 2D-CNNs and Domain Adaption

Deep learning has been applied to tackle the seismic inversion problem, bringing more efficiency and accuracy. However, bad spatial continuity and poor generalizability limit the practical application. To solve these problems, we propose a 2D end-to-end seismic inversion method based on domain adaption. Firstly, the proposed 2D network learns the inversion mapping of seismic data under the constraint of domain adaption layer, which can reduce the difference between the features of real seismic data and synthetic seismic data, improving the generalization ability on real seismic data. Then, the trained model is finetuned with well logging data. In the first process, the spatial continuity of the inversion result is guaranteed by the 2D training scheme. Meanwhile, due to the constraint of the domain adaption layer, our model not only performs well on the synthetic data but also has good generalization ability on the real seismic data. And we carefully discuss the mechanism of domain adaption layer. In the second process, finetuning introduces well logging information, which can further improve the ability to invert details. Moreover, in order to improve the inversion accuracy on real seismic data, we develop a new training data generation method that can generate the synthetic samples close to the real samples, and a 2.5D training strategy is adopted to improve the continuity of the 3D data. The experiments on both synthetic and real seismic data show that our method performs better than both the recursive inversion method and the 1D closed-loop CNN methods.