Feedback Control Loop Research Articles

Controlled removal of hydrogen atoms from H-terminated silicon surfaces

The controlled formation of dangling bond structures on a H-terminated silicon surface is the first step in an atomically precise method of fabrication of silicon quantum electronic devices. An ultrahigh vacuum scanning tunneling microscope (STM) tip is used to selectively desorb hydrogen atoms from a Si(100)-2×1:H surface by injecting electrons with the sample held at a positive bias voltage. The authors propose a lithography method that allows the STM to operate under negative bias imaging conditions and simultaneously desorb H atoms as required. A high frequency signal is added to the negative bias voltage to deliver the required energy for hydrogen removal. The resulting current at this frequency and its harmonics are filtered to minimize their effect on the operation of the STM’s feedback control loop. The authors show that the chance of tip-sample crash during the lithography process is reduced by employing this method. They also demonstrate that this approach offers a significant potential for controlled and precise removal of H atoms from a H-terminated silicon surface and thus may be used for the fabrication of practical silicon-based atomic-scale devices.

Cryo-EM structures from sub-nl volumes using pin-printing and jet vitrification

The increasing demand for cryo-electron microscopy (cryo-EM) reveals drawbacks in current sample preparation protocols, such as sample waste and lack of reproducibility. Here, we present several technical developments that provide efficient sample preparation for cryo-EM studies. Pin printing substantially reduces sample waste by depositing only a sub-nanoliter volume of sample on the carrier surface. Sample evaporation is mitigated by dewpoint control feedback loops. The deposited sample is vitrified by jets of cryogen followed by submersion into a cryogen bath. Because the cryogen jets cool the sample from the center, premounted autogrids can be used and loaded directly into automated cryo-EMs. We integrated these steps into a single device, named VitroJet. The device’s performance was validated by resolving four standard proteins (apoferritin, GroEL, worm hemoglobin, beta-galactosidase) to ~3 Å resolution using a 200-kV electron microscope. The VitroJet offers a promising solution for improved automated sample preparation in cryo-EM studies.

Distributed Supervisory Secondary Control for a DC Microgrid

This article investigates the current sharing and voltage regulation problem of a DC microgrid by a distributed supervisory control method. In the existing secondary control methods, current sharing feedback control loop and voltage regulation feedback control loop are linearly integrated to shift the droop equation. However, the linear combination of two loops may easily offset the control effect of each other, as the control dimension is less than the objective dimension. This fact motivates us to design a distributed supervisory secondary control (DSSC) scheme to achieve a more flexible configuring of each converter. The DSSC consists of two candidate controllers and a supervisor. The two controllers refer to current sharing configuring and DC bus voltage regulation respectively, and the supervisor is to orchestrate the switching between candidate controllers based on the system environment of DC microgrid. Under the proposed DSSC, each converter selects to configure the current sharing or regulates the DC bus voltage according to its need. Theoretical stability analysis is rigorously conducted. Finally, both simulation and experiment results are presented to demonstrate the effectiveness of DSSC.

Skyhook-PID Control Strategy to Improve Performance of a Pneumatic Active Suspension System

The research applies a skyhook-PID control method for an active suspension system. The control strategy has three feedback control loops. They are the innermost loop for the force tracking of the pneumatic actuator, the intermediate loops applying skyhook strategy for the elimination of the disturbances, and the outermost loop using PID controller for the determination of the desired force. Some experiments were carried out on a physical test rig with a hardware-in-the-loops feature. The performance of the proposed control method was evaluated and benchmarked to examine the effectiveness of the system in suppressing the disturbance effect of the suspension system. It was found that the experimental results demonstrate the superiority of the active suspension system with Skyhook-PID scheme compared to the PID and passive suspension systems.

Effect of rotator cuff muscle activation on glenohumeral kinematics: A cadaveric study

Healthy shoulder function requires the coordination of the rotator cuff muscles to maintain the humeral head’s position in the glenoid. While glenohumeral stability has been studied in various settings, few studies have characterized the effect of dynamic rotator cuff muscle loading on glenohumeral translation during shoulder motion. We hypothesize that dynamic rotator cuff muscle activation decreases joint translation during continuous passive abduction of the humerus in a cadaveric model of scapular plane glenohumeral abduction. The effect of different rotator cuff muscle activity on glenohumeral translation was assessed using a validated shoulder testing system. The Dynamic Load profile is a novel approach, based on musculoskeletal modeling of human subject motion. Passive humeral elevation in the scapular plane was applied via the testing system arm, while the rotator cuff muscles were activated according to the specified force profiles using stepper motors and a proportional control feedback loop. Glenohumeral translation was defined according to the International Society of Biomechanics. The Dynamic load profile minimized superior translation of the humeral head relative to the conventional loading profiles. The total magnitude of translation was not significantly different (0.805) among the loading profiles suggesting that the compressive forces from the rotator cuff primarily alter the direction of humeral head translation, not the magnitude. Rotator cuff muscle loading is an important element of cadaveric shoulder studies that must be considered to accurately simulate glenohumeral motion. A rotator cuff muscle activity profile based on human subject muscle activity reduces superior glenohumeral translation when compared to previous RC loading profiles.

Local self-optimizing control based on extremum seeking control

Self-optimizing Control (SOC) aims to find controlled variables with which setpoint regulation of the resultant feedback control loops can yield near-optimal operation under a range of disturbances. However, standard local SOC methods, e.g. the null-space SOC, require an offline analysis with large amounts of steady-state data, which can be computationally cumbersome. In this paper, we propose a new SOC procedure enabled by extremum seeking control (ESC) which will largely simplify the offline analysis process of null-space or extended null-space SOC methods. First, ESC is used to determine the optimal manipulated variable values under the nominal condition for the system. Next, by dithering the plant with periodic disturbances, the dither-demodulation technique in ESC is used to estimate the Jacobian and Hessian needed for obtaining the optimal measurement combination; then the null-space and extended null-space methods can be carried out in a computationally efficient fashion, for the scenarios with noise-free and noisy measurements, respectively. The proposed procedure are compared with the standard null-space and extended null-space SOC methods using a Modelica-based dynamic simulation model of an air-source heat pump (ASHP) system. The results show that a similar performance can be achieved with much simpler process of data acquisition and processing.

An improved 2-degree-of-freedom internal model proportional–integral–derivative controller design for stable time-delay processes

In this article, an enhanced 2-degree-of-freedom internal model control strategy for typical industrial processes with time-delay is developed. For the proposed controller, it is composed of an inner loop feedback controller which is designed based on the internal model control theory and a weighted set-point tracking controller. Note that the adjustment of set-point tracking performance and disturbance rejection characteristics can be decoupled by employing the developed strategy, which indicates that more degrees of freedom are obtained for the proposed controller design; thus, better ensemble performance and stronger robustness are anticipated by regulating these two controllers separately, which may not be achieved in the conventional internal model control method. Case studies on two kinds of stable processes with time-delay verify the effectiveness of the proposed scheme finally.

Symmetries and Isomorphisms for Privacy in Control Over the Cloud

Cloud computing platforms are being increasingly used for closing feedback control loops, especially when computationally expensive algorithms, such as model-predictive control, are used to optimize performance. Outsourcing of control algorithms entails an exchange of data between the control system and the cloud and, naturally, raises concerns about the privacy of the control system's data (e.g., state trajectory and control objective). Moreover, any attempt at enforcing privacy needs to add minimal computational overhead to avoid degrading control performance. In this article, we propose several transformation-based methods for enforcing data privacy. We also quantify the amount of provided privacy and discuss how much privacy is lost when the adversary has access to side knowledge. We address three different scenarios: 1) the cloud has no knowledge about the system being controlled; 2) the cloud knows what sensors and actuators the system employs but not the system dynamics; and 3) the cloud knows the system dynamics, its sensors, and actuators. In all of these three scenarios, the proposed methods allow for the control over the cloud without compromising private information (which information is considered private depends on the considered scenario).

Anytime Computation and Control for Autonomous Systems

The correct and timely completion of the sensing and action loop is of utmost importance in safety critical autonomous systems. Crucial to the performance of this feedback control loop are the computation time and accuracy of the estimator which produces state estimates used by the controller. These state estimators often use computationally expensive perception algorithms like visual feature tracking. With on-board computers on autonomous robots being computationally limited, the computation time of such an estimation algorithm can at times be high enough to result in poor control performance. We develop a framework for codesign of anytime estimation and robust control algorithms, taking into account computation delays and estimation inaccuracies. This is achieved by constructing an anytime estimator from an off-the-shelf perception-based estimation algorithm and obtaining a trade-off curve for its computation time versus estimation error. This is used in the design of a robust predictive control algorithm that at run-time decides a contract, or operation mode, for the estimator in addition to controlling the dynamical system to meet its control objectives at a reduced computation energy cost. This codesign provides a mechanism through which the controller can use the tradeoff curve to reduce estimation delay at the cost of higher inaccuracy, while guaranteeing satisfaction of control objectives. Experiments on a hexrotor platform running a visual-based algorithm for state estimation show how our method results in up to a 10% improvement in control performance while simultaneously saving 5%–6% in computation energy as compared to a method that does not leverage the codesign.

A Recursive Watermark Method for Hard Real-Time Industrial Control System Cyber-Resilience Enhancement

Cybersecurity is of vital importance to industrial control systems (ICSs), such as ship automation, manufacturing, building, and energy automation systems. Many control applications require hard real-time channels, where the delay and jitter are in the levels of milliseconds or less. To the best of our knowledge, no encryption algorithm is fast enough for hard real-time channels of existing industrial fieldbuses and, therefore, made mission-critical applications vulnerable to cyberattacks, e.g., delay and data injection attacks. In this article, we propose a novel recursive watermark (RWM) algorithm for hard real-time control system data integrity validation. Using a watermark key, a transmitter applies watermark noise to hard real-time signals and sends through the unencrypted hard real-time channel. The same key is transferred to the receiver by the encrypted nonreal-time channel. With the same key, the receiver can detect if the data have been modified by the attackers and take action to prevent catastrophic damages. We provide analysis and methods to design proper watermark keys to ensure reliable attack detection. We use a ship propulsion control system for the simulation-based case study, where our algorithm smoothly shuts down the system after attacks. We also evaluated the algorithm speed on a Siemens S7-1500 programmable logic controller (PLC). This hardware experiment demonstrated that the RWM algorithm takes about $2.8~\mu \text{s}$ to add or validate the watermark noise on one sample data point. As a comparison, common cryptic hashing algorithms can hardly process a small data set under 100 ms. The proposed RWM is about 32 to 1375 times faster than the standard approaches. Note to Practitioners —It is widely believed that the emerging Internet-of-Things (IoT) technologies will seamlessly connect countless smart devices, profoundly change the industry. Traditionally, field devices within the feedback control loops are isolated from the Internet by secure gateways. In the future, field devices will connect to the Internet in a more direct manner. To the best of our knowledge, no encryption algorithm is fast enough for hard real-time channels of existing industrial fieldbuses and, therefore, made mission-critical applications vulnerable to cyberattacks. We propose a novel recursive watermark (RWM) algorithm for hard real-time control system data integrity validation. This article serves industry practitioners in three ways. First, it is a timely caution to industrial IoT (IIoT) pilot users on the security challenges in real-time channels. Once a compromised edge device is connected to a field device, attackers have unlimited means to jeopardize valuable assets. In this article, we gave an example where attackers may damage shipboard assets by introducing millisecond-level delays. Second, since hard real-time encryption is not available, we propose a detour solution. With the proposed algorithm, even attackers may read the content in the real-time channel, and they cannot change it without being detected. We implemented the real-time RWM algorithms in structured control language (SCL) and tested on a Siemens S7-1500 programmable logic controller (PLC). Third, we provide theoretical analysis as design guidelines for practitioners to set up or customize the RWM algorithm per their specific applications.

Control of heat exchangers in series using neural network predictive controllers

Abstract The paper reveals three applications of neural network predictive control (NNPC) to a system of four heat exchangers (HEs) in series with counterflow configuration to save energy expressed by cooling water in the system of HEs cooling the distillation product. Neural networks (NNs) are used at first in conventional NNPC and subsequently, neural network predictive controllers (NNPCLs) are employed as a master controller in a cascade control, and as a feedback controller in the control system with disturbance measurement. Neural-network-predictive-control-based (NNPC-based) feedback control systems are compared with PI controller based feedback control loop. Series of simulation experiments were done and the results showed that using NNPC-based cascade control reduced cooling water consumption. This control system also significantly reduced the settling time and overshoots in the control responses and provided the best assessed integral quality criteria compared to other control systems. NNPC-based cascade control can also be interesting for industrial use. Generally, simulation results proved that NNPC-based control systems are promising means for the improvement of HEs control and achievement of energy saving.

MAPE-K/MAPE-SAC: An interaction framework for adaptive systems with security assurance cases

Security certification establishes that a given system satisfies properties and constraints as specified in the system security profile. Mechanisms and techniques have been developed to assess if and how well the system complies with the properties, thereby providing a degree of confidence in the security certification. Generally, certification of security controls defined by NIST SP800-53 is performed at design time to provide confidence in a system’s trustworthiness to achieve the organization’s mission and business requirements. Assuring confidence in a self-adaptive system’s security profile is challenging when both functional and security conditions may change at run time. Static security solutions are insufficient, given that dynamic application of defense mechanisms often needs to dynamically adapt security functionality at run time as part of self-protection. This security adaptation may hinder maintaining functional constraints or vice versa. In addition, adaptation capabilities may give rise to the need for dynamic certification, which can be a difficult procedure given the complexity of the security dependencies. Confidence in an information system’s compliance with security constraints can be expressed using security assurance cases (SACs). NIST security controls are defined with a hierarchical structure that makes them amenable to being specified in terms of SACs. A collection of SACs for related security controls form a network that can be used to measure the confidence of security compliance through certification-based evidence. Once the system is deployed, environmental and functional uncertainties may require the coordination of functional and security adaptations. This paper introduces the MAPE-SAC, a security-focused feedback control loop, and its interaction with a MAPE-K, function and performance-focused control loop, to dynamically manage run-time adaptations in response to changes in functional and security conditions. We illustrate the use of both control loops and their interaction with an example of two independent systems that need to cooperate to facilitate autonomous search and rescue in the aftermath of a natural disaster.

Preventing stroke saturation of inertial actuators used for active vibration control of floor structures

Active vibration control techniques using inertial-mass actuators have gained some level of acceptance in civil structures. Recent research indicates the effectiveness of this technique in mitigation of human-induced excitation in pedestrian structures such as floors and footbridges. However, there are some drawbacks associated with the use of inertial-mass actuators which needs to be dealt with. One of the main disadvantages of using inertial actuators is their stroke saturation non-linearity. When the stroke saturation phenomenon happens, excessive movement of inertial mass along the stroke hits the ends of actuator and can destabilize the system or even damage the actuator. This paper presents a novel velocity feedback control strategy to robustly prevent stroke saturation of inertial actuator. Two inner loops are added into a direct velocity feedback (DVF) control loop. First inner loop is a proportional-derivative (PD) controller based on the measured displacement of inertial mass. The second inner loop is implemented as a DVF gain modifier based on the actuator mass displacement over-range. This adaptively reduces the DVF gain, by an amount proportional to the over-range by displacement ratio, when inertial mass displacement is predicted to exceed the certain limit. Theoretical and experimental study of the control strategy is examined on a laboratory scale floor structure using an inertial-mass actuator. Both the results demonstrate the effectiveness of the proposed control strategy. The displacement of actuator's mass is kept within the stroke limits while satisfactory control performance is maintained.

Mathematical modeling for short term indoor room temperature forecasting using Box-Jenkins models

PurposeThe rapid urbanization of Indian cities and the population surge in cities has steered a massive demand for energy, thereby increasing the carbon emissions in the environment. Information and technology advancements, aided by predictive tools, can optimize this energy demand and help reduce harmful carbon emissions. Out of the multiple factors governing the energy consumption and comfort of buildings, indoor room temperature is a critical one, as it envisages the need for regulating the temperature. This paper aims to propose a mathematical model for short-term forecasting of indoor room temperature in the Indian context to optimize energy consumption and reduce carbon emissions in the environment.Design/methodology/approachA study is conducted to forecast the indoor room temperature of an Indian corporate building structure, based upon various external environmental factors: temperature and rainfall and internal factors like cooling control, occupancy behavior and building characteristics. Expert insight and principal component analysis are applied for appropriate variables selection. The machine learning approach using Box–Jenkins time series models is used for the forecasting of indoor room temperature.FindingsARIMAX model, with lagged forecasted and explanatory variables, is found to be the best-fit model. A predictive short-term hourly temperature forecasting model is developed based upon ARIMAX model, which yields fairly accurate results for data set pertaining to the building conditions and climatic parameters in the Indian context. Results also investigate the relationships between the forecasted and individual explanatory variables, which are validated using theoretical proofs.Research limitations/implicationsThe models considered in this research are Box–Jenkins models, which are linear time series models. There are non-linear models, such as artificial neural network models and deep learning models, which can be a part of this study. The study of hybrid models including combined forecasting techniques comprising linear and non-linear methods is another important area for future scope of study. As this study is based on a single corporate entity, the models developed need to be tested further for robustness and reliability.Practical implicationsForecasting of indoor room temperature provides essential practical information about meeting the in-future energy demand, that is, how much energy resources would be needed to maintain the equilibrium between energy consumption and building comfort. In addition, this forecast provides information about the prospective peak usage of air-conditioning controls within the building indoor control management system through a feedback control loop. The resultant model developed can be adopted for smart buildings within Indian context.Social implicationsThis study has been conducted in India, which has seen a rapid surge in population growth and urbanization. Being a developing country, India needs to channelize its energy needs judiciously by minimizing the energy wastage and reducing carbon emissions. This study proposes certain pre-emptive measures that help in minimizing the consumption of available energy resources as well as reducing carbon emissions that have significant impact on the society and environment at large.Originality/valueA large number of factors affecting the indoor room temperature present a research challenge for model building. The paper statistically identifies the parameters influencing the indoor room temperature forecasting and their relationship with the forecasted model. Considering Indian climatic, geographical and building structure conditions, the paper presents a systematic mathematical model to forecast hourly indoor room temperature for next 120 h with fair degree of accuracy.

Fiber optic interferometric seismometer with phase feedback control.

A high-sensitivity ultralow-frequency fiber optic interferometric seismometer using phase feedback control is proposed and demonstrated. The principle of sensitivity improvement using feedback is described, and the characteristics of the seismometer, including the ultralow-frequency vibration sensing with Michelson interferometer with and without feedback control, are analyzed in terms of the amplitude response and phase response. The phase feedback control loop is designed and implemented, and higher sensitivity for very low frequency vibration is achieved. The efficacy of the new approach is demonstrated experimentally, showing that the weak vibration signal originally buried in noise can be observed unambiguously.

Dronument: System for Reliable Deployment of Micro Aerial Vehicles in Dark Areas of Large Historical Monuments

This letter presents a self-contained system for robust deployment of autonomous aerial vehicles in environments without access to global navigation systems and with limited lighting conditions. The proposed system, application-tailored for documentation in dark areas of large historical monuments, uses a unique and reliable aerial platform with a multi-modal lightweight sensory setup to acquire data in human-restricted areas with adverse lighting conditions, especially in areas that are high above the ground. The introduced localization method relies on an easy-to-obtain 3-D point cloud of a historical building, while it copes with a lack of visible light by fusing active laser-based sensors. The approach does not rely on any external localization, or on a preset motion-capture system. This enables fast deployment in the interiors of investigated structures while being computationally undemanding enough to process data online, onboard an MAV equipped with ordinary processing resources. The reliability of the system is analyzed, is quantitatively evaluated on a set of aerial trajectories performed inside a real-world church, and is deployed onto the aerial platform in the position control feedback loop to demonstrate the reliability of the system in the safety-critical application of historical monuments documentation.

PKCα and Netrin-1/UNC5B positive feedback control in relation with chemical therapy in bladder cancer.

To explore the cisplatin medical tolerance mechanism affecting bladder cancer cells. Bladder cancer cells were treated with protein kinase C α (PKCα) stimulation and inhibition agents. The small-interfering RNA (siRNA) inhibitory technique was used to differentiate and remove Netrin-1 from UNC5B. Cells treated by cisplatin were processed by the MIT method to estimate cell death and growth rate. The Western blot method was utilized to analyze PKCα, netrin-1, and UNC5B in bladder cancer cells, used in the relative control sample. Co-immunoprecipitation was employed to analyze PKCα, netrin-1, and UNC5B combination effects. PKCα high activity, netrin-1 high expression, and UNC5B with low expression can enhance bladder cancer cells cisplatin medical tolerance. PKCα low activity, netrin-1 low expression, and UNC5B with high expression can also enhance bladder cancer cells sensitivity to chemical therapeutic treatments. PKCα high activity with enhanced netrin-1 reduced UNC5B expression, and also enhanced netrin-1/UNC5B combination. It inhibits and/or deletes PKCα, Netrin-1 lower stream extracellular regulated protein kinases (ERK) signal, deletes UNC5B, PKCα, and lowers stream (ERK) signaling from activity. PKCα and netrin-1/UNC5B form a positive feedback control loop in relation to the regulation of cisplatin in bladder cancer cells.

Automated control algorithms for silicon photonic polarization receiver.

We demonstrate greedy linear descent-based, basic gradient descent-based, two-point step size gradient descent-based, and two-stage optimization method-based automated control algorithms and examine their performance for use with a silicon photonic polarization receiver. With an active feedback loop control process, time-varying arbitrary polarization states from an optical fiber can be automatically adapted and stabilized to the transverse-electric (TE) mode of a single-mode silicon waveguide. Using the proposed control algorithms, we successfully realize automated adaptations for a 10 Gb/s on-off keying signal in the polarization receiver. Based on the large-signal measurement results, the control algorithms are examined and compared with regard to the iteration number and the output response. In addition, we implemented a long-duration experiment to track, adapt, and stabilize arbitrary input polarization states using the two-point step size gradient descent-based and two-stage optimization method-based control algorithms. The experimental results show that these control algorithms enable the polarization receiver to achieve real-time and continuous polarization management.

A Progressive Output Strategy for Real-time Feedback Control Systems

The real-time requirements imposed on a feedback control system are often hard to be met, as the controller spends a disproportionately large amount of time waiting for a control cycle to reach its final state. When such a final state is established, multiple tasks have to be prioritized and launched altogether simultaneously, and the system is given an extremely short time window to generate its output. This huge gap between the wait and action times, perceived as a load unbalancing problem, hinders a control decision to be made in real time. To address this challenging problem, in this paper, we present a progressive output strategy that divides a control cycle into a few fine-grained control intervals, and the entire workload is scheduled across these control intervals. Dubbed as Progressive Output Strategy (PROS), this approach actively requests intermediate states be created between adjacent control cycles in an adaptive manner. Specifically, as the sensing information is arriving, a system that adopts PROS can generate a series of intermediate solutions that eventually converge to the final optimal control signal. This way, the controller will no longer waste its time idling while waiting for the arrival of all the data for one-shot decision-making. Rather the system actually cuts down the waiting time and is able to act on the intermediate data/states throughout the entire control cycle. Experimental results have confirmed that adopting the PROS in a feedback control loop can evenly distribute the workload over a control cycle, and thus, the time delay is reduced by as much as two orders of magnitude, which is essential to meet the most stringent timing requirements.

A Predictor-Corrector Algorithm Based on Laurent Series for Biological Signals in the Internet of Medical Things

In future engineered systems for medical applications, a tight real-time integration between physical and computational processes will be required. That integration is achieved using feedback control loops which need high quality input data streams. However, hardware platforms can barely provide such high-quality data sequences (especially if mobile nodes are considered), and mechanisms to improve and polish physical and biological signals are then necessary. This paper proposes a predictor-corrector algorithm to improve the quality and precision of data (biological) signals in Internet of Medical Things deployments, especially if composed of mobile nodes. The proposed algorithm employs an Artificial Intelligence approach and statistical learning techniques to predict future data samples and correct errors in received information. Employed mathematical models follow a prediction-correction scheme and are based on complex functions, Laurent series and the idea of complex envelope. Simulation techniques are used to evaluate the performance of the proposed solution, showing that it improves the precision of traditional linear interpolation techniques up to 85%, and cubic splines up to 20%. Processing delay during operation is, for the referred precision, around 200ms.