Real-time Control Technique Research Articles

A Koopman-based residual modeling approach for the control of a soft robot arm

Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.

Highly efficient inverse lumped modeling for the pre-strained circular dielectric elastomer

The equi-biaxially pre-strained circular dielectric elastomer actuator (PCDEA) is a classical actuator configuration to demonstrate the electromechanical deformation mechanism of dielectric elastomer. Till now, there still lacks an effective method to analyze the non-uniform voltage-induced deformation of PCDEA with both high accuracy and low computational cost. Besides, the inverse problem that determines the voltage input for a desired deformation is also a hard problem for the current modeling approaches. In this work, a novel shape function approximation method is proposed to model the non-uniform deformation of PCDEA in a highly accurate lumped-parameter way. The method can solve both the inverse problem and forward problem of the PCDEA, making it possible to position the onset of electromechanical instability (EMI) of this configuration. Detailed comparisons are made between this method and high-resolution finite element analysis to verify its accuracy. This method also features low computational cost, providing opportunities for developing model-based real-time control and proprioception techniques. Further investigation is also carried out on how material types and configurational parameters interact with PCDEAs’ performance, along with checking the findings with experiment observation. Guidelines for DE material development and actuator design are also summarized accordingly.

Hydro Electrometric Tracking Application

We offer a brand-new technique for real-time control over the project's water flow rate and electricity usage. Unlike current systems, which are frequently costly and solely monitor power or water, our method integrates both parameters into a single, reasonably priced platform. Our solution allows users to track their resource consumption in real-time by utilizing Arduino-based sensors and a user-friendly mobile application created with MIT App Inventor. By providing consumers with the knowledge, they need to make educated decisions about how much water and power to use, this promotes more effective and sustainable resource management. It is unique in that it provides an affordable solution that integrates water and power metering capabilities, solving major issues with current systems.

Toward a water-sensitive precinct with stormwater harvesting: a case study in South Africa

ABSTRACT The study assessed static management, i.e., without planned periodic maintenance of the storage (no sediment dredging) denoted as SC1, or with maintenance denoted as SC2. The second approach was dynamic management based on applying Real-Time Control (RTC) techniques to scenario SC1 and scenario SC2 denoted as RTC-1 and RTC-2, respectively. The dynamic management of the UCT dam with RTC-1 and RTC-2 approaches increase yield by 2.1 and 1.1 ml, respectively. Additionally, RTC-1 and RTC-2 approaches increase volumetric reliability by 5.3 and 2.5%, respectively, while maintaining the required level of service of a stormwater harvesting system. SC1 and SC2 results in water savings of up to 21.15 and 21.45 ZAR/kl, respectively, while RTC-1 and RTC-2 could save up to 19.35 and 19.45 ZAR/kl. Thus, static configurations results in water savings approximately 9% in comparison to RTC. In addition, static configurations harvested stormwater at a relatively lowest unit cost in comparison to RTC configurations. Notwithstanding this finding, the RTC system exhibits a great potential in reshaping the stormwater harvesting system to simultaneously deliver water conservation and stormwater management.

Optimization and evaluation of a neural network based policy for real-time control of construction factory processes

This paper focuses on the development, optimization, and evaluation of an intelligent real-time control system for the fabrication of precast reinforced concrete components. The study addresses the unique challenges associated with real-time control in the construction manufacturing industry, including high customization, uncertain work demand, and limited stockpiling opportunities. A production system model is built based on a real construction manufacturing factory to simulate real-world precast reinforced concrete component fabrication, and acts as the basis for the development and validation of the control system. A review of alternative decision-making techniques is presented to identify the most suitable for the control of construction manufacturing factories. Ultimately, an artificial neural network approach trained using a reinforcement learning strategy is selected as a promising technique for effective real-time control. The controller is developed and validated, and its performance is optimized using sensitivity analysis, which takes into account both the structure of the artificial neural network and the parameters of the reinforcement learning algorithm. The ANN-based control policy is applied to the sequencing of precast reinforced concrete component production, while a rule-of-thumb policy is used as a benchmark for comparison. The study demonstrates that the optimized ANN-based control policy significantly outperforms the standard rule-of-thumb policy. The paper concludes by providing suggestions for further advancement of the ANN-based approach and potential avenues to increase the control policy's scope of application in construction manufacturing.

Compression Techniques for Real-Time Control and Non-Time-Critical Big Data in Smart Grids: A Review

Significant amounts of data need to be transferred in order to optimize the operation of power grids. The development of advanced metering and control infrastructure ensures a growth in the amount of data transferred within smart grids. Data compression is a strategy to reduce the burden. This paper presents current challenges in the field of time-series data compression. This paper’s novel contribution is the division of data in smart grids to real-time data used for control purposes and big data sets used for non-time-critical analysis of the system. Both of these two applications have different requirements for effective compression. Currently used algorithms are listed and described with their advantages and drawbacks for both of these applications. Details needed for the implementation of an algorithm were also provided. Comprehensive analysis and comparison are intended to facilitate the design of a data compression method tailored for a particular application. An important contribution is the description of the influence of data compression methods on cybersecurity, which is one of the major concerns in modern power grids. Future work includes the development of adaptive compression methods based on artificial intelligence, especially machine learning and quantum computing. This review will offer a solid foundation for the research and design of data compression methods.

A Novel Real-Time Robust Controller of a Four-Wheel Independent Steering System for EV Using Neural Networks and Fuzzy Logic

In this study a four-wheel independent steering (4WIS) system for an electric vehicle (EV) steered by stepper motors is presented as a revolutionary real-time control technique employing neural networks in combination with fuzzy logic, where the use of the neural network greatly simplifies the computational process of fuzzy logic. The control of the four wheels is based on a variation of a Hopfield Neural Network (VHNN) method, in which the input is the error of each steering motor and the output is processed by a hyperbolic tangent function (HTF) feeding the fuzzy logic controller (FLC), which ultimately drives the stepper motor. The whole system consists of the four aforementioned blocks which work in sync and are inseparable from each other with the common goal of driving all the steering stepper motors at the same time. The novelty of this system is that each wheel monitors the condition of the others, so even in the case of the failure of one wheel, the vehicle does not veer off course. The results of the simulation show that the suggested control system is very resilient and workable at all angles and speeds.

Assessing the benefits of real-time control to enhance rainwater harvesting at a building in Cape Town, South Africa

In the period 2015–2017, the City of Cape Town, South Africa, faced the possibility of taps running dry due to a prolonged drought. To mitigate the impacts of water scarcity, many households installed rainwater tanks to harvest water to use for non-potable purposes such as toilet flushing and washing. The installation of the rainwater tanks was mainly arbitrary, in response to perceived impact of water scarcity rather than a systematic needs assessment. This study was thus undertaken to determine the available opportunity to optimise the use of these rainwater tanks using real-time control (RTC) techniques. Many studies have demonstrated the potential of rainwater harvesting (RWH) systems to supplement potable water supply and minimize stormwater flows to downstream drainage networks. RTC technology can be used to enhance the performance of RWH systems in achieving these two objectives, by receiving a rainfall forecast and initiating pre-storm release in real time. In this study, RTC was applied on the RWH system at the New Engineering Building, University of Cape Town (UCT) to enhance water supply and increase rainwater retention period. The performance with RTC was compared with the conventional management of the RWH system. It was determined that RWH with RTC technology was generally superior in simultaneously achieving water supply and rainwater retention benefits compared to the conventional management approach. RTC provides an active operation which optimizes the performance of the system across varying conditions but requires an assiduous management process designed to meet set objectives. It was concluded that the active release mechanism employing RTC exhibited great potential; the system opens up the possibility of delivering a more robust and reliable system due to its ability to provide failure detection and centralised control. The system can readily be adapted to variation of local climatic conditions in the short and long term.

Advanced Image and Video Processing Techniques for Real-Time Quality Control in Laboratory Benchtop Equipment

Advanced Image and Video Processing Techniques for Real-Time Quality Control in Laboratory Benchtop Equipment

Magnetically tightened multifunctional phase change materials

As a non-contact, real-time, accurate, and flexible control technique, the magnetic field has been widely utilized in a variety of applications, including industrial grab, robot drives, medical assistance, etc. However, non-magnetic phase change materials (PCMs) are difficult to manipulate with magnetic fields. Recently in <i>Nature Communications</i>, He et al. innovatively proposed magnetically tightened form-stable PCMs with the integrated multi-functions of being leakage-proof, having highly thermal/electrical conductivities, robust mechanical strength, dynamic assembly, and shape reconfiguration by introducing hard magnetic particles. Additionally, they developed free-standing temperature control and high-performance thermoelectric conversion systems based on magnetically tightened form-stable PCMs.

Patient-based real-time quality control: A primer

Patient-based real-time quality control (PBRTQC) techniques utilise routinely generated patient results to monitor assay performance. These techniques have been described in the clinical laboratory for more than 50 years. PBRTQC has a number of purported advantages over traditional quality control (QC) including commutability, cost and the opportunity for real-time monitoring. Despite these benefits, the widespread uptake of PBRTQC methods in many laboratories has been slow. In this session, the basics of a PBRTQC program in clinical chemistry will be discussed along with recent developments in the field.

Prediction of the ice resistance of icebreakers using explicit finite element analyses with a real-time load control technique

This paper proposes a simulation technique to real-time control hydrostatic and hydrodynamic forces acting on pack ices depending on a user-subroutine of an explicit finite element code. By controlling the buoyancy for the wet elements, the hydrostatic force was possible in terms of a nonlinear restoring force. The hydrodynamic force acting on a pack ice was expressed as a drag force. The CFD analyses for a single pack ice under constant surge speed were carried out to estimate the total resistance and accordingly to calibrate the average drag coefficient. The proposed approach with the average drag coefficient produced same results with the CFD analyses. The user-subroutine with the drag coefficient was used to simulate the towing tests of an icebreaker in the pack ice condition. There were different ice resistances between the test and simulations. This forced to recalibrate the drag coefficient considering the difference of the stiffnesses in the towing test and simulations. After recalibration of the drag coefficient to 2.0, there were successful coincidences between the towing tests and simulations in terms of the ice resistances. The radiation effects were critical in the motion of pack ice, so the radiation force should be considered for future study.

Evaluation of Data-Driven and Process-Based Real-Time Flow Forecasting Techniques for Informing Operation of Surface Water Abstraction

AbstractThis paper presents an approach to managing surface water abstraction utilizing real-time flow forecasting and control techniques. To evaluate the effectiveness of alternative data-driven a...

Real-time neuro-fuzzy controller for pressure adjustment in water distribution systems

Abstract This work applied a neuro-fuzzy technique for real-time pressure control in water distribution systems with variable demand. The technique acted to control the rotation speed of the pumping system, aiming mainly at increasing energy efficiency. Fuzzy, neural and neuro-fuzzy controllers were tested in an experimental setup to compare their performances in a transient regime, a permanent regime, and with respect to disturbances applied to the system. To evaluate the efficiency of the system, a demand variation curve was emulated for different operating conditions. The results demonstrate that the neuro-fuzzy controller (NFC) presented a significant increase in pumping system efficiency and a reduction in specific energy consumption of up to 79.7% when compared to the other controllers. Target pressures were kept close to the set-point values with low hydraulic transients and maintained satisfactory stability (error &amp;lt;8%) under severe situations of demand variation. It is concluded that the NFC presented superior results when compared with the other analyzed controllers.

On the Use of a Real-Time Control Approach for Urban Stormwater Management

The real-time control (RTC) system is a valid and cost-effective solution for urban stormwater management. This paper aims to evaluate the beneficial effect on urban flooding risk mitigation produced by applying RTC techniques to an urban drainage network by considering different control configuration scenarios. To achieve the aim, a distributed real-time system, validated in previous studies, was considered. This approach uses a smart moveable gates system, controlled by software agents, managed by a swarm intelligence algorithm. By running the different scenarios by a customized version of the Storm Water Management Model (SWMM), the findings obtained show a redistribution of conduits filling degrees, exploiting the whole system storage capacity, with a significant reduction of node flooding and total flood volume.

Ten-Month Evaluation of the Routine Application of Patient Moving Average for Real-Time Quality Control in a Hospital Setting.

In recent years there has been renewed interest in patient-based real-time quality control (PBRTQC) techniques. This interest has been stimulated by the availability of new optimization and validation methods. Only a limited amount of research has focused on investigating the true operational value of PBRTQC. Therefore, we have evaluated the performance and value of recently implemented patient moving average quality control (MA QC) procedures. The MA QC settings and protocols were as previously described (Clin Chem Lab Med 2019;57:1329-38) and included MA QCs for 10 chemistry and 6 hematological tests, all performed on duplicate analyzer systems. All MA QC alarms that occurred during the first 10 months of routine clinical application were investigated for assay-specific alarm rate and occurrence in time. Furthermore, the causes of these MA QC alarms were investigated, and alarm relevance was determined on the basis of total allowable bias (TBa) and error (TEa) derived from biological variations. During the 10-month period, 202 individual MA QC alarms occurred, resulting in an overall MA QC alarm rate of 0.030% and a frequency of 4.67 per week. Most alarms were triggered by sodium MA QC. Based on all available fully executed and documented MA QC alarm work-ups, MA QC detected errors that in 26.0% of the alarms exceeded the TBa and in 13.7% the TEa. In 9.2% of the alarms, MA QC alarming triggered instant (technical) corrections. Routine clinical application of MA QC is feasible with maintaining a manageable number of alarms and enabling detection of relevant analytical errors.

Real-Time River Flood Control under Historical and Future Climatic Conditions: Flanders Case Study

AbstractModel predictive control (MPC) has shown to be an efficient technique for real-time flood control. The evaluation of the control performance is, however, typically restricted to a limited s...

Real-time Inverse Model Estimation by a Recursive Least Squares Method for Disturbance Observer-based Control Systems: Balancing Control of a Single-wheel Robot

This article proposes a real-time identification and control technique for disturbance observer (DOB) - based control systems to improve the balancing control performance of a single-wheel robot. In a DOB-based control configuration, the inverse model of the system is obtained by inverting the identified forward model to extract the disturbance. However, a problem arises when the stability of the inverse model is not guaranteed. The Q filter design may solve the problem, but the process is time consuming. Therefore, we propose a stable inverse model identification technique with a minimal effort of designing Q filters in DOB schemes. The proposed scheme follows three steps: a recursive least square (RLS) method is used for updating the parameters of a second order model. Then the stability is checked. The last step is to make the model stabilized depending upon the stability through the all-pass-filter technique. After these steps, the transformed inverse model becomes stable and finally can be used for DOB control schemes. Experimental studies on balancing control of a single-wheel robot are conducted and their performances are compared to verify the proposal.

Flood Mitigation through Optimal Operation of a Multi-Reservoir System by Using Model Predictive Control: A Case Study in Myanmar

Managing a multi-reservoir system is complicated, due to conflicting interests among various objectives. This study proposes an optimization-based approach for the operations of a multi-reservoir system. An advanced real-time control technique, Model Predictive Control (MPC), is adopted to control a multi-reservoir system with two control objectives, i.e., flood mitigation and water conservation. The case study area is the Sittaung River basin in Myanmar, where the current reservoir operating rule needs to be improved for a more effective operation. A comparison between an MPC-based operation and the current operation is presented by using performance indicators. The result shows a reduction of the system’s vulnerability by 0.9 percent using MPC. Due to the physical constraint of the reservoirs, it is impossible to completely eliminate the flood risk at Taungoo City during high inflow events. However, the results indicate that the potential flood risk can be mitigated by improving the current operating rule.

Model uncertainty reduction for real-time flood control by means of a flexible data assimilation approach and reduced conceptual models

Recently, a combination of model predictive control and a reduced genetic algorithm (RGA-MPC) has shown to be an efficient control technique for real-time flood control, making use of fast conceptual river models. This technique was so far only tested under ideal circumstances of perfect model predictions. Prediction errors originating from hydrodynamic model mismatches, however, result in a deterioration of the real-time control performance. Therefore, this paper presents two extensions of the RGA-MPC technique. First, a new type of conceptual model is introduced to further increase the computational efficiency. This reduced conceptual model is specially tailored for real-time flood control applications by eliminating all unnecessary intermediate calculations to obtain the flood control objectives and by introducing a new transport element by means of flow matrices. Furthermore, the RGA-MPC technique is extended with a flexible data assimilation approach that analyzes the past observed errors and applies an appropriate error prediction scheme. The proposed approach largely compensates for the loss in control performance due to the hydrodynamic model uncertainty.