Real-time Control Algorithm Research Articles

Efficient Extraction of Electrochemical Impedance Spectra from Physical Models

Electrochemical impedance spectroscopy (EIS) is a highly valuable and widely practiced approach to assist the interpretation of electrochemical device performance. In the laboratory, the device (e.g., fuel cell or battery) is actuated with a small-amplitude harmonic signal (e.g., current) and the response (e.g., voltage or temperature) is measured. The complex impedance is represented in terms of amplitude and phase-shift differences between actuation and response as functions of actuation frequency. Most often, the measured impedance is interpreted with equivalent circuit models [1]. The present approach focuses on the use of physical models to derive and interpret the impedance spectra. Assuming that a transient physical model has been developed (e.g., protonic-ceramic fuel cell), the model could be exercised just as done in the laboratory with small-perturbation actuation [2]. This approach certainly works, but is computationally expensive, especially in the low-frequency ranges. Compared to the direct harmonic perturbation of the physical models, deriving state-space models from the physical models provides a much more efficient alternative to evaluate impedance spectra. State-space models take the form of two vector equations: dx/dt = Ax + Bu; y = Cx + Du. In these equations, x is the state vector, u is the actuation vector, and y is the vector of observables. A particular system is identified in terms of the four matrices A, B, C, and D. Once the state-space matrices are established, the complex impedance can be evaluated directly from the state-space matrices as Z(s;p) = C(sI – A)-1 B + D, where s=j, p is a vector of physical parameters, and I is the identity matrix. The complex impedance can be represented in various forms, such as in Nyquist plots. The present paper develops two alternative approaches to establish the space-state models. The first approach is based upon actuation with pseudo-random binary sequences (PRBS), seeking to identify locally linear state-space models [3-4]. The PRBS actuation is composed of small-amplitude step-change actuations of random durations, with the model-predicted responses being recorded. The relationships between the PRBS actuation and the predicted responses can be used to establish matrix parameters in a state-space model. As an alternative to PRBS identification of the state models, the physical model can be interpreted directly as a large-scale state-space model. In this case, the A, B, C, and D matrices can be evaluated by numerical differentiation of the physical model. Compared to the physical models that may have thousands of states (local composition, temperature, electrostatic potential, etc.), state-space models are typically reduced to only tens of states. In addition to evaluating the complex impedance, the reduced-order state-space models can play valuable roles in real-time model-predictive control algorithms.

Selection of an Efficient Linear State Estimator for Unified Real Time Dynamic State Estimation in Indian Smart Grid

Abstract As electricity demand is continuously increasing, the complexity and criticality of the power system infrastructure is increasing. After suffering from world’s largest power blackout on 30th and 31st July, 2012, the Indian power grids are now adopting the smart grid technologies to prevent such massive power failures. It initiated with the installation of PMUs on each strategically selected critical location in the entire grid under the Real Time Dynamic State measurements (URTDSM) scheme to attain the complete observability of the system. However, attainment of complete observability further demands selection of accurate and fast SE algorithm which is still a challenging task. The aim of this paper is to recommend the most efficient and accurate state estimator based on Linear State Estimation (LSE) algorithms for efficient real time monitoring and control of the grid, especially for Indian Power Grid. The performance test was conducted on two different SE algorithms, the Weighted Least Square (WLS) algorithm and recursive Kalman Filter algorithm. The test results are obtained on Indian 75-bus transmission system and validation is done by comparing the test results with the results obtained on standard IEEE-39 bus test system. Based on the performance comparison of the two algorithms, the selection of most suitable algorithm to be implemented in Indian power grid is achieved.

Rapid Simulation of 3D Liquid Sloshing in the Lunar Soft-Landing Spacecraft

Sloshing of liquid fuel has significant implications in the accurate control of the spacecraft. Rapid simulation of the force and moment exerted by liquid against the spacecraft is rather important for the dynamics and control of the spacecraft. Fast numerical simulation models, especially those capable of dealing with violent liquid sloshing in the lunar soft-landing spacecraft with fluid merging and splitting, are required for real-time control algorithms. For this purpose, a modified smoothed particle hydrodynamics method, namely, noninertial smoothed particle hydrodynamics (NI-SPH), is proposed. In this method, noninertial coordinate system is used to derive transient external excitations to liquid. Meanwhile, the linear and angular momentum theorems of the particle system are adopted to calculate the force and moment exerted by liquid against the spacecraft, which avoids the accuracy loss of the integration of pressures. Furthermore, parallelization based on OpenMP is used to speed up the simulation. To show the accuracy and efficiency, results from the NI-SPH method are compared with those obtained by traditional computational fluid dynamics software, for several 3D liquid sloshing cases.

A real-time adaptive look-ahead speed control algorithm for FDM-based additive manufacturing technology with Hbot kinematic system

PurposeThe purpose of this paper is to enhance the accuracy as well as efficiency of high-speed machining, avoid the speed fluctuation caused by acceleration/deceleration (ACC/DEC) and increase the smoothness of feedrate in continuous corners or curves machining. The Hbot kinematic system was analyzed and combined with fused deposition modeling-based (FDM) additive manufacturing (AM) technology. Then a real-time adaptive look-ahead speed control algorithm was proposed.Design/methodology/approachTo validate the performance of Hbot kinematic system and the proposed speed control algorithm, the positioning accuracy of Hbot and cross structure was compared. Also, the experimental verification was conducted among FDM based 3-D printer with cross structure as well as open source speed control algorithm (FDM with cross-OS), cross structure and the proposed speed control algorithm (FDM with cross-PS) and Hbot structure, as well as the proposed speed control algorithm (FDM with Hbot-PS), respectively.FindingsThe results indicate that the Hbot kinematic system leads to the high stability of positioning accuracy due to the small motion inertia. Furthermore, the experimental verification shows that the efficiency, printing precision and surface finish of models for FDM with Hbot-PS are obviously higher than that for FDM with cross-PS as well as FDM with cross-OS, while FDM with cross-OS shows the worst performance. The contribution of Hbot kinematic system and the proposed speed control algorithm to FDM based AM technology was validated by this work.Practical implicationsThe Hbot kinematic system and proposed speed control algorithm have the important implication of improving the accuracy of FDM machines, especially in the low-price range segment. Also, this work can help future system developers show a possible way of tackling the motion inertia problem.Originality/valueThe study of Hbot kinematic system and proposed algorithm are expected to advise the current research for improving the accuracy as well as the efficiency of FDM-based AM technology.

Residential Energy Storage Management With Bidirectional Energy Control

We consider the residential energy storage management system with integrated renewable generation, with the availability of bidirectional energy flow from and to the grid thorough buying and selling. We propose a real-time bidirectional energy control algorithm, aiming to minimize the net system cost, due to energy buying and selling and battery deterioration and inefficiency from storage activities, within a given time period, subject to the battery operational constraints and energy buying and selling constraints. We formulate the problem as a stochastic control optimization problem. We then modify and transform this difficult problem into one that enables us to develop the real-time energy control algorithm through Lyapunov optimization. Our developed algorithm is applicable to arbitrary and unknown statistics of renewable generation, load, and electricity prices. It provides a simple closed-form control solution only based on current system states with minimum complexity for real-time implementation. Furthermore, the solution structure reveals how the battery energy level and energy prices affect the decision on energy flow and storage. The proposed algorithm possesses a bounded performance guarantee to that of the optimal non-causal T-slot look-ahead control policy. Simulation shows the effectiveness of our proposed algorithm as compared with alternative real-time and non-causal algorithms, as well as the effect of selling-to-buying price ratio and battery inefficiency on the storage behavior and system cost.

Frequency regulation service provision in data center with computational flexibility

The rapid adoption of cloud storage and computing services led to unprecedented growth of data centers in the world. As bulk energy consumers, large-scale data centers in the U.S. rack up billions in electricity costs annually. Fortunately, the operational flexibility of data centers can be leveraged to provide valuable frequency regulation services in smart grids to mitigate the indeterminacy of the renewable generation resources. Specifically, this paper aims to leverage computational flexibility provided by servers, such as dynamic voltage frequency scaling and dummy loads. This paper develops a comprehensive framework for data center’s frequency regulation service provision in both hour-ahead market and real-time operations. A risk constrained hour-ahead bidding strategy along with a real-time data center power consumption control algorithm are developed to minimize electricity bills and the total response time of the requests. The introduction of dummy load, realistic bi-linear server power consumption model, and probabilistic forecast of electricity and frequency regulation service prices enable the data center to accurately follow frequency regulation signals, while reducing the financial risks associated with electricity market participation. The simulation results show that the proposed frequency regulation provision framework results not only in significant cost reduction for data centers, but also limits degradation in quality of service. Meanwhile, the stability and reliability of a power grid will be improved by the frequency regulation service provision.

Dynamic traffic demand uncertainty prediction using radio‐frequency identification data and link volume data

Dynamic traffic demand is crucial for developing effective strategies and algorithms for real-time traffic management and control. The uncertainty of traffic demand provides additional information while its prediction is very complicated and is inadequately investigated in the existing literature. Recently, radio-frequency identification (RFID) technology has been deployed to monitor the condition of traffic. In this study, the authors propose a modelling system to predict dynamic traffic demand uncertainty using the RFID data and the link traffic volume. The modelling system includes an optimisation model using the RFID data to calculate the historical traffic demand in terms of origin–destination matrix, a state-space model solved by Kalman filter to predict the traffic demand, and a GARCH model to capture the conditional variance of the predicted traffic demand. Performance measures are applied to evaluate prediction accuracy. The case study in Nanjing, China shows that the modelling system has the desirable performance to successfully predict uncertainties of dynamic traffic demand.

Improved reliability of stormwater detention basin performance through water quality data-informed real-time control

Stormwater detentions basins are designed to capture stormwater to reduce and delay peak flows and to improve water quality. A novel technology proposed to improve basin performance is real-time, active control of the basin outflow, in so-called “smart” stormwater systems. Existing studies demonstrate the performance of active controls that respond in real-time to basin water level, detention time, and rainfall forecast for one or a small number of rainfall events. We hypothesize that the performance of these active controls can be improved by incorporating real-time water quality data into the control algorithm. In addition, we hypothesize that active control performance depends on hydrologic variability, specifically the frequency and intensity of runoff inputs. In this paper, we test these hypotheses using a numerical modeling framework for systems-level reliability analysis of active and passive stormwater basin outflow control using a Monte Carlo method. The analysis is performed using the urban hydrology model EPA-SWMM driven by stochastic rainfall time-series generated from the Modified Bartlett-Lewis Rectangular Pulses Model. Water quality-informed real-time active control algorithms are developed, tested, and shown to display an improvement over traditional, passive (no control) systems and other storage-based active controls for water and pollutant capture. Seasonal and duration curve analysis showed that water level- and water quality- informed control performance varied for different storm return periods and this variability could partly be attributed to the fraction of time the valve is closed. In addition, control performance was sensitive to rainfall variability, generally decreasing as storms become less frequent and more intense. Therefore, control system performance may depend on seasonal and longer time-scale variability in climate and rainfall-runoff processes. We anticipate this study to be a starting point to incorporate theories of reliability to assess detention basin and conveyance network performance under more complex real-time control algorithms and failure modes.

Efficient Real-Time Control Design for Automatic Train Regulation of Metro Loop Lines

This paper systematically investigates the efficient real-time control design for automatic train regulation (ATR) of metro loop lines subjected to frequent minor disruptions. To describe the dynamic evolution of the trains periodically operating on the loop line, a train traffic model related to the departure time is proposed based on a state-space equation, where the number of the station increases from circle to circle. Under the frequent minor disruptions, a dynamic optimal control model is developed to determine the ATR strategy to improve the punctuality and the regularity of the metro operation with the safety and control constraints. To solve the formulated optimal control model with the updated information, a real-time control algorithm based on a model predictive control approach is designed, which splits the original optimization problem into a set of convex quadratic programming problems, which can be numerically calculated efficiently and satisfy the real-time control requirement. Numerical examples are given to illustrate the effectiveness of the proposed method.

Design and simulation of the snowflake divertor control for NSTX–U

This paper presents the development of a physics-based multiple-input-multiple-output algorithm for real-time feedback control of snowflake divertor (SFD) configurations on the National Spherical Torus eXperiment Upgrade (NSTX–U). A model of the SFD configuration response to applied voltages on the divertor control coils is first derived and then used, in conjunction with multivariable control synthesis techniques, to design an optimal state feedback controller for the configuration. To demonstrate the capabilities of the controller, a nonlinear simulator for axisymmetric shape control was developed for NSTX–U which simultaneously evolves the currents in poloidal field coils based upon a set of feedback-computed voltage commands, calculates the induced currents in passive conducting structures, and updates the plasma equilibrium by solving the free-boundary Grad–Shafranov problem. Closed-loop simulations demonstrate that the algorithm enables controlled operations in a variety of SFD configurations and provides capabilities for accurate tracking of time-dependent target trajectories for the divertor geometry. In particular, simulation results suggest that a time-varying controller which can properly account for the evolving SFD dynamical response is not only desirable but necessary for achieving acceptable control performance. The algorithm presented in this paper has been implemented in the NSTX–U Plasma Control System in preparation for future control and divertor physics experiments.

A physics-based distributed-parameter equivalent circuit model for lithium-ion batteries

A physics-based equivalent circuit model (ECM) is derived by applying finite volume method to a pseudo-two-dimensional (P2D) model of lithium-ion (Li-ion) batteries. Only standard passive components are used to construct the equivalent circuit, which reflects the fact that a Li-ion battery is an energy storage device. Voltages across and currents through the circuit elements in the ECM are identified with the respective internal electrochemical processes in the battery, thus allowing the parametric values of circuit elements to be expressed as functions of the Li-ion concentrations and temperature. Variations in the parametric values across the thickness of the battery leads to a distributed-parameter ECM amenable for wide range of applications. Furthermore, in contrast to existing reduced-order models of Li-ion battery which are described by differential algebraic equations, the ECM is governed by ordinary different equations wherein all the circuit components are expressed as explicit functions of the state and input variables. Hence, the developed model allows solution to be found directly using matrix algebra, resulting in rapid simulation study suitable for the development of computationally-efficient real-time battery control algorithm. Results of simulation based on the developed distributed-parameter ECM show close agreement with those obtained from a partial differential equation based P2D model under wide range of applied current rate, but at a much reduced computational burden.

Intelligent Method of Computer-Aided Design and Checking of Real-Time Spacecraft Control Algorithms

The paper is devoted to description of the method for computer aided design of consistent real-time spacecraft onboard control algorithms. This kind of control algorithms should organize synchronized cooperation of different onboard apparatus and functional software for successful completion of space exploration mission. We introduce the mathematical models for onboard resources, equipment and apparatus, and for real-time control algorithm to be implemented by onboard real-time software. The important feature of the proposed model is considering that each onboard apparatus consumes some onboard resources, thus consistent control algorithm must not violate limits of these resources. There is discussion devoted to construction of effective and consistent real-time control algorithm which guarantees completion of the mission in case of limited accessible onboard energy, computer power, etc. We also present the specially developed CAD/CASE tool allowing automated checking of existing algorithms whether they violate the resource constraints as well as design of new algorithms from scratch.

On course to smart large-diameter pipe production

Abstract In the production of longitudinally submerged-arc welded (LSAW) large-diameter steel pipes – covering diameters in the range of 18 to 56 inch – a complex sequence of manufacturing processes has to be planned and optimized to match strict market requirements. Thus, two approaches are essential. On the one hand process optimizations have to be done with regard to the process interrelations along the whole process chain. On the other hand process deviations based on material variations have to be managed with reference to each individual steel plate. In this context, a new concept for process chain control is presented comprising an up-to-date calculation model for offline and real-time optimization of the LSAW pipe production process chain – including measuring and feedback-control systems. Core processes are controlled in consideration of real-time information coming from upstream and downstream processes. Due to intelligent algorithms, interrelations between subsequent forming processes (e.g. plate edge crimping, pipe forming, pipe closing, tack welding, pipe expanding) are processed by a higher-level process chain model. As a practical example, the efficiency of the real-time feedback control algorithm is shown with focus on the pipe forming press. Influencing factors (e.g. variations in yield stress and wall thickness, positioning accuracy) are discussed on the basis of extensive simulations. Corresponding model algorithms are presented and compared with practical results. The presented concept aims at a reduced start-up time for producing new pipe dimensions and at tighter tolerances for finished pipe dimensions. Furthermore, it allows for traceable real-time optimization of current production in existing LSAW pipe mills.

Alternating Current Electric Generator Machine Inverters in a Parallel Power Sharing Connection

This paper covers the theoretical background as well as chosen selected experimental results obtained for two AC current generators connected to voltage source inverters working in parallel. Inverters were connected in parallel via their intermediate direct current circuits by means of auctioneering diodes which select higher voltage source, and then directly to a DC power network. The selected real-time control algorithm for a digital signal processor and a field programmable gate array was used to achieve the proper excitation of two different types of alternators: a squirrel-cage generator denoted as and a self-excited synchronous generator. Another task for the excitation system was to maintain the proper level of direct current link voltage and stable power distribution between both generators rotating at changing speeds. The windings of alternators were fed with the use of so-called machine side inverters. The asynchronous generator was a voltage source inverter driven with the use of a sensorless multiscalar algorithm based on the current machine model, whereas the self-excited synchronous generator worked on the field oriented control principle. To prove the robustness of the chosen algorithm, different types of load were applied while during the parallel work of the generators and inverters.

A Precision Irrigation Model Using Hybrid Automata

HighlightsControl theory applied to precision irrigation reduces water consumption and improves crop productivity.A model-based control using hybrid automata is proposed to describe soil, crop, and weather dynamics.The model was developed and validated with experimental data from a grass irrigation process.The proposed model can be used to develop robust and real-time control algorithms to improve irrigation systems. Abstract. Closed-loop control for precision irrigation represents an effective method to provide water savings in this resource-intensive activity. Typically, implementation of these systems uses an on-off control approach with soil moisture as the feedback variable. In these cases, no process modeling is required due to the simplicity of the implemented control algorithm. However, the amount of water consumed by irrigation globally presents an interesting application area for the control discipline. Therefore, to obtain greater water savings and to improve crop productivity through control theory, an irrigation model is required that integrates the three main elements in the irrigation process: soil, crop, and weather. This article proposes a hybrid automata model for a closed-loop irrigation system applied to a grass-covered area. The main measured variables used to represent the process dynamics are soil moisture, vegetation index, and evapotranspiration. The hybrid automata approach allows a complex system to be modeled as a finite state machine in which there is a specific linear model for each state. When certain conditions are met, the system transitions from one state to another, and the new behavior is represented by a different linear model. To develop the proposed model, experimental data were obtained from an irrigation process, the data were analyzed to produce a model using the hybrid automata approach, and finally the model was validated with a new set of measured data. Keywords: Closed-loop irrigation, Evapotranspiration, Hybrid automata, NDVI, Precision irrigation modeling.

Development of a Multi-Architecture and Multi-Application Hybrid Vehicle Design and Management Tool

Hybrid electric vehicles (HEVs) are very promising sustainable mobility solutions. Series, parallel and series-parallel (SP) seem to be three most promising architectures among the multitude of hybrid architectures, and it is possible to find them in a multi-applications such as the motorcycles, family-cars, hybrid city busses and sport cars. It is import to have a well configured model in order to develop the different control strategies (CsTs) for each application. Therefore, a multi-architecture/multi-application (MAMA) approach capable of identifying the most energy efficient hybrid architecture considering both the dimensions of key components: electric motor (EM), battery, internal combustion engine (ICE) and the optimal control is presented. Basis of the model is the energetic macroscopic representation (EMR), which has been combined with object oriented programming (OOP) in order to enhance its modularity and reuse capabilities. The obtained results show, that different hybrid architectures are most adapted for different applications. Moreover, the robustness of the results using real time control algorithms are studied, showing that CsT matters. The obtained results contribute to simplify and harmonize the design of hybrid solutions for multiple applications.

Internet of things control mechanism based on controlled object information perception and multimode image signal processing

With the scale increase of the internet of things (IOT) control system, the difficulty of information acquisition and image processing for controlled objects increases gradually. To solve the above problems, we proposed a stable and robust IOT control system based on the controlled object information sensing algorithm and multi-mode image signal processing. Firstly, a perceptual real-time control algorithm for the controlled objects information is indicated for controlling, monitoring and maintaining the moving objects at anywhere and anytime. Secondly, in the target location 3D area, the pixels of the controlled object are collected. These pixels are combined with the data characteristics of controlled objects to accurately describe the controlled targets. The multi-mode image signal of the controlled object was computed and processed in the multi-dimensional localization region of the image in real time. Finally, the data acquisition accuracy, signal processing accuracy and system execution efficiency of the proposed scheme were analyzed by mathematical analysis and field experiments. The results show that the proposed algorithm and the designed control mechanism of the IOT have good real-time performance, accuracy and robustness.

MONITORING OF PRODUCTION LINE SUPPLY SYSTEMS WITH DRONES

There is extensive literature about drones in a logistics context, and numerous applications have been implemented, but there is one unique use case that hasn’t gotten momentum so far, yet offers significant potential. Our paper focuses on this opportunity, which is deploying drones to support the monitoring of intralogistical processes, i.e. Multi Moment Analysis (MMA). This way the observation and measurement of manufacturing processes could be automated to a large extent, making it faster, more reliable and cheaper, therefore offering benefits to both the logistics company performing the MMA and the customer. Our paper describes the architecture of a system needed to perform MMAs using drones, focusing on two key components: the indoor localization sub-system and a real-time closed-loop control algorithm, that enables the drone to track the monitored object. In order to test our algorithm, we built a simulator in MS Excel, where a drone is tracking an object moving along a straight line and a curve. The results of our experiments indicate that the drone was able to stay well within 1 meter of the object, despite the introduced uncertainty in their motion, therefore our algorithm appears to be validated and ready to be tested in a physical environment.

An Untethered Ankle Exoskeleton Improves Walking Economy in a Pilot Study of Individuals With Cerebral Palsy.

The high energy cost of walking in individuals with cerebral palsy (CP) contributes significantly to reduced mobility and quality of life. The purpose of this paper was to develop and clinically evaluate an untethered ankle exoskeleton with the ability to reduce the metabolic cost of walking in children and young adults with gait pathology from CP. We designed a battery-powered device consisting of an actuator-and-control module worn above the waist with a Bowden cable transmission used to provide torque to pulleys aligned with the ankle. Special consideration was made to minimize adding mass to the body, particularly distal portions of the lower-extremity. The exoskeleton provided plantar-flexor assistance during the mid-to-late stance phase, controlled using a real-time control algorithm and embedded sensors. We conducted a device feasibility and a pilot clinical evaluation study with five individuals with CP ages five through thirty years old. Participants completed an average of 130 min of exoskeleton-assisted walking practice. We observed a 19±5% improvement in the metabolic cost of transport (p = 0.011) during walking with untethered exoskeleton assistance compared to how participants walked normally. These preliminary findings support the future investigation of powered ankle assistance for improving mobility in this patient population.

Demonstration of Subretinal Injection Using Common-Path Swept Source OCT Guided Microinjector

In this paper, we present the development of a handheld common-path swept source optical coherence tomography (CP-SSOCT) guided microinjector system and demonstrated its utility by precisely injecting fluorescein dye in the subretinal layer of ex vivo bovine eyes. The system enables precise subretinal injection with micron-level injection depth control. This was achieved by using a high-resolution CP-SSOCT distal sensor and signal processing using a graphics-processing unit (GPU), which made a real-time smart motion control algorithm possible. The microinjector performance was first evaluated using a gelatin phantom in terms of its ability for dynamic depth targeting and injection depth. This is followed by using an ex vivo bovine eye model to perform multiple consecutive subretinal injections of fluorescein dye. The results validated the OCT guided injector’s ability to precisely guide and lock in the needle tip to the target depth during injection. The ex vivo evaluation tests demonstrate that an OCT-guided injector can consistently guide the injecting needle to the desired depth and is able to maintain the position with 9.38 μm average root mean square error during the injections.