Real-time Control Framework Research Articles

FLORIDyn - A dynamic and flexible framework for real-time wind farm control

This paper presents a new framework of the FLOw Redirection and Induction Dynamics (FLORIDyn) model. It is able to dynamically simulate the wake behaviour in wind farms under heterogeneous and changing environmental conditions at a low computational cost. The novelty of this work is the improved segregation of wake dynamics and wake influence: the framework creates Observation Points (OPs) at each turbine, which propagate wind field states and turbine states downstream and follow the wind direction of the free stream velocity. These observation points cover the dynamic aspects of the simulation. The OPs, along with the stored states, are now used to derive so-called Temporary Wind Farms (TWF), which approximate the effective intra-farm wind conditions at a given location. Within these TWF, the flow conditions are homogeneous and steady state. This way, arbitrary wake models can be used to calculate the farm influence on the location. The FLORIDyn framework also provides interfaces to flow field estimators, which is tested with an effective wind speed estimator. A nine turbine case is used to highlight the quality and performance of the simulation result. Compared to its predecessor, the new FLORIDyn framework decreases the computational cost by one to two orders of magnitude, which makes it a promising candidate for real-time model predictive dynamic wind farm control.

A Blockchain-Based IoT Framework for Oil Field Remote Monitoring and Control

The oil and gas industry involves a high level of operational expenditure and often faces high risks of asset safety and operational failures. It has never been so important to monitor and control the oil field operations remotely in real-time to ensure safety and efficiency. The traditional monitoring and control systems for oil field operations are typically centralized, prone to failure, and lack efficiency. Blockchain technology mitigates the centralization problem by creating a decentralized, immutable and transparent control environment for automatic monitoring and control of industrial operations. In this study, we propose a blockchain-based IoT framework for real-time monitoring and control to increase oil field operation and asset efficiency and safety. We present the key components of the framework, including the system architecture, operation flows, algorithms, and smart contracts. As a proof-of-concept modeling, a smart contract is developed and validated on a blockchain test platform. A comparative analysis shows the advantages of using blockchain technology and smart contract to provide trustworthy and automatic monitoring and control for oil field operations.

Application of a modified iterative learning control algorithm for superconducting radio-frequency cavities

Transient beam loading, which causes cavity gradient fluctuation, is becoming a major concern for the stable operation of the high current superconducting radio-frequency (SRF) accelerators. Iterative learning control (ILC) is an effective algorithm aiming to improve systems operated in repetitive mode. This ILC technique was successfully introduced to the low-level radio-frequency (LLRF) control in accelerators to compensate for the field fluctuation caused by repetitively pulsed beam. The modern LLRF system prefers to use the FPGA-based hardware platform to realize a real-time control framework. However, considering the algorithm complexity and the hardware cost, the ILC algorithm is usually implemented outside FPGA. This practice would decrease the real-time ability of the control system. In this paper, we present a modified ILC algorithm that can be implemented inside FPGA. The key idea of our method is to simplify the beam profile using a rectangular pulse. The method was demonstrated in the SRF cavities at Chinese Accelerator driven system Front-end demo SRF linac (CAFe). The experimental results in the CAFe beam-commissioning confirmed that the beam-induced gradient fluctuation is successfully suppressed.

DRL-TP3: A learning and control framework for signalized intersections with mixed connected automated traffic

The emerging connected and automated vehicle (CAV) technologies offer new opportunities for urban signalized intersection management. Through wireless communication and advanced sensing capabilities, CAVs can detect the surrounding traffic environment and share real-time vehicular information with each other and the infrastructure, and individual trajectories of CAVs can be precisely controlled. This paper proposes a real-time learning and control framework for signalized intersection management, which includes both signal optimization and CAV trajectory control. The proposed framework integrates perception, prediction, planning, and optimization components and aims at improving efficiency mixed connected automated traffic in terms of traffic throughput and delay. This framework applies the Long Short Term Memory (LSTM) networks to implicitly learn traffic patterns and driver behavior and then estimate and predict the microscopic traffic conditions that are only partially observable. Then it utilizes deep reinforcement learning (DRL) to solve signal optimization problems by learning from the dynamic interactions between vehicles and the traffic environment. Under the proposed framework, the vehicular trajectories of CAVs can be controlled to maximize the utilization of the green time and reduce the start-up lost time by using a highly efficient trajectory planning algorithm. The CAV platooning operation, in coordination with traffic signals, is also implemented such that CAVs can pass the intersection efficiently. Simulations are performed at a signalized intersection a signalized intersection with multi-lane approaches, high traffic demand, and standard ring-barrier control, and results show that the proposed DRL-TP3 framework can significantly improve the throughput and reduce the average delay across different CAV market penetration rates (MPRs). We also investigate the impacts of different sensor capabilities of unobservable vehicle estimation and implementation of a lane change prohibition zone under the DRL-TP3 framework.

Rhythmic Control of Automated Traffic—Part II: Grid Network Rhythm and Online Routing

Connected and automated vehicle (CAV) technology is providing urban transportation managers tremendous opportunities for better operation of urban mobility systems. However, there are significant challenges in real-time implementation as the computational time of the corresponding operations optimization model increases exponentially with increasing vehicle numbers. Following the companion paper (Chen et al. 2021), which proposes a novel automated traffic control scheme for isolated intersections, this study proposes a network-level, real-time traffic control framework for CAVs on grid networks. The proposed framework integrates a rhythmic control method with an online routing algorithm to realize collision-free control of all CAVs on a network and achieve superior performance in average vehicle delay, network traffic throughput, and computational scalability. Specifically, we construct a preset network rhythm that all CAVs can follow to move on the network and avoid collisions at all intersections. Based on the network rhythm, we then formulate online routing for the CAVs as a mixed integer linear program, which optimizes the entry times of CAVs at all entrances of the network and their time–space routings in real time. We provide a sufficient condition that the linear programming relaxation of the online routing model yields an optimal integer solution. Extensive numerical tests are conducted to show the performance of the proposed operations management framework under various scenarios. It is illustrated that the framework is capable of achieving negligible delays and increased network throughput. Furthermore, the computational time results are also promising. The CPU time for solving a collision-free control optimization problem with 2,000 vehicles is only 0.3 second on an ordinary personal computer.

RTCF: A framework for seamless and modular real-time control with ROS

<h2>Abstract</h2> Owing to the steady progress in the field of Linux kernel development, high-performance control applications are no longer a rarity on general-purpose computing platforms. However, many real-time control libraries lack important properties such as modularity, effortless integration, and encapsulation. These are key design features of the popular Robot Operating System (ROS) that is, however, not real-time capable. We aim to solve this issue by introducing the Real-Time Control Framework (RTCF), which offers high modularity, ROS-related concepts leading to seamless interoperability with ROS, and high performance. To demonstrate the capabilities of the RTCF, we provide several examples and exemplary performance data.

Sensor based real-time information for monitoring and control of a manufacturing process

This paper demonstrates a real-time process monitoring and control framework for the implementation of Industry 4.0 in manufacturing. For a case study, friction stir welding (FSW) has been selected owing to its extensive usage of welding in various manufacturing sectors. Force data has been collected online during the welding and sent to a cloud server enabling telewelding. It is processed through several signal processing and machine learning (ML) techniques for assessment of the weld quality. A control system thereafter controls the weld quality for avoiding the occurrence of weld-defect in real-time. Two ML models have been built, one for assessment of the weld quality, and the other for forecasting the controlled parameters for avoiding defect occurrence. For manufacturing sectors involved in mass production, this technique will be useful to keep a track of the process in real-time and avoid rejection of material in case of occurrence of an anomaly in the process. It is always beneficial for the manufacturing industry to track the quality of a product in real-time, and control the rejection rate, both of which have been successfully shown in this paper.

Probabilistic decentralised control and message passing framework for future grid

In this paper, we propose a unified probabilistic decentralised control and message passing framework for real time control of the electrical grid which enables the development of the future smart grid. The key elements of the proposed framework are the design of local randomised controllers and probabilistic message passing methodology which enables the coordination between the designed local controllers to account for optimisation considerations on the system operation. Within the proposed framework, the electric grid is decomposed into a number of control areas. Additionally, since the frequency is the ubiquitous grid state variable, representing the balancing of active power generation and consumption, the proposed framework is demonstrated based on local Load Frequency Control (LFC). Simulation studies involving a six-area power system and three interconnection schemes are carried out to illustrate the applicability and effectiveness of the proposed approach.

Real-Time Control of Battery Energy Storage Systems to Provide Ancillary Services Considering Voltage-Dependent Capability of DC-AC Converters

Frequency response and voltage support are vital ancillary services for power grids. In this paper, we design and experimentally validate a real-time control framework for battery energy storage systems (BESSs) to provide ancillary services to power grids. The objective of the control system is to utilize the full capability of the BESSs to provide ancillary services. We take the voltage-dependent capability curve of the DC-AC converter and the security requirements of BESSs as constraints of the control system. The initial power set-points are obtained based on the droop control approach. To guarantee the feasibility of the power set-points with respect to both the converter capability and BESS security constraints, the final power set-points calculation is formulated as a nonconvex optimization problem. A convex and computationally efficient reformulation of the original control problem is then proposed. We prove that the proposed convex optimization gives the global optimal solution to the original nonconvex problem. We improve the computational performance of this algorithm by discretizing the feasible region of the optimization model. We achieve a 100 ms update time of the controller setpoint computation in the experimental validation of the utility-scale 720 kVA/560 kWh BESS on the EPFL campus.

On Bayesian active vibration control of structures subjected to moving inertial loads

This study introduces a novel Bayesian framework for online and real-time vibration control of beam type structures, which represent a comprehensive control system associated with input-state algorithms. Control design systems typically require knowledge of system states, which in structures are displacements and velocities at some degrees of freedom. Currently, full-field measurements of displacements and velocities in large structural systems are not feasible. Also, properties of the moving inertial loads as key parameters in control designs are assumed known; however, in practice, measuring their characteristics is a challenging issue. As a remedy, an observer is required to estimate quantities of interest needed in control algorithms via indirect measurement of structural response. To estimate the entire state of the system in the absence of input information, often the input needs to be estimated together with the unknown states. Three state of the art methods for estimation of input and states of a linear state-space system, namely augmented Kalman filter (AKF), dual Kalman filter (DKF), and joint input state estimation algorithm (JIS), were adopted to assess controller performance in the absence of direct displacement measurements in comparison to conventional Kalman filter (KF). The linear quadratic Gaussian (LQG) framework, in association with these three filters, presents a novel control algorithm. Various sensor networks featuring pure accelerations and a combination of strain gauges and accelerometers with different noise levels were considered. Extensive numerical examples and comparison with the passive control system proved the viability of input-state estimation as an integral part of active control algorithms in structural systems subjected to moving inertial loads with different speeds.

Bridging the gap between optimal trajectory planning and safety-critical control with applications to autonomous vehicles

We address the problem of optimizing the performance of a dynamic system while satisfying hard safety constraints at all times. Implementing an optimal control solution is limited by the computational cost required to derive it in real time, especially when constraints become active, as well as the need to rely on simple linear dynamics, simple objective functions, and ignoring noise. The recently proposed Control Barrier Function (CBF) method may be used for safety-critical control at the expense of sub-optimal performance. In this paper, we develop a real-time control framework that combines optimal trajectories generated through optimal control with the computationally efficient CBF method providing safety guarantees. We use Hamiltonian analysis to obtain a tractable optimal solution for a linear or linearized system, then employ High Order CBFs (HOCBFs) and Control Lyapunov Functions (CLFs) to account for constraints with arbitrary relative degrees and to track the optimal state, respectively. We further show how to deal with noise in arbitrary relative degree systems. The proposed framework is then applied to the optimal traffic merging problem for Connected and Automated Vehicles (CAVs) where the objective is to jointly minimize the travel time and energy consumption of each CAV subject to speed, acceleration, and speed-dependent safety constraints. In addition, when considering more complex objective functions, nonlinear dynamics and passenger comfort requirements for which analytical optimal control solutions are unavailable, we adapt the HOCBF method to such problems. Simulation examples are included to compare the performance of the proposed framework to optimal solutions (when available) and to a baseline provided by human-driven vehicles with results showing significant improvements in all metrics.

Real-Time Integrated Power and Thermal Management of Connected HEVs Based on Hierarchical Model Predictive Control

Cabin heating requirement and engine efficiency degradation in cold weather lead to considerable increase in fuel consumption of hybrid electric vehicles (HEVs). This article focuses on the development of a real-time control framework for integrated power and thermal management (i-PTM) of a connected HEV through incorporating vehicle speed preview informed by traffic connectivity. To evaluate the ceiling of the fuel economy improvement via i-PTM, forward dynamic programming (FDP) is adopted as a benchmark study to find the global optimization solution and then implemented into a centralized model predictive controller (CMPC). Regarding the slow response associated with the coupled electric-thermal dynamics in the HEV, a two-layer hierarchical MPC (HMPC) framework is developed, which exploits vehicle speed preview predictions over short and long prediction horizons to optimize fuel consumption while satisfying power and cabin heating demand. The simulation results show that the proposed HMPC provides near-optimal solutions in reference to the benchmark while reduces up to 79% computation burden compared with CMPC. Additionally, compared to a baseline power management (PM) controller, up to 5.46% improvement on fuel economy can be achieved by HMPC for congested driving scenarios. Finally, the real-time feasibility of the HMPC is assessed in a dSPACE rapid prototyping system.

Predictive modeling of thermal parameters inside the raised floor plenum data center using Artificial Neural Networks

Data centers are large facilities housing numerous IT equipment and supporting infrastructure. Frequent variations in IT load, continuous removal/addition/replacement of IT equipment for business requirement, cooling equipment, air supply settings, design layout, etc. make Data centers dynamic. Such complexities lead to overcooling and increased energy consumption. To reduce the energy consumption of the data center, a real-time control framework based on various thermal parameters inside the data center is imperative. Accurate prediction of various variables affecting the thermal behavior of the data center, especially for the small-time horizon, using computational fluid dynamics (CFD) simulations requires a large number of computational resources and physical time, making them unfeasible for real-time control of the data centers. Data-driven modeling especially, the Artificial Neural Networks (ANN) can be potentially helpful in such cases. This study aims to examine the ANN-based model with Multi-Layer Perceptron (MLP) to predict thermal variables such as rack air temperature inside data centers. The ANN-based models for the rack and facility-level system were trained and validated on the experiments and validated CFD data. The optimum delay for each case was found using cross-correlation between the input and output parameters of the ANN. The response of the multi-input multi-output ANN model was validated using R-value and mean square error (MSE). R-value for all the cases was approximately 0.99. This study recommends the use of ANN models for fast and accurate prediction of thermal parameters for real-time energy-efficient control of the data center system.

MARTe2 and MDSplus integration for a comprehensive fast control and data acquisition system

Abstract MARTe is a framework for real-time control that has been used in several fusion experiments. Recently, a new version named MARTe2 has been developed adhering to software quality standards. MDSplus is a data system widely adopted in the fusion community. MDSplus provides fast data acquisition and access to pulse files and is intended to provide a complete interface both for the configuration of the experiment and the experimental results. MDSplus and MARTe2 are already integrated via a set of components that are able to store a data stream originated in real-time in the pulse file and get experiment set-up information. A further integration is proposed here, that is, using the Device abstraction provided by MDSplus to specify the components involved in the data acquisition process and MDSplus expressions to specify data relationships, in order to describe also the real-time components and the associated data flow. Following this approach, the whole real-time configuration will be described exactly as the rest of the other non real-time data acquisition components and the corresponding MARTe2 configuration will be generated on the fly, integrating all the required consistency checks.

An encoder–decoder LSTM-based EMPC framework applied to a building HVAC system

Numerous studies have demonstrated the benefit of economic model predictive control (EMPC) applied to building heating, ventilation, and air conditioning (HVAC) systems. However, the construction and training of predictive models for building HVAC systems are widely recognized as a key technological barrier preventing large-scale adoption of EMPC for buildings. In this work, an encoder–decoder long short-term memory-based EMPC framework is developed. The key advantage of the approach is that a model may be automatically generated from a list of inputs and outputs. From the definition of inputs and outputs, the constructed model may be trained and automatically embedded into the EMPC framework for real-time estimation and control. The overall end-to-end EMPC framework from model training to on-line estimation and control are described. To this end, the encoder–decoder model provides a natural framework for state estimation (encoder), which is required to provide an initial condition for the predictive model of EMPC (decoder). Closed-loop simulations using EnergyPlus are performed to demonstrate the approach. The simulated closed-loop system consists of a building zone from a multi-zone building, which is served by an air handling unit-variable air volume HVAC system. For the HVAC example considered, the trained encoder–decoder model can predict the indoor air temperature and HVAC sensible cooling rate of a building zone over a two-day horizon with high accuracy. Considering a time-of-use electric rate structure, the EMPC, which manipulates the zone temperature setpoint, can reduce the HVAC power consumption cost relative to keeping the zone temperature setpoint at its maximum value (i.e., minimum energy approach).

Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation

We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.

Ikaros: A framework for controlling robots with system-level brain models

Ikaros is an open framework for system-level brain modeling and real-time robot control. Version 2 of the system includes a range of computational components that implements various algorithms and methods ranging from models of neural circuits to control systems and hardware interfaces for robot. Ikaros supports the design and implementation of large-scale computation models using a flow programming paradigm. Version 2 includes a number of new features that support complex networks of hierarchically arranged components as well as a web-based interactive editor. More than 100 persons have contributed to the code base and over 100 scientific publications report on work that has used Ikaros for simulations or robot control.

Real-Time Optimal Lithium-Ion Battery Charging Based on Explicit Model Predictive Control

The rapidly growing use of lithium-ion batteries across various industries highlights the pressing issue of optimal charging control, as charging plays a crucial role in the health, safety and life of batteries. The literature increasingly adopts model predictive control (MPC) to address this issue, taking advantage of its capability of performing optimization under constraints. However, the computationally complex online constrained optimization intrinsic to MPC often hinders real-time implementation. This paper is thus proposed to develop a framework for real-time charging control based on explicit MPC (eMPC), exploiting its advantage in characterizing an explicit solution to an MPC problem, to enable real-time charging control. The study begins with the formulation of MPC charging based on a nonlinear equivalent circuit model. Then, multi-segment linearization is conducted to the original model, and applying the eMPC design to the obtained linear models leads to a charging control algorithm. The proposed algorithm shifts the constrained optimization to offline by precomputing explicit solutions to the charging problem and expressing the charging law as piecewise affine functions. This drastically reduces not only the online computational costs in the control run but also the difficulty of coding. Extensive numerical simulation and experimental results verify the effectiveness of the proposed eMPC charging control framework and algorithm. The research results can potentially meet the needs for real-time battery management running on embedded hardware.

Adaptive sliding mode control of robot based on fuzzy neural network

The robot is a very complex multi-input multi-output nonlinear system. Due to the inaccuracy of measurement and modeling, coupled with changes in load and the effects of external disturbances, it is virtually impossible to obtain a complete kinetic model. The strong robustness of sliding mode variable structure control makes it particularly suitable for solving the trajectory tracking problem of robots. In this paper, a better real-time adaptive control framework is designed to realize the robust target tracking of mobile robots. Based on the TSK neural network, an efficient control framework combining neural network controller and compensation controller is proposed. Based on the new framework research and improvement of neural network controller, considering the factors of emotional influence decision-making, the existing brain emotional learning neural network is improved and researched, and the brain-emotion learning neural network with radial basis function is proposed. The simulation results show that the trajectory tracking ability, anti-disturbance ability and robustness of the mobile robot are improved to some extent, which verifies the feasibility and efficiency of the proposed control method.

Sustainable Traffic Management in an Urban Area: An Integrated Framework for Real-Time Traffic Control and Route Guidance Design

This paper focuses on the presentation of an integrated framework based on two advanced strategies, aimed at mitigating the effect of traffic congestion in terms of performance and environmental impact. In particular, the paper investigates the “operational benefits” that can be derived from the combination of traffic control (TC) and route guidance (RG) strategies. The framework is based on two modules and integrates a within-day traffic control method and a day-to-day behavioral route choice model. The former module consists of an enhanced traffic control model that can be applied to design traffic signal decision variables, suitable for real-time optimization. The latter designs the information consistently with predictive user reactions to the information itself. The proposed framework is implemented to a highly congested sub-network in the city center of Naples (Italy) and different scenarios are tested and compared. The “do nothing” scenario (current; DN) and the “modeled compliance” (MC) scenario, in which travelers’ reaction to the information (i.e., compliance) is explicitly represented. In order to evaluate the effectiveness of the proposed strategy and the modeling framework, the following analyses are carried out: (i) Network performance analysis; (ii) system convergence and stability analysis, as well as the compliance evolution over time; (iii) and emissions and fuel consumption impact analysis.