Real-time Control Approach Research Articles

A Practical, Adaptive, and Scalable Real-Time Control Approach for Stormwater Storage Systems

Traditionally, urban stormwater infrastructure systems consist of passive infrastructure that is not actively controlled in response to rainfall events. Recently, real-time control (RTC) has been considered as a means to significantly increase the capacity and lifespan of these systems. This paper introduces the target flow control systems (TFCS) approach, which can use real-time control of systems of storages to achieve the desired flow conditions at the locations of interest. The first distinctive feature of this approach is that it does not require calibration to catchment-specific data, unlike existing approaches. This means that the TFCS approach is generally applicable to different catchments and is able to respond to future changes in runoff due to land use and/or climate change. The second distinctive feature is that the approach only requires storage-level information measured in real time with the aid of low-cost pressure sensors. This means that the approach is practical and relatively easy to implement. In addition to the introduction of the novel TFCS approach, a key innovation of this study is that the approach is tested on three case studies, each with different physical configurations and stormwater management objectives. Another key innovation is that the TFCS approach is compared to five RTC approaches, including three of the best-performing advanced approaches from the literature. Comparisons of multiple RTC approaches that consider both performance and practicality across multiple case studies are rare. Results show that the TFCS approach is the only one of the five control approaches analysed that has both the best overall performance and the highest level of practicality. The outcomes highlight the potential of the TFCS approach as a practical RTC approach that is applicable to a wide range of catchments with different stormwater management objectives. By maximizing the performance of existing stormwater storages, the TFCS approach can potentially extend the lifespan of existing infrastructure and avoid costly upgrades due to increased runoff caused by land use and climate change.

A multi-agent system approach for real-time energy management and control in hybrid low-voltage microgrids

The real-time operation of microgrids is crucial due to its ability to enhance energy resilience and efficiency. Continuous monitoring and adaptation to fluctuations in energy supply and demand ensure a stable and reliable electricity supply to local communities, even in the case of grid disturbances. This article presents an efficient and easily implementable real-time energy management and control system based on multi-agent systems for hybrid Low-Voltage Micro-Grids (LVMGs) using energy storage systems and renewable sources. The main objective of the proposed approach is to determine optimal setpoints for all microgrid components to improve overall efficiency and reduce electricity costs while satisfying multiple constraints. The well-defined flexibilities of all microgrid components to adjust their power are exploited to achieve optimal power allocation. The approach is tested and validated by developing a simulation environment using the Java Agent Development (JADE) framework, enabling the implementation of microgrid optimization through MATLAB/Simulink.

Bio-Inspired Motion Emulation for Social Robots: A Real-Time Trajectory Generation and Control Approach.

Assistive robotic platforms have recently gained popularity in various healthcare applications, and their use has expanded to social settings such as education, tourism, and manufacturing. These social robots, often in the form of bio-inspired humanoid systems, provide significant psychological and physiological benefits through one-on-one interactions. To optimize the interaction between social robotic platforms and humans, it is crucial for these robots to identify and mimic human motions in real time. This research presents a motion prediction model developed using convolutional neural networks (CNNs) to efficiently determine the type of motions at the initial state. Once identified, the corresponding reactions of the robots are executed by moving their joints along specific trajectories derived through temporal alignment and stored in a pre-selected motion library. In this study, we developed a multi-axial robotic arm integrated with a motion identification model to interact with humans by emulating their movements. The robotic arm follows pre-selected trajectories for corresponding interactions, which are generated based on identified human motions. To address the nonlinearities and cross-coupled dynamics of the robotic system, we applied a control strategy for precise motion tracking. This integrated system ensures that the robotic arm can achieve adequate controlled outcomes, thus validating the feasibility of such an interactive robotic system in providing effective bio-inspired motion emulation.

Data-driven real-time predictive control for industrial heating loads

Uncertainties and computational complexity are two growing challenges in scheduling industrial heating loads. In this paper, a data-driven real-time predictive control approach is proposed to deal with these challenges in the industrial scheduling of bitumen tanks. Specifically, predictive control technology is utilized to leverage the updated information to mitigate the negative impact of past uncertainties in equipment parameters and external environmental factors, which may lead to temperature constraint violations in the bitumen tank operation processes. Meanwhile, a data-driven method using artificial neural networks (ANN) is developed to ensure efficient computation for real-time predictive control. Moreover, a two-layer control method is devised to reduce the calculation time for day-ahead optimal scheduling of a large scale of bitumen tanks, aiming to generate sufficient high-quality data for training ANN. In the two-layer control method, the clustered temperature transfer processes of bitumen tanks are analyzed and modeled for the first time. Simulation results indicate that the two-layer control method can significantly reduce the computational time required for the day-ahead optimal scheduling of bitumen tanks, facilitating the generation of a large amount of high-quality data for training ANN. Subsequently, the application of ANN enables real-time predictive control, helping to eliminate the negative impact of uncertainties.

An eco-driving strategy for autonomous electric vehicles crossing continuous speed-limit signalized intersections

The rapid advancement of Vehicle-to-Everything communication (V2X) technology presents opportunities for enhancing traffic energy efficiency. With V2X, this paper introduces an eco-driving strategy for autonomous electric vehicles to navigate continuous signalized intersections. This strategy incorporates the realistic powertrain's working efficiency and constraints into the vehicle motion optimization. In the domain of the global path, we formulate a co-optimization problem of vehicle motion and powertrain operation to tap the vehicle energy-saving potential and derive an optimal velocity profile, considering constraints related to queue release and limit speed in intersections. In the short rolling time domain, we propose a predictive control approach for vehicle speed tracking and real-time powertrain control. Simulation results under various scenarios show the advantages of the proposed method in terms of energy consumption compared to the baseline and existing methods.

Non-parametric fractional-order controller design for stable process based on modified relay

The relay feedback approach is an effective approach for real-time process control. However, it requires accurate limit cycle data. This paper proposes a novel relay feedback approach for fractional-order (FO) controller design to regulate the stable process performance. The proposed methodology does not require prior knowledge of the process and assures accurate limit cycle. Accurate limit cycle parameters are first estimated using the proposed relay. Then, the FO controller is designed based on the output of the limit cycle obtained through the proposed relay, phase, and Nyquist stability criteria. Further, the accuracy of the proposed relay is verified analytically through harmonic analysis. Various performance parameters and indices are estimated and compared with recently published techniques to assess the effectiveness. Additionally, disturbance and parametric uncertainty analyses are conducted to examine the disturbance rejection capacity and robustness. The efficacy of the proposed methodology is further validated on a real-time liquid-level control four-tank process.

Robust control of electrohydraulic soft robots.

This article introduces a model-based robust control framework for electrohydraulic soft robots. The methods presented herein exploit linear system control theory as it applies to a nonlinear soft robotic system. We employ dynamic mode decomposition with control (DMDc) to create appropriate linear models from real-world measurements. We build on the theory by developing linear models in various operational regions of the system to result in a collection of linear plants used in uncertainty analysis. To complement the uncertainty analyses, we utilize ("H Infinity") synthesis techniques to determine an optimal controller to meet performance requirements for the nominal plant. Following this methodology, we demonstrate robust control over a multi-input multi-output (MIMO) hydraulically amplified self-healing electrostatic (HASEL)-actuated system. The simplifications in the proposed framework help address the inherent uncertainties and complexities of compliant robots, providing a flexible approach for real-time control of soft robotic systems in real-world applications.

Optimal scheduling and real-time control of a microgrid with an electrolyzer and a fuel cell systems using a reference governor approach

This paper proposes a novel approach for the optimal scheduling and control of a microgrid with an electrolyzer and fuel cell systems, both of proton exchange membrane (PEM) technology. It is based on a hierarchical procedure constituted of two levels of optimization: a higher level based on an economic optimization for the optimal scheduling of different components of the microgrid, and a lower level for the real-time control of the hydrogen systems: PEM electrolyzer (PEMEZ) and PEM fuel cell (PEMFC). The flexible operation imposed by the higher level leads to a violation of the limits designed by the manufacturer of the hydrogen components, specifically when switching from one power level to another. The current proportional-integral (PI) controllers integrated into those systems cannot handle this issue, which provokes a premature aging phenomenon of the materials and leads to poor performance of the systems. In the present paper, at the lower level, a reference governor (RG) real-time control approach has been added on top of the PI controller to ensure the respect of the operating limits and guarantee better performances. The focus has been given to the stack's temperature in both the electrolyzer and fuel cell systems as the control objective because of its direct influence on the material's durability and, by extension, on the efficiency. The bi-level optimization and control architecture has been applied and validated through simulations using data from a real-world case study, specifically the Savona Campus Smart Polygeneration Microgrid in Italy. The results showed a significant reduction in the overshoots of the stack's temperature compared to the PI controller.

A Novel Real-Time Control Approach for Sparse and Safe Frequency Regulation in Inverter Intensive Microgrids

A Novel Real-Time Control Approach for Sparse and Safe Frequency Regulation in Inverter Intensive Microgrids

Pseudospectral convex optimization for on-ramp merging control of connected vehicles

It can be a daunting task for human drivers to merge into highways because of the intricate vehicle negotiations and potential risk within limited time and space. Connected vehicle (CV) technologies could be a solution to this problem and offer many benefits to the road safety, traffic mobility, and energy efficiency. However, real-time optimal control of CVs is still an open challenge, due to the nonlinear vehicle dynamics, non-convex fuel consumption model, and highly dynamic uncertain inter-vehicle interactions. To tackle these issues, a novel real-time optimal control approach that balances the computational efficiency and solution optimality is proposed for the purpose of onboard application. To this end, the pseudospectral collocation method is integrated with a sequential convex programming approach to develop two new optimization algorithms, which are implemented within a model predictive control (MPC) framework to allow for real-time generation of optimal merging speed profiles. One algorithm leverages the line search technique to improve convergence, and the other benefits from the trust region method for better computational efficiency. The optimality and convergence process of both proposed algorithms are investigated by comparing their solutions with a popular non-linear solver. Simulation results show that the proposed methods outperform the benchmark in terms of computational cost, fuel consumption, and traffic efficiency. In particular, the proposed fuel-economy merging rule can save 57.1% fuel consumption on average on four different traffic volumes. Meanwhile, the proposed optimal control algorithms can reduce 2.2% travel time on average comparing to the “first-in-first-out” merging rule.

A Linear-Parameter-Varying Formulation for Model Predictive Perimeter Control in Multi-Region MFD Urban Networks

An alternative approach for real-time network-wide traffic control in cities that has recently gained attention is perimeter flow control. Many studies have shown that this method is more efficient than state-of-the-art adaptive signal control strategies for heterogeneously congested urban networks. The basic concept of such an approach is to partition heterogeneous cities into a small number of homogeneous regions (zones) and apply perimeter control to the interregional flows along the boundaries between regions. The transferring flows are controlled at the traffic intersections located at the borders between regions so as to distribute the congestion in an optimal way and minimize the total delay of the system. The focus of current work is the mathematical formulation of the original nonlinear problem in a linear parameter-varying (LPV) form so that optimal control can be applied in a (rolling horizon) model predictive concept. This work presents the mathematical analysis of the optimal control problem as well as the approximations and simplifications that are assumed in order to derive the formulation of a linear optimization problem. Numerical simulation results for the case of a macroscopic environment (plant) are presented in order to demonstrate the efficiency of the proposed approach. Results for the closed-loop model predictive control scheme are presented for the nonlinear case, which is used as “benchmark,” as well as the linear case. Furthermore, the developed scheme is applied to a large-scale microsimulation of a European city with more than 500 signalized intersections in order to better investigate its applicability to real-life conditions. The simulation experiments demonstrate the effectiveness of the scheme compared with fixed-time control because all of the performance indicators are significantly improved. Funding: This work was supported by Dit4Tram “Distributed Intelligence & Technology for Traffic & Mobility Management” project from the European Union’s Horizon 2020 research and innovation programme under [Grant agreement 953783].

Towards in-line real-time characterization of roll-to-roll produced ZTO/Ag/ITO thin films by hyperspectral imaging

Large area manufacturing processes of thin films such as large-area vacuum roll-to-roll coating of dielectric and gas permeation barrier layers in industry require a precise control of e.g. film thickness, homogeneity, chemical compositions, crystallinity and surface roughness. In order to determine these properties in real time, hyperspectral imaging is a novel, cost-efficient, and fast tool as in-line technology for large-area quality control. We demonstrate the application of hyperspectral imaging to characterize the thickness of thin films of the multilayer system ZTO/Ag/ITO produced by roll-to-roll magnetron sputtering on 220 mm wide polyethylene terephthalate substrate. X-ray reflectivity measurements are used to determine the thickness gradients of roll-to-roll produced foils with sub nanometer accuracy that serve as ground truth data to train a machine learning model for the interpretation of the hyperspectral imaging spectra. Based on the model, the sub-layer thicknesses on the complete substrate foil area were predicted which demonstrates the capabilities of this approach for large-scale in-line real-time quality control for industrial applications.

Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches

Optimizing the production of recombinant proteins is a problem of major industrial and pharmaceutical importance. Secretion of the protein by the host cell considerably simplifies downstream purification processes. However, for many proteins, this is also the limiting production step. Current solutions involve extensive engineering of the chassis cell to facilitate protein trafficking and limit protein degradation triggered by excessive secretion-associated stress. Here, we propose instead a regulation-based strategy in which induction is dynamically adjusted to an optimal strength based on the current stress level of the cells. Using a small collection of hard-to-secrete proteins, a bioreactor-based platform with automated cytometry measurements, and a systematic assay to quantify secreted protein levels, we demonstrate that the secretion sweet spot is indicated by the appearance of a subpopulation of cells that accumulate high amounts of proteins, decrease growth, and face significant stress, that is, experience a secretion burnout. In these cells, adaptations capabilities are overwhelmed by a too strong production. Using these notions, we show for a single-chain antibody variable fragment that secretion levels can be improved by 70% by dynamically keeping the cell population at optimal stress levels using real-time closed-loop control.

Real-time measurement-driven reinforcement learning control approach for uncertain nonlinear systems

The paper introduces an interactive machine learning mechanism to process the measurements of an uncertain, nonlinear dynamic process and hence advise an actuation strategy in real-time. For concept demonstration, a trajectory-following optimization problem of a Kinova robotic arm is solved using an integral reinforcement learning approach with guaranteed stability for slowly varying dynamics. The solution is implemented using a model-free value iteration process to solve the integral temporal difference equations of the problem. The performance of the proposed technique is benchmarked against that of another model-free high-order approach and is validated for dynamic payload and disturbances. Unlike its benchmark, the proposed adaptive strategy is capable of handling extreme process variations. This is experimentally demonstrated by introducing static and time-varying payloads close to the rated maximum payload capacity of the manipulator arm. The comparison algorithm exhibited up to a seven-fold percent overshoot compared to the proposed integral reinforcement learning solution. The robustness of the algorithm is further validated by disturbing the real-time adapted strategy gains with a white noise of a standard deviation as high as 5%.

A holistic model-less approach for the optimal real-time control of power electronics-dominated AC microgrids

This paper addresses the problem of optimally operating a set of grid-forming devices in an AC microgrid when a detailed network model is not available. The main aim of the approach is to maximize the power sharing of the controllable grid-forming devices and to maintain the frequency and the nodal voltages of the microgrid as close as possible to their corresponding references. The proposed control architecture is conformed by a local control layer in each grid-forming device that intends to emulate the performance of a synchronous machine and a centralized secondary controller composed of two complementary tools that coordinates the setpoints of the grid-forming devices: an online feedback optimization algorithm and an automatic generation control. The proposed method has been validated through simulations and hardware-in-the-loop tests, evidencing its good performance and robustness under different conditions.

Calibration-free approach to reactive real-time control of stormwater storages

Stormwater systems will likely require major upgrades due to increases in peak flows caused by the combined effects of urbanization, densification (urban infill) and climate change. Recently, the real-time control (RTC) of storages has been considered as a means to reduce peak flows and potentially avoid major infrastructure upgrades. This paper introduces a RTC approach, the Target Flow Control (TFC) approach, which is able to maintain system outflows at or below specified target flows (e.g., existing system capacity). The key features of the approach are that it does not require calibration to catchment specific data, as is the case for existing approaches and is only based on storage level information measured in real-time during rainfall events. This makes the approach generally applicable to different catchments and able to respond to future changes in rainfall due to land use and/or climate change. The TFC approach is tested on a simple two-storage system for 750 design rainfall events from a range of climates, event durations, rainfall intensities and temporal patterns using a practically achievable control time step of 30 s. Results show that the TFC approach can achieve the desired target flows effectively, with 95% of the experiments having less than 10% errors in target flows. This is an exciting achievement, given the TFC approach does not require calibration and only requires measurable storage level information. The outcomes highlight the potential of the TFC approach as a practical RTC approach that can maximize the effectiveness of existing stormwater conveyance systems by maintaining stormwater system outflows at desired target flows, thereby potentially avoiding stormwater infrastructure upgrades due to land use and climate change.

A predictive control approach for thermal energy management in buildings

Building equipment accounts for almost 40% of total global energy consumption. More than half of which is used by active systems, such as heating, ventilation and air conditioning (HVAC) systems. These latter are responsible for the occupants’ well-being and considered among the main consumers of electricity in buildings. In order to improve both occupants’ comfort and energy efficiency in buildings, optimal control oriented models, such as Model Predictive Control (MPC), have proven to be promising techniques for developing intelligent control strategies for building energy management systems. This paper presents a real-time predictive control approach of an air conditioning (AC) system for thermal regulation in a single-zone building using MPC control framework. The proposed approach takes into account the physical parameters of the building, weather predictions (i.e. ambient temperature and solar radiation) and time-varying thermal comfort constraints to maintain optimal energy consumption of the AC while enhancing occupants’ comfort. For this purpose, a control-oriented thermal model for a room integrated with AC system is first developed using physics-based (white box) technique and then used to design and develop the MPC controller model. A numerical case study has been investigated and simulation results show the effectiveness of the proposed approach in reducing the energy consumption by about 68% while providing a significant indoor thermal improvement. A conventional On–Off controller was used as a baseline reference to evaluate the system performance against the proposed approach.

A Real-Time Fault-Tolerant Control Approach to Ensure the Resiliency of a Self-Healing Multilevel Converter

Ensuring service continuity in safety critical applications is crucial. In some of these applications, multilevel converters play a vital role. In this regard, this research work presents a self-healing fault-tolerant control approach to ensure the resiliency of a neutral-point clamped converter when a semiconductor component encounters an open circuit fault. The defective semiconductor can be a power switch or a clamping diode. By applying the proposed real-time fault-tolerance control, the rated output voltage and output current are restored during post-fault operation. Furthermore, the total harmonic distortion value of the output voltage during application of the real time self-healing control does not increase when compared with that during healthy operation. Since the realization of the proposed control strategy does not require any bidirectional switch, a fast transition between the healthy and fault-tolerant operation is accomplished. Moreover, the proposed structure can ensure service continuity in case of a fault event in the anti-parallel diodes, something which has been overlooked in previously conducted research works.

Improving the performance of solar membrane distillation processes for treating high salinity feeds: A process control approach for cleaner production

The treatment of high salinity feeds, for example, brines from other desalination technologies such as reverse osmosis, is becoming one of the sought-after applications of solar membrane distillation processes. One of the most appropriate operating methodologies to carry out these treatments is a batch operation. Nevertheless, throughout this operation, the variability of the salinity in the feed water acts as a disturbance causing the optimal operating conditions required to reduce the process’ thermal energy consumption (expressed in kWh/m3 of freshwater production) to change. To face this issue, this study proposes two real-time optimization control approaches, with energy-saving achievements, based on the extremum-seeking control methodology. The first controller reduces the thermal energy consumption of the membrane distillation module by adapting the feed flow rate according to the level of salinity of the operation. The second one is based on the same idea but tries to search for a trade-off solution among energy consumption and water production. Both approaches are simulated under actual process circumstances using data and a validated model of a pilot plant located at Plataforma Solar de Almería (Spain). The results show how the proposed controllers improve the system’s daily operation concerning conventional operational procedures, reducing the system’s mean thermal energy consumption up to 35% (from 993.81 kWh/m3 to 651.52 kWh/m3) in the best case. On the whole, the systematic implementation of the proposed control strategies allows enhancing the sustainability and thermal efficiency of the operation by minimizing unnecessary thermal energy usage. This fact can play a role in solar membrane distillation systems either to lengthen operations or reduce reliance on conventional backup devices in periods with inconstant solar irradiance.

Integrated real-time control of mixed-model assembly lines and their part feeding processes

Mixed-model assembly lines enable a flow-oriented mass production of individualized products such as cars. The most investigated operational decision problems in this domain are the sequencing problem, which plans the initial sequence of cars launched down the line, and the resequencing problem, which alters a given car sequence by applying resequencing buffers. This dichotomy of existing research neglects a third, even more short-term decision level: launch control under real-time conditions. In large automotive assembly lines unforeseen disturbances are rather the norm, so that one of the most important decisions to maintain a smooth and error-free production flow is the continuous launching decision of the next car model released from the central chassis storage system into final assembly. The launching decision in every cycle is made in a central control room (or leitstand) and has to consider the current state of the assembly line itself, with all potential time delays and defects in any assembly station, along with all part feeding processes under real-time conditions. This paper provides an integrated solution approach for real-time launch control that applies sophisticated optimization procedures considering both the workflow along the assembly line and its impact on part feeding processes. We test our real-time approach in a simulation study and compare it with alternative launching approaches. Our results show that sophisticated real-time optimization clearly outperforms its competitors. Furthermore, an integrated approach including part feeding aspects can considerably reduce safety stocks of parts within workstations.