Real-time Control Strategy Research Articles

Distributed Real-Time Multi-Objective Control of a Virtual Power Plant in DC Distribution Systems

Due to the strong coupling between power injections and bus voltages in DC distribution networks, it is impossible to achieve economic dispatch and recover the nominal voltages of all buses, simultaneously. In light of this, a multi-objective optimization-based control model is formulated for the multiple distributed energy resources (DERs) within the DC distribution network to be dispatched as a virtual power plant. A compromise can be made between the economic efficiency and voltage distribution quality by placing different weights on the multiple objectives. To this end, a distributed real-time multi-objective control strategy is proposed. Specifically, facilitated by sequential peer-to-peer communication, the dynamics of the cyber-physical DC distribution system imitate the primal-dual subgradient algorithm to the multi-objective optimization, thus can iteratively converge to its optimal solution. A dynamic weight adjustment mechanism is further designed to bound the bus voltages within a feasible range. This strategy is plug-and-play, privacy-preserving, robust to communication failures, and responsive to change of operating conditions compared to its centralized counterpart. Simulation studies of two typical DC distribution feeders verify the effectiveness of the proposed approach in various scenarios.

Reduced Collateral Tissue Damage Using Thermal-Feedback-Based Power Adaptation of an Electrosurgery Inverter.

Well-selected power with accurate delivery is of importance in electrosurgery to generate proper temperature at the cutting site, and thus, reduce undesired collateral tissue damages. Conventional electrosurgery generator (ESG) targets tracking a preset power, manually set by surgeons per their experience before the surgery, with high accurate delivery. It is possible that this fixed power setting is not at the optimal point and, thus, increases the possibility of added-collateral biomedical tissue damage. To eliminate the potential negative impact of the fixed and ill-suited power setting, a real-time feedback control scheme is outlined in this article to adjust the preset power of the ESG to create an adaptive power reference, which is then tracked using an experimental high-frequency inverter (HFI) that enables electrosurgery with a fundamental (sinusoidal) output frequency of 390 kHz. Subsequently, experiments using the gallium nitride (GaN)-based HFI are carried out to demonstrate the efficacy of the new variable-power approach over the conventional fixed power approach in terms of collateral tissue damage reduction.

Research on Motor Speed Control Method Based on the Prevention of Vehicle Rollover

Vehicle driving safety is an important performance indicator for vehicles, and there is still much room for development in the active safety control of electric vehicles. Vehicles are susceptible to rollover when making sharp turns or overtaking at high speed. In order to improve the anti-rollover stability of electric vehicles (EV), this study proposes a real-time motor control strategy, mainly according to the acquisition of vehicle attitude data and real-time monitoring of the vehicle’s operating state. The lateral load transfer rate was defined as the vehicle rollover evaluation index. When the real-time rollover indicator exceeded the limit safety threshold this article set, the motor speed would be reduced through active control to avoid rollover or reduce the risk of rollover. Both simulation results in Carsim and Simulink showed that the motor control strategy is highly reliable and real-time capable, and the active safety of EV was improved significantly.

Two-time-scale coordinated optimal control of building energy systems for demand response considering forecast uncertainties

Predictive scheduling is essentially needed to optimize energy dispatch of building energy systems for demand response, as it can maximize the benefits considering future conditions. However, the optimized demand response probably cannot be achieved and energy demands probably cannot be fully satisfied in operation due to the large discrepancy between the predicted and actual conditions. Generic and comprehensive optimal control methods of building energy systems for demand response, which can achieve maximized benefits while ensuring satisfaction of actual energy demands, are still absent. In this study, a two-time-scale coordinated optimal control strategy is proposed to optimize energy dispatch between building energy systems for demand response considering forecast uncertainties. The control strategy includes two coordinated schemes: a stochastic scheduling scheme and a real-time optimal control scheme. Forecast uncertainties are quantified based on real meteorological data. The optimal start time of scheduling optimization horizon is investigated. The control strategy was tested on a platform with PV and battery, established based on the Zero Carbon Building in Hong Kong. Results show that the optimal scheduling optimization horizon start time is the initial of the off-peak period. The proposed strategy reduces up to 14.7% cost compared with existing strategies while satisfying actual energy demands.

Elasto-Geometrical Model-Based Control of Industrial Manipulators Using Force Feedback: Application to Incremental Sheet Forming

This paper aims to improve the positioning accuracy of serial industrial manipulators using force feedback in manufacturing processes by implementing an elasto-geometrical model-based control. Initially, the real-time position control strategy using a force feedback to elastically correct the Tool Center Point (TCP) pose of serial industrial manipulators is detailed. To continue, an efficient model structure identification and calibration is proposed to shorten the elasto-geometrical modeling process. The Virtual Joint Method (VJM) is chosen to iterate and complete the robot stiffness modeling. This method considers that the elastic deformations are only localized at the joints of the robot. An appropriate and original test-model approach allows a minimum of optimization iterations to find the best compromise between complexity and accuracy of the modeling. The proposed approach is illustrated in detail by the Stäubli TX200 robot modeling. Finally, the reliability and responsiveness of the developed control framework is then evaluated through experimental tests in an Incremental Sheet Forming (ISF) context. An average improvement of 70% in trajectory-tracking accuracy is achieved during these tests. Overall, the high accuracy and responsiveness of the developed system demonstrate a promising potential for deploying industrial manipulators to a cost-effective manufacturing processes in industry 4.0.

Vision sensor based residual vibration suppression strategy of non-deformable object for robot-assisted assembly operation with gripper flexibility

PurposeIndustrial robots are extensively deployed to perform repetitive and simple tasks at high speed to reduce production time and improve productivity. In most cases, a compliant gripper is used for assembly tasks such as peg-in-hole assembly. A compliant mechanism in the gripper introduces flexibility that may cause oscillation in the grasped object. Such a flexible gripper–object system can be considered as an under-actuated object held by the gripper and the oscillations can be attributed to transient disturbance of the robot itself. The commercially available robots do not have a control mechanism to reduce such induced vibration. Thus, this paper aims to propose a contactless vision-based approach for vibration suppression which uses a predictive vibrational amplitude error-based second-stage controller.Design/methodology/approachThe proposed predictive vibrational amplitude error-based second-stage controller is a real-time vibration control strategy that uses predicted error to estimate the second-stage controller output. Based on controller output, input trajectories were estimated for the internal controller of the robot. The control strategy efficiently handles the system delay to execute the control input trajectories when the oscillating object is at an extreme position.FindingsThe present controller works along with the internal controller of the robot without any interruption to suppress the residual vibration of the object. To demonstrate the robustness of the proposed controller, experimental implementation on Asea Brown Boveri make industrial robot (IRB) 1410 robot with a low frame rate camera has been carried out. In this experiment, two objects have been considered that have a low (<2.38 Hz) and high (>2.38 Hz) natural frequency. The proposed controller can suppress 95% of vibration amplitude in less than 3 s and reduce the stability time by 90% for a peg-in-hole assembly task.Originality/valueThe present vibration control strategy uses a camera with a low frame rate (25 fps) and the delays are handled intelligently to favour suppression of high-frequency vibration. The mathematical model and the second-stage controller implemented suppress vibration without modifying the robot dynamical model and the internal controller.

Reducing airborne infection risk of COVID-19 by locating air cleaners at proper positions indoor: Analysis with a simple model

Portable air cleaners (PACs) can remove airborne SARS-CoV-2 exhaled by COVID-19 infectors indoor. However, effectively locating PAC to reduce the infection risk is still poorly understood. Here, we propose a simple model by regressing an equation of seven similarity criteria based on CFD-modeled results of a scenario matrix of 128 cases for office rooms. The model can calculate the mean droplet nucleus concentration with very low computing costs. Combining this model with the Wells–Riley equation, we estimate the airborne infection risk when a PAC is located in different positions. The two similarity criteria, Bp+ and Gp+, are critical for characterizing the effect of the position and airflow rate of PAC on the infection risk. An infection probability of less than 10% requires Bp + to be larger than 144 and Gp + to be larger than 0.001. These criteria imply that locating PAC in the center of the room is optimal under the premise that the airflow rate of PAC is greater than a certain level. The model provides an easy-to-use approach for real-time risk control strategy decisions. Furthermore, the placement strategies offer timely guidelines for precautions against the prolonged COVID-19 pandemic and common infectious respiratory diseases.

Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems

Extreme events are featured by high impact and low probability, which can cause severe damage to power systems. There has been much research focused on resilience-driven operational problems incorporating mobile energy storage systems (MESSs) routing and scheduling due to its mobility and flexibility. However, existing literature focuses on model-based optimization approaches to implement the routing process of MESSs, which can be time consuming and raise privacy issues since the requirement for global information of both power and transportation networks. Furthermore, a real-time automatic control scheme of MESSs has become a challenging task due to the system high variability. As such, this paper develops a model-free real-time multi-agent deep reinforcement learning approach featuring parameterized double deep Q-networks to reformulate the coordination effect of MESSs routing and scheduling process as a Partially Observable Markov Game, which is capable of capturing a hybrid policy including both discrete and continuous actions. A coupled transportation network and linearized AC-OPF algorithm are realized as the environment, while the internal uncertainties associated with renewable energy sources, load profiles, line outages, and traffic volumes are incorporated into the proposed data-driven approach through learning procedure. Extensive case studies including both 6-bus and 33-bus power networks are developed to evaluate the effectiveness of the proposed approach. Specifically, a detailed comparison between different multi-agent reinforcement learning and model-based optimization approaches is conducted to present the superior performance of the proposed approach.

A review of public transport transfer synchronisation at the real-time control phase

ABSTRACT We perform a systematic review of real-time control strategies for transfer synchronisations in public transport. In particular, we review inter-station and intra-station control measures that can be applied in near real-time, including vehicle holding, stop-skipping, speed control, short-turning, rescheduling, interlining, rerouting, and boarding limits. The topic of public transfer synchronisation at the operational phase is both a technically challenging and timely topic. It is technically challenging because finding optimal control measures is a computationally intensive problem and these measures should be computed in near real-time. It is also timely because of the emerging developments in shared mobility, Mobility-as-a-Service schemes, on-demand public transport, and vehicle automation that pose new opportunities as well as challenges in designing seamless passenger transfers. In our systematic review, we analyse the modelling approaches for real-time transfer synchronisation, including mathematical programmes, heuristics, rule-based approaches, and stochastic optimisation models. We then critically discuss the available literature and provide a future outlook, outlining six research directions.

Coordinated Control Can Deliver Synergies Across Multiple Rainwater Storages

AbstractStudies in Real−Time Control (RTC) Rainwater Harvesting Systems (RWH) have to date been limited to the control of single storages, leaving the potential benefits of operating multiple storages in a coordinated manner largely untested. In this study, we aimed to design an optimization‐based RTC strategy that can operate multiple storages in a coordinated manner to achieve multiple objectives. We modeled the long‐term performance of this coordinated approach (i.e., termed as coordinated control) across a range of storage sizes and compared it with a strategy that optimized the operation of each storage individually, ignoring the state of other stores within the system. Our results show that coordinated control delivered a synergy benefit in achieving better baseflow restoration, with almost no detriment to the water supply and flood protection (overflow reduction) performance. The efficiency achieved through coordinated control allows large storages to compensate for smaller, underperforming systems, to achieve higher overall performance. Such a finding suggests a general control principle in building coordination among multiple storages, which can potentially be adapted to mitigate flooding risks, and also applied to other stormwater control measures. This also opens up a new opportunity for practitioners to construct a future “smart rainwater grid” using a network of distributed storages, in combination with centralized large storages, to manage urban stormwater in a range of contexts and for a range of environmental objectives.

Swift hydraulic models for real-time control applications in sewer networks

Real-time control (RTC) is an important tool for safe and cost-effective operations of sewer systems by, for example, reducing sewer overflow or enhancing sulfide mitigation. Due to the long transport time of sewage and the inherent dynamics in sewage flow rates, model-predictive control is often needed, which requires the prediction of sewage hydraulic characteristics across the network. The full hydraulic models are often unsuitable for such purposes due to their high computational demands, which are not affordable as the models need to be called for numerous times in each optimisation step. In this study, two swift, data-driven hydraulic models are developed to predict sewage flow rates in gravity sewers receiving feeds from rising main(s) and gravity main(s), respectively. The models are shown to be able to predict both the sewage flow rate and the cross-sectional flow area in high fidelities with solutions of Saint-Venant Equations, but reduce the computational time by up to four orders of magnitude. The swift hydraulic models were then integrated into an RTC strategy for NaOH dosing in a simulated real-life sewer network, and achieved cost-effective control of sulfide. These models could potentially be used for other sewer RTC applications.

Optimization of 3D controlled ELM-free state with recovered global confinement for KSTAR with n = 1 resonant magnetic field perturbation

Mitigation of deleterious heat flux from edge-localized modes (ELMs) on fusion reactors is often attempted with 3D perturbations of the confining magnetic fields. However, the established technique of resonant magnetic perturbations (RMPs) also degrades plasma performance, complicating implementation on future fusion reactors. In this paper, we introduce an adaptive real-time control scheme on the KSTAR tokamak as a viable approach to achieve an ELM-free state and simultaneously recover high-confinement (β N ∼ 1.91, β p ∼ 1.53, and H 98 ∼ 0.9), demonstrating successful handling of a volatile complex system through adaptive measures. We show that, by exploiting a salient hysteresis process to adaptively minimize the RMP strength, stable ELM suppression can be achieved while actively encouraging confinement recovery. This is made possible by a self-organized transport response in the plasma edge which reinforces the confinement improvement through a widening of the ion temperature pedestal and promotes control stability, in contrast to the deteriorating effect on performance observed in standard RMP experiments. These results establish the real-time approach as an up-and-coming solution toward an optimized ELM-free state, which is an important step for the operation of ITER and reactor-grade tokamak plasmas.

Learning-based hierarchical control of water reservoir systems

The optimal control of a water reservoir system represents a challenging problem, due to uncertain hydrologic inputs and the need to adapt to changing environment and varying control objectives. In this work, we propose a real-time learning-based control strategy based on a hierarchical predictive control architecture. Two control loops are implemented: the inner loop is aimed to make the overall dynamics similar to an assigned linear model through data-driven control design, then the outer economic model-predictive controller compensates for model mismatches, enforces suitable constraints, and boosts the tracking performance. The effectiveness of the proposed approach is illustrated on an accurate simulator of the Hoa Binh reservoir in Vietnam. Results show that the proposed approach outperforms stochastic dynamic programming.

A Novel Real-Time Traffic Signal Control Strategy for Emergency Vehicles

Intelligent traffic signal control strategies for emergency vehicles (EV) is a research hotspot in intelligent transportation systems. Its research results will promote the shortening of the travel time of EVs, which is vital to saving lives and reducing property losses. This paper proposes a novel real-time traffic signal control strategy. The first step is called on-demand signal timing for reducing road saturation, which makes it possible that ordinary vehicles(OV) give way to EVs and make EVs travel faster, according to three indicators: the emergency response level(ERL), the congestion level of the road section(CLRS), and the time urgency level(TUL). The second step is called novel signal preemption, which combines non-intrusive preemption and intrusive preemption. This step provides green indicates and makes EVs pass through intersections quickly without stopping. The final step is called the recovery cycle strategy, which restores the road network to the normal situation as soon as possible by using linear programming(LP) to find the shortest green time in each phase after an EV passes the intersection. This paper conducts simulation experiments on an urban traffic simulator SUMO. The experimental results show that our novel strategy can shorten the travel time and reduce the traffic impact on the road network caused by prioritizing the EVs significantly. Compared with the fixed-time control method (FTCM), Min’s flexible signal preemption method(FSPM), and Qin’s intrusive signal preemption method (ISPM), our method can optimize travel time by up to 62.85%, 50.83%, and 11.62%, respectively.

Cost-Effective Coordinated Voltage Control in Active Distribution Networks With Photovoltaics and Mobile Energy Storage Systems

High penetration of intermittent renewable energy resources (RES) will impose great challenges on the conventional voltage control in distribution networks. This paper proposes a two-stage cost-effective coordinated voltage control strategy to mitigate fast voltage violations while minimizing the total voltage regulation cost. In the first stage, the optimal power flow (OPF) is performed to determine the day-ahead dispatch results of on-load tap changers (OLTC) and capacitor banks (CBs). The hourly charging schedule of mobile energy storage systems (MESS) is also obtained through a spatial-temporal decision model embedded in the optimization problem. In the second stage, the real-time voltage control strategy is implemented to regulate the power output of photovoltaics (PVs) and MESS with one minute resolution. A novel fine-tuning control method is proposed to adjust the power output, which can further reduce voltage regulation cost within a certain voltage dead band. To reserve more fast-response power resources for PVs and MESS, the voltage regulation cost curves are designed as a piecewise linear function with different per unit cost in each range. The proposed approach is tested on the IEEE 33-bus and 123-bus distribution systems and comparative simulation results show the enhanced control effect in mitigating voltage violations and saving costs.

Reinforcement learning-based real-time control of coastal urban stormwater systems to mitigate flooding and improve water quality

Reinforcement learning agents can learn real-time stormwater system control strategies that balance the competing goals of flood mitigation and sediment capture in urban watersheds.

A linear parameter-varying modelling approach for dielectric elastomer loudspeakers

In this paper, we present a linear parameter-varying (LPV) model of an electrostatic loudspeaker driven by dielectric elastomer (DE) actuators. Because of its numerical simplicity, this LPV model represents a convenient tool to develop real-time control strategies for the speaker. Starting from a non-linear reduced model of the device, we assume that the control input can be broken into two components: a small-amplitude high-frequency component, responsible for the sound generation, and a slowly-varying bias component, which can be used as a free control parameter to tune and adjust the speaker response. We thus build an LPV model, treating the dynamic response to the high-frequency input component as linear, and parametrising the coefficients of such linear system with respect to the low-frequency input component. We present a validation of the model against experiments, and show that the proposed LPV formulation captures the most relevant non-linearities involved in the DE speaker operation.

Reliable Dispatch of Active Distribution Networks via a Two-Layer Grid-Aware Model Predictive Control: Theory and Experimental Validation

Dispatching active distribution networks (ADNs) is an energy-intensive application that, if implemented via battery-energy storage systems (BESSs), can require a large capacity of these assets in order to fully balance the uncertainties caused by the stochastic demand and generation. The insufficient capacity of the BESSs often leads to their state-of-charge (SOC) saturation; this results in unreliable dispatch tracking. In this work, we propose and experimentally validate a real-time control scheme that achieves a highly-reliable dispatching of ADNs and ensures that the BESSs’ SOC is not saturated during the daily operation. Our proposed scheme uses a two-layer model predictive control (MPC). The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">upper-layer</i> MPC, running every 5 minutes, optimizes the BESSs’ SOC trajectories while minimizing the tracking error, considering the prosumption forecast of the whole day. Then, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">lower layer</i> MPC, running every 30 seconds, takes the BESSs’ SOC trajectories as constraints while achieving a high-resolution tracking of the dispatch plan over the current 5-minute time horizon. Both layers account for the grid constraints by using the augmented relaxed optimal power-flow (AR-OPF) model; an exact convex relaxation of the original AC-OPF and used in this paper (for the first time in the literature) to solve a real-time constrained control problem for ADNs. Our proposed framework was experimentally validated using a 1.5 MVA/2.5 MWh BESS connected to an actual 24-node medium-voltage (MV) ADN that, in Aigle, Switzerland, hosted an uncontrollable 3.2 MWp distributed photovoltaic generation, 3.4 MVA hydro-power generations, and a 2.8 MW base demand.

Research on real-time control strategy of multi-power flow of dual-mode power-split hybrid electric vehicle

With the continuous development of hybrid vehicle control technology, great progress has been made in the research of multi-power flow collaborative control. Due to the internal delay link of each power component, the role of energy storage element, and the limitation of electric power in the whole system, the inevitable delay characteristic of state transfer is caused. Therefore, the speed of multi-power flow control torque coordinated response of hybrid vehicles needs to be improved. The dual-mode power-split hybrid electric vehicle (DMPS-HEV) overall structure and working modes are analyzed, by adopting the combination of theory and experiment method. In order to solve the problem that the power components of dual-mode power-split hybrid electric vehicle cannot follow the optimal control command of the upper energy management strategy quickly due to the engine response delay, thus affecting the control effect of the upper energy management strategy. The research on torque coordination control strategy is carried out, the reference model of electromechanical composite drive is established, and the model reference adaptive coordination control strategy based on Lyapunov stability theory is proposed. The results show that the proposed model reference adaptive torque coordinated control strategy significantly improves the effect of engine response delay on the optimization effect of energy management strategy, and can achieve the control effect of the optimal control strategy of 93.58%. The test platform of the dual-mode power-split hybrid electric vehicle was built. The control system was built based on the rapid control prototype, and the data acquisition system was built based on the NI data acquisition module. The coordinated control strategy of the dual-mode power-split hybrid electric vehicle power system proposed in this paper was verified through the bench test to significantly improve the vehicle fuel economy and the real-time performance of the control strategy, which has a good practical value

Alfvén eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks

Modern tokamaks have achieved significant fusion production, but further progress towards steady-state operation has been stymied by a host of kinetic and MHD instabilities. Control and identification of these instabilities is often complicated, warranting the application of data-driven methods to complement and improve physical understanding. In particular, Alfvén eigenmodes are a class of ubiquitous mixed kinetic and MHD instabilities that are important to identify and control because they can lead to loss of confinement and potential damage to the walls of a plasma device. In the present work, we use reservoir computing networks to classify Alfvén eigenmodes in a large labeled database of DIII-D discharges, covering a broad range of operational parameter space. Despite the large parameter space, we show excellent classification and prediction performance, with an average hit rate of 91% and false alarm ratio of 7%, indicating promise for future implementation with additional diagnostic data and consolidation into a real-time control strategy.