Real-time Control Algorithm Research Articles

A smart home energy management system based on human activity recognition and deep reinforcement learning

Smart home energy management systems (SHEMS) can help users save on energy costs by controlling various household loads in response to environmental changes. However, human activity is closely related to household energy consumption, which can lead to potential energy waste. Additionally, smart homes often feature multiple types of distributed energy resources (DER), and how to leverage their potential for participating in grid scheduling without significantly impacting users remains an unresolved issue. This paper proposes an intelligent home energy management method based on human activity recognition (HAR) and deep reinforcement learning. By using activity recognition results derived from intrusive load monitoring (ILM), we categorize contexts into two groups. The agent employs the soft actor-critic (SAC) algorithm for real-time control. In the first context, the goal is to ensure user satisfaction while reducing energy costs. In the second context, the focus is on controlling DER to participate in grid ancillary services to generate revenue. The proposed agent is trained using a real dataset, and simulations validate the effectiveness of the algorithm in home energy management.

실시간 항공영상 기반 UAV-USV 간 협응 유도·제어 알고리즘 개발

실시간 항공영상 기반 UAV-USV 간 협응 유도·제어 알고리즘 개발

Real-time optimal hierarchy control of HVAC system with maximized ancillary service support in smart buildings: An experimental approach

This article proposes a real-time optimum control algorithm for Heating, Ventilation, and Air Conditioning (HVAC) units of a commercial building aimed to maximize the ancillary power available for frequency regulation services. First, the prediction of energy consumption of air conditioning zones using long short-term memory (LSTM) is achieved, which helps forecast the aggregated regulation capacity bid of the building in the energy markets. Furthermore, to minimize energy costs and thermal discomfort of building dwellers, an optimization algorithm is designed considering the day-ahead electricity tariffs. Moreover, using installed sensors in a lab-scaled hardware prototype, the proposed algorithm is also shown to be capable of interacting with a Building Energy Management System (BEMS) and investigating the electrical and thermal properties of the HVAC unit. The BEMS controller uses an ethernet protocol to interface with the sensors installed at various operational points throughout the HVAC system. The optimal operation of the HVAC system has been executed with the real-time thermal feedback system, which counterbalances the uncertain thermal load or renewable power availability in the building confronted in real-time operation. The simulation results show the performance of the Artificial Neural Network (ANN) based compressor model in deep learning integrated with the other components of the HVAC unit and thermal model of the air-conditioning zone. Finally, the experimental results display the real-time implementation of multiple thermal and electrical conditions within the proposed control scheme of the HVAC unit. This shows the potential of ancillary services capacity through commercial buildings can be maximized with flexible loads and thereby help in power grid support.

SINDy-Based Feature Extraction for Data Driven Model Predictive Control of a Buck Boost Switch Mode Power Supply

Abstract This paper presents two methods of creating model predictive control strategies for efficient real-time control algorithms for power electronics. Two novel methods of performing nonlinear modeling are presented in this research, the first being novel Takagi-Sugeno model, which combines two linear state space models using a membership function to model the nonlinear transitions between operating points. The second method involves using sparse identification of nonlinear dynamics, a nonlinear modeling technique that uses L1 regularization of least squares on for time series data to define a parsimonious polynomial function set. This set is used to define the input feature space to extended dynamic mode decomposition with control. These models are then used for data-driven model predictive control of a buck switch mode power supply to find the optimal duty cycle that regulates the output voltage over a finite receding horizon and validated using an LTSPICE simulation to assess their effectiveness in comparison to a tuned proportional integral derivative controller. Numerical accuracy challenges are discussed, and strategies are offered for their mitigation.

Intelligent control of combined sewer systems using PySWMM—A Python wrapper for EPA’s Stormwater Management Model

Wastewater utilities face competing priorities as they work to protect human health and water quality, and to maintain infrastructure in their communities. Budgetary constraints can be especially pronounced among small to medium-sized utilities. Utilities are increasingly turning to so-called intelligent water approaches as a cost-effective alternative to upgrading aging infrastructure. Intelligent water encompasses automated control and real-time decision support technologies and can be applied at scale to large and small utilities alike accommodating differences in needs, capabilities, and funds. Intelligent water upgrades can be designed to optimize existing conveyance, storage, and treatment during storms to help mitigate flooding and combined sewer overflows. The most promising real-time control algorithms coordinate control of upstream and downstream assets and are designed using urban hydrologic and hydraulic modeling software. The capabilities of legacy software, however, can sometimes inhibit the creation of sophisticated control algorithms. In this paper, we present PySWMM — an open-source Python wrapper developed for the EPA Storm Water Management Model (SWMM). PySWMM enables runtime interactions with the SWMM computational engine to flexibly read, modify system parameters, and control digital infrastructure during a simulation. Crucially, it allows modelers to easily combine SWMM with the rich set of scientific computing, big data, and machine learning modules found in the Python ecosystem. We highlight two real-world intelligent water case studies utilizing PySWMM in the cities of Cincinnati and Columbus, Ohio where it has helped to eliminate tens of millions of gallons of combined sewer overflows annually.

Evaluation of Different Filtering Methods Devoted to Magnetometer Data Denoising

In this article, we describe a performance comparison conducted between several digital filters intended to mitigate the intrinsic noise observed in magnetometers. The considered filters were used to smooth the control signals derived from the magnetometers, which were present in an autonomous forestry machine. Three moving average FIR filters, based on rectangular Bartlett and Hanning windows, and an exponential moving average IIR filter were selected and analyzed. The trade-off between the noise reduction factor and the latency of the proposed filters was also investigated, taking into account the crucial importance of latency on real-time applications and control algorithms. Thus, a maximum latency value was used in the filter design procedure instead of the usual filter order. The experimental results and simulations show that the linear decay moving average (LDMA) and the raised cosine moving average (RCMA) filters outperformed the simple moving average (SMA) and the exponential moving average (EMA) in terms of noise reduction, for a fixed latency value, allowing a more accurate heading angle calculation and position control mechanism for autonomous and unmanned ground vehicles (UGVs).

Design of real time closed loop control in hybrid energy system using cascaded converters: a dynamic solution for high power applications

ABSTRACT This paper presents a Hybrid Renewable Energy system combining multiple sources of renewable energy, such as PV, Wind, and energy storage devices, to provide a reliable and sustainable power supply. Cascaded converters are used to efficiently transform and manage the energy from these sources. To achieve optimal performance and reliable operation, a real-time closed-loop control algorithm is designed and implemented for the cascaded converter. The control algorithm facilitates the smooth integration of energy from various renewable sources, optimizing the utilization of available resources and ensuring a consistent power supply. The algorithm maximizes the energy conversion efficiency of the cascaded converter, reducing energy losses and minimizing waste. It also optimizes the integration of renewable sources, regulates output parameters, and ensures efficient energy conversion.

Multifunctional nanorobot system in precise evaluation and manipulation of virus capsid

It is imperative to have high adaptive techniques for sensing and manipulating biological targets at the nanoscale. This necessity becomes particularly crucial when dealing with fragile living bio-organisms like viruses, where the expression of capsids is closely linked to viral functions and genome constitution. Therefore, the development of a comprehensive system for dissecting and measuring viruses holds significant implications for the pharmaceutical industry and drug manufacturing. Leveraging the sub-nanometer spatial resolution and controllable tip-cantilever architecture of atomic force microscopy (AFM), a probe-laser system has been integrated as a self-sensing robotic end effector. To address intrinsic challenges in AFM-based robotic systems such as the lack of real-time monitoring, low scanning rates, and nonlinear motion caused by piezoelectric actuators, an augmented reality robotic system has been implemented. This system incorporates stereoscopic vision, a haptic feedback controller, a position recovery scheme, and a real-time force control algorithm. The integration of these components enhances the system’s capability to accurately dissect virus capsids. Operators can now perform highly efficient nanoscale tasks with multidimensional perception, utilizing the combination of stereoscopic vision and haptic force control. The position correction during manipulation can achieve a frame rate of over 30 frames per second, imperceptible to the operator, enabling closed-loop operation control. By adopting the proposed nanorobotic system in virology studies, it becomes possible to achieve accurate manipulation and dissection of SARS-CoV-2 pseudovirus capsids, and derive multi-parametric properties such as structural integrity, protein fragment thickness, and adhesive forces. The established nanobot system and experimental results serve as a guiding platform for high-accuracy evaluation in drug manufacturing development.

Reinforcement learning-driven proximal policy optimization-based voltage control for PV and WT integrated power system

The depletion of conventional fuel reserves and the high carbon emissions have alarmed the energy sector drivers. The integration of renewable energy resources (RERs) towards power system transformation is an obvious approach for a new-zero future. However, this integration also brings challenges in terms of voltage stability and power losses due to the variable output of RERs. To address these challenges, this work proposes a novel approach utilizing photovoltaic (PV) inverters and static var compensators (SVCs) for reactive power control in power distribution networks (PDNs). It enhances voltage stability and minimizes power losses. The proposed study deploys a proximal policy optimization (PPO) algorithm for real-time communication and control between reactive power devices. Performance evaluation was made on an IEEE-33 Bus system to demonstrate the effectiveness of the proposed scheme in integrating RER-based distributed generators (DGs). The proposed system achieved 68 % voltage control, keeping the voltage within a certain range of ±5 % while minimizing power losses. The proposed system also reduced the voltage out-of-control ratio to 0.044, which indicates minimum voltage deviation from the standard value. The proposed study provides a promising solution for controlling the voltage of DGs integrated PDN, which can potentially enhance the efficiency and reliability of the power system.

Optimal and Quasi-Optimal Automatic Tuning of Vibration Neutralizers

Vibration neutralizers are single-degree-of-freedom devices affixed to vibrating structures in order to reduce the response at a specific troublesome harmonic excitation frequency. As this frequency may vary over time, it becomes imperative to track and adjust the neutralizer to maintain the optimal performance. Recent years have witnessed the emergence of adaptive tunable vibration neutralizers, offering real-time adjustment capabilities through external actions. Thanks to real-time control algorithms, these devices enable the automatic mitigation of vibration levels in mechanical structures. A particularly successful algorithm for the automatic tuning of these devices leverages the phase angle between the base acceleration and the neutralizer’s mass. This study critically examines the justification for employing such an algorithm and scrutinizes its optimal applicability limits, particularly in the context of viscous and structurally damped systems. The findings reveal that this algorithm accurately approximates optimum tuning for systems with low damping. Moreover, from an engineering perspective, the algorithm remains acceptable even for heavily damped structures. Through a focused and comprehensive analysis, this paper provides valuable insights into the efficacy and limitations of the phase-angle-based tuning algorithm, contributing to the advancement of adaptive vibration control strategies in smart structures.

Model Development for Off-Road Traction Control: A Linear Parameter-Varying Approach

The number of highly automated machines in the agricultural sector has increased rapidly in recent years. To reduce their fuel consumption, and thus their emission and operational cost, the performance of such machines must be optimized. The running gear–terrain interaction heavily affects the behavior of the vehicle; therefore, off-road traction control algorithms must effectively handle this nonlinear phenomenon. This paper proposes a linear parameter-varying model that retains the generality of semiempirical models while supporting the development of real-time state observers and control algorithms. First, the model is derived from the Bekker–Wong model for the theoretical case of a single wheel; then, it is generalized to describe the behavior of vehicles with an arbitrary number of wheels. The proposed model is validated using an open-source multiphysics simulation engine and experimental measurements. According to the validated results, it performs satisfactorily overall in terms of model complexity, calculation cost, and accuracy, confirming its applicability.

Traffic light control with reinforcement learning

Urban traffic signal optimization is important for alleviating congestion in urban transportation systems. This study proposes a real-time traffic light control algorithm based on deep Q learning with a reward function that accounts for queue lengths, delays, travel times, and throughput. The model dynamically decides phase changes based on current traffic conditions. The training of the deep Q network involves an offline stage from pre-generated data with fixed signal timing and an online stage using real-time traffic data. A deep Q network structure with aphase gate component is used to simplify the model's learning task under different phases. Amemory palace" mechanism is used to address sample imbalance during the training process. Both synthetic and real-world traffic flow data are used to validate our approach under an urban road intersection scenario in Hangzhou, China. Results demonstrate significant performance improvements of the proposed method in reducing vehicle waiting time (57.1% to 100%), queue lengths (40.9% to 100%), and total travel time (16.8% to 68.0%) compared to traditional fixed signal timing plans.

Optimizing encrypted control algorithms for real-time secure control

In network control systems (NCSs), one obvious advantage of using homomorphic encryption is to protect data privacy while remotely computing the control function without decryption. Homomorphism analysis shows that it is more secure to protect the control system with fully homomorphic encryption (FHE) than with partially homomorphic encryption (PHE). However, the high computational complexity of FHE hinders its acceptance in NCSs. In this paper, FHE is utilized to highly enhance the security of NCSs against potential eavesdropping attacks. To guarantee control efficiency, an optimization scheme of the encrypted control algorithm is designed to make FHE practically applicable for real-time control. Besides, the secure controller achieves the expected control performance and inherits asymptotic stability. The experimental results can demonstrate the effectiveness of the proposed encrypted control system in terms of efficiency, security, and stability.

Novel and Robust Forward Kinematic Algorithm for Real-Time Control of General Six-Degree-of-Freedom Parallel Robot for Tele-Manipulation and Tele-Navigation

Abstract The forward kinematics (FK) of a 6-6 universal-prismatic-spherical (UPS) structure of a parallel robot is highly nonlinear, coupled, and has a one-to-many nature of mapping. There exists no close form solution to a forward kinematic problem (FKP), and real-time kinematic control is extremely difficult. This paper presents the implementation of time efficient and robust solution for FKP using a trajectory modifier algorithm along with a Newton Raphson (NR) method. One micrometer in translation and 0.001 deg in orientation accuracy with an average pose computation time of 2.3 ms are achieved. The novel algorithm is elaborated and the detailed performance parameters are tabulated. The paper presents trajectory following experiments to show robust, real-time FK solution and efficient kinematic control on both standalone and master–slave modes to be used for robot-assisted neurosurgery. The neuro-registration using the FK solutions in real time in a tele-manipulation mode is demonstrated.

PV-OPTIM: A Software Architecture and Functionalities for Prosumers

The future development of the energy sector is influenced by Renewable Energy Sources (RES) and their integration. The main hindrance with RES is that their output is highly volatile and less predictable. However, the utility of the RES can be further enhanced by prediction, optimization, and control algorithms. The scope of this paper is to disseminate a smart Adaptive Optimization and Control (AOC) software for prosumers, namely PV-OPTIM, that is developed to maximize the consumption from local Photovoltaic (PV) systems and, if the solar energy is not available, to minimize the cost by finding the best operational time slots. Furthermore, PV-OPTIM aims to increase the Self-Sustainable Ratio (SSR). If storage is available, PV-OPTIM is designed to protect the battery lifetime. AOC software consists of three algorithms: (i) PV Forecast algorithm (PVFA), (ii) Day Ahead Optimization Algorithm (DAOA), and (iii) Real Time Control Algorithm (RTCA). Both software architecture and functionalities, including interactions, are depicted to promote and replicate its usage. The economic impact is related to cost reduction and energy independence reflected by the SSR. The electricity costs are reduced after optimization and further significantly decrease in case of real-time control, the percentage depending on the flexibility of the appliances and the configuration parameters of the RTCA. By optimizing and controlling the load, prosumers increase their SSR to at least 70% in the case of small PV systems with less than 4 kW and to more than 85% in the case of PV systems over 5 kW. By promoting free software applications to enhance RES integration, we estimate that pro-environmental attitude will increase. Moreover, the PV-OPTIM provides support for trading activities on the Local Electricity Markets (LEM) by providing the deficit and surplus quantities for the next day, allowing prosumers to set-up their bids.

Robot Real-time Pedestrian Tracking Algorithm based on Deep Learning

In recent years, deep learning models have greatly improved the performance of pedestrian detection and tracking. Although the accuracy of neural network-based modeling methods is high, they often require large computational and storage resource overheads, which makes them difficult to apply to robots with high resource requirements. Pedestrian tracking for robots still faces great challenges in the problems of occlusion, multi-target, and target loss. In this thesis, we focus on solving the problem of real-time pedestrian tracking by lightweight fusion of deep learning target detection models for robots, firstly, through the lightweight YOLO network, we perform pedestrian detection and feature extraction, and then we propose a Gaussian mixture model based feature matching method to construct the target pedestrian tracker, and finally, we use the PID-based control algorithm for real-time control of the robot's motion, and finally realize the real-time pedestrian Tracking. In this thesis, we validate the feature matching method based on the Gaussian mixture model on the ETH dataset, and at the same time, combined with the motion control algorithm, we carry out the actual validation, and the experimental results show that our proposed method can realize the real-time pedestrian tracking.

Identification of MHD modes on EAST using a deep learning framework

The improvement of plasma parameters is severely limited by magnetohydrodynamic (MHD) instabilities. The identification of MHD modes is crucial for the study and control of MHD instabilities. In this study, an MHD mode identifier is developed based on a temporal convolutional network and long short-term memory (LSTM) network. The identifier is trained and tested on a small dataset containing 33 shots. Firstly, the temporal convolutional network encodes 27 diagnostic signals and then decodes them using LSTM network with different parameters to obtain the MHD modes and their frequency and intensity. The identifier exhibits an accuracy of approximately 98.38% on the test set and can accurately calculate the frequency and intensity of the MHD modes. To further examine the performance of the identifier, seven shots outside the dataset are used for shot-by-shot testing. The identifier can accurately identify the time period of tearing modes, and the identification accuracies of 2/1 and 3/2 tearing modes are 92.7% and 100%, respectively. The identification accuracy of the fishbone mode is slightly worse, only 82.1%. This is because the fishbone mode occurs intermittently. The frequent switching between the fishbone mode and no MHD behavior affects the identification of the fishbone mode. Overall, through the training of the small datasets, the identifier exhibits a good identification performance for the MHD modes. The proposed data-driven identifier can serve as a reference for establishing a large MHD mode database of EAST as well as a real-time MHD identification and control algorithm.

Two-stage robust scheduling and real-time load control of community microgrid with multiple uncertainties

With the increasing popularity of wind and solar generation, the stable and economic operation of the community microgrid system become more challenging. This paper is concerned with the robust scheduling and real-time control of demand response (DR) air conditioning (AC) loads for community microgrid. Firstly, a two-stage robust scheduling model coupled by the day-ahead dispatch and real-time dispatch is established by considering the uncertainties of wind/photovoltaic power generation and loads. Moreover, the incorporation of multiple interval uncertain sets is introduced to characterize the uncertainties related to wind power, photovoltaics, and load, thereby reducing the conservativeness of the model. Secondly, the real-time control of AC loads by adjusting the on/off actions and temperature setpoints with limited impact on user comfort so as to track the optimized electricity consumption curve. Finally, the simulation results show the effectiveness and robustness of the proposed optimization dispatch and real-time control algorithm.

Development of a Real-Time Tractor Model for Gear Shift Performance Verification

Verification of the system is essential during the development of a tractor; however, there are cost and time limitations when verification is performed on an actual tractor. To solve this problem, we developed a tractor model for real-time simulation to verify the gear shift performance of the tractor and evaluate the control algorithm. This study examined and modeled a dual-clutch transmission (DCT)-type 105 kW class tractor. The proportional control valve, synchronizer, and clutch were modeled to accurately implement the shift behavior, and the developed individual model was verified based on actual individual product test data. The 45 s driving simulation was conducted to confirm whether real-time simulation of the entire developed tractor model was possible and whether it simulated the behavior of the target tractor well. The driving simulation results confirmed that the driving speed of the tractor model matched the engine speed, transmission gear ratio, and tractor specifications, and the gear shift performance of the tractor model according to the number of gears was confirmed. The simulated model thus satisfies the characteristics of the target tractor and can be used to verify the gear shift performance, indicating that the model can verify the performance of the control algorithm in real time.

High-speed polarization tracking using thin film lithium niobate integrated dynamic polarization controller.

Dynamic polarization controllers (DPCs) are essential devices in various optical applications. We develop a thin film lithium niobate (TFLN) integrated DPC driven by the real-time implemented Jacobian control algorithm for fast polarization tracking. Experimental results demonstrate a high polarization tracking speed of 100 krad/s when targeting a specific linear state of polarization, with a low control loop delay of 420 ns, half-wave control voltages of 2.75 V, and a fast polarization restoring time of 1.6 us. Compared to previously reported integrated DPCs, the TFLN-based DPC achieves significantly higher tracking speed and lower loop delay. The results highlight the effectiveness of the Jacobian method and the outstanding performance of TFLN-based DPCs. The study opens up possibilities for further advancements in DPC solutions using TFLN technology.