Low-latency quantum control using AI algorithms on cryogenic microelectronic platforms
Italian Summer Students Final Presentations 2023
Russo, Marco [Polytech. Turin; Fermilab]↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Algorithms for Orbit Control on Spear
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Sebek, J.↗
A biomimetic approach to fast selection of optimal controlled variables using multiagent algorithms and a decomposition approach
Selection of primary controlled variables (CVs) with due consideration of economic loss and ease of control is an important step for achieving optimal plant operation under design and off-design conditions. However, the computational expense of the combinatorial optimization step that is undertaken for CV selection renders the existing approaches unaffordable for fast selection of optimal CVs. Two approaches are proposed in this paper for improving the computational efficiency of the optimization step. Typically, the CV selection process is typically executed by considering the entire plant together. Opposed to that, the first approach proposed here seeks to decompose the process plant into multiple partitions based on the connectivity strength. Then the CV selection algorithm can be executed on each partition independently. The decomposition algorithm is biomimetic and has been developed, in analogy to neuroscience, mimicking how the information about the connectivity information is extracted using a dynamic casual model. This approach can significantly decrease the number of combinatorial optimization problems that needs to be solved and facilitates parallelization of the solution. In the second approach, a multiagent optimization framework is proposed that utilizes metaheuristic optimization strategies such as the efficient ant colony algorithm, simulated annealing and the genetic algorithm. These two approaches can be used by themselves or in combination. This framework enables cooperative search by a group of algorithmic agents facilitated through an information sharing protocol. These two approaches are applied to a toy problem and an acid gas removal unit as part of an integrated gasification combined cycle. This study shows how the computational time and economic and control performance of the optimal CV sets vary as the number of partitions are changed.
42 ENGINEERING↗
Active Reaction Control of Cu Redox State Based on Real-Time Feedback from In Situ Synchrotron Measurements
Abstract not provided
Rakita, Yevgeny↗
Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building
This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Model Based Predictive Control for Frequency Support in Multi-port Autonomous Reconfigurable Solar Plants
Primary frequency support functionality can be an added advantage for any new grid integrated power electronics resource. With increased penetration of power electronics resources, inertial and primary frequency response capabilities are decreasing. In this research paper, an advanced model-based predictive control (MBPC) algorithm for providing frequency support to a new topology of integrated photovoltaic(PV), batterybased energy storage systems (ESS), and high-voltage direct current (HV dc) systems called multi-port autonomus reconfigurable solar (MARS) plants is based on the synchronverter-based control. The simulation of MARS-HV dc system with proposed control strategy is simulated and validated for MARS connected to low short circuit ratio (SCR) grids in PSCAD/EMTDC simulation environment. The control algorithm proposed showcases better performance in terms of frequency nadir improvement and frequency steady state improvemnt with respect to no frequency control.
Marthi, Phani Ratna Vanamali↗
Independent Blade Pitch Controller Design for a Three-Bladed Turbine Using Disturbance Accommodating Control
Two independent pitch controllers (IPCs) based on the disturbance accommodating control (DAC) algorithm are designed for the three-bladed Controls Advanced Research Turbine to regulate rotor speed and to mitigate blade root flapwise bending loads in above-rated wind speed. One of the DAC-based IPCs is designed based on a transformed symmetrical-asymmetrical (TSA) turbine model, with wind disturbances being modeled as a collective horizontal component and an asymmetrical linear shear component. Another DAC-based IPC is designed based on a multiblade coordinate (MBC) transformed turbine model, with a horizontal component and a vertical shear component being modeled as step waveform disturbance. Both of the DAC-based IPCs are found via a regulation equation solved by Kronecker product. Actuator dynamics are considered in the design processes to compensate for actuator phase delay. The simulation study shows the effectiveness of the proposed DAC-based IPCs compared to a proportional-integral (PI) collective pitch controller (CPC). Improvement on rotor speed regulation and once-per-revolution and twice-per-revolution load reductions has been observed in the proposed IPC designs.
17 WIND ENERGY↗
Verification of Energy Reduction Effect through Control Optimization of Supply Air Temperature in VRF-OAP System
This paper developed an algorithm that controls the supply air temperature in the variable refrigerant flow (VRF), outdoor air processing unit (OAP) system, according to indoor and outdoor temperature and humidity, and verified the effects after applying the algorithm to real buildings. The VRF-OAP system refers to a heating, ventilation, and air conditioning (HVAC) system to complement a ventilation function, which is not provided in the VRF system. It is a system that supplies air indoors by heat treatment of outdoor air through the OAP, as a number of indoor units and OAPs are connected to the outdoor unit in the VRF system simultaneously. This paper conducted experiments with regard to changes in efficiency and the cooling capabilities of each unit and system according to supply air temperature in the OAP using a multicalorimeter. Based on these results, an algorithm that controlled the temperature of the supply air in the OAP was developed considering indoor and outdoor temperatures and humidity. The algorithm was applied in the test building to verify the effects of energy reduction and the effects on indoor temperature and humidity. Loads were then changed by adjusting the number of conditioned rooms to verify the effect of the algorithm according to various load conditions. In the field test results, the energy reduction effect was approximately 15–17% at a 100% load, and 4–20% at a 75% load. However, no significant effects were shown at a 50% load. The indoor temperature and humidity reached a comfortable level.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Plasma Modeling Results and shape control improvements for NSTX
New control implementations and dynamics studies on the National Spherical Torus eXperiment (NSTX) are summarized. In particular, strike point (SP) position, X-point height and squareness control, and two new system-identification methods / control-tuning algorithms were put into operation.The PID controller for the SP was tuned by analyzing the step response of the SP position to the poloidal coil currents, employing the Ziegler-Nichols method. The resulting SP controller was successfully employed to achieve the “snowflake” divertor configuration in NSTX. An offline system identification of the plasma response to the control inputs based on ARMAX input-output models was implemented. With this tool, rough estimates of the improvements were realized and several control improvements were identified.An online automatic relay-feedback PID tuning algorithm was implemented, which has the advantage of tuning the controller in one shot, thus optimizing the use of experimental time. Furthermore, using these new capabilities, we implemented LLD scenarios with control for theall four upper/lower/inner/outer SPs and with a combined X-point height, SP radius control. Separately, an independent squareness control was implemented.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
ADMS Test Bed: AMI Data-Based Grid Controls
Develop algorithms for leveraging existing AMI infrastructure to provide a foundational, pervasive secondary voltage monitoring network solution.
ADMS test bed↗
Genetic Algorithm-Based Commutation Angle Control for Torque Ripple Mitigation in Switched Reluctance Motor Drives
This work addresses the application of the Genetic Algorithm (GA) technique to optimize the commutation angles of a 2 kW 8/6 switched reluctance machine (SRM). The primary goal is to reduce the well-known drawback of SRMs: the torque ripple. Firstly, the machine was modeled in Matlab /Simulink ® using lookup tables obtained via finite element method (FEM) simulations. Subsequently, the model was used to perform the GA routine aiming to find the optimal phase commutation angles that minimize the torque ripple factor. Notably, the torque performance of the SRM was significantly affected by the commutation angles during the search for the optimal solution. Afterwards, the GA results for four different operation points were verified experimentally through a developed drive platform with digital signal processor-based (DSP) control and an asymmetric bridge converter. As showed by the experiments, the proposed approach was suitable to reduce the torque ripple by more than 50% for one of the evaluated operating points. Furthermore, it was confirmed that the torque ripple mitigation led to acoustic noise improvement.
42 ENGINEERING↗
Beneficial Integration of Energy Storage and Load Management with Photovoltaic (PV)
In recent years, a number of industry activities have aimed at addressing the integration challenges posed by the variability and uncertainty of higher penetration of renewable generation sources, like solar photovoltaic (PV) – one of the key objectives of the Sustainable and Holistic Integration of Energy Storage and Solar PV (SHINES) program launched by the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). This EPRI led Beneficial Integration of Energy Storge, and Load Management with PV project aimed to design, develop, and demonstrate end-to-end distributed energy resource (DER) integration solution to build on these activities. EPRI led project team designed and implemented a local controller that uses model predictive control (MPC) algorithm to optimally manage DERs on site by planning for a receding horizon while executing the control settings for the first step of its plan. The team has also developed a system controller to interface with the local controller to demonstrate the hierarchical control and how it can leverage DER capabilities to address challenges like over voltage and thermal limit violations which typically limits the DER hosting capacity of distribution feeders. Team has demonstrated how the local controller with optimization algorithm can effectively manage controllable loads like HVAC, water heater, and pool pumps to allow for greater integration of PV with relatively smaller energy storage system requirements. Optimal utilization of the load control can also reduce the depth of discharge of batteries to meet grid export/import limit from behind-the-meter (BTM) DERs. Proper utilization of DER capabilities via local control intelligence, like the one developed and demonstrated in this project can help the industry to address integration challenges of higher penetration of solar PV in economically efficient manner. This can help to accelerate deployment of clean renewable energy systems at lower societal cost.
14 SOLAR ENERGY↗
Distributed Conditions for Small-signal Stability of Power Grids and Local Control Design
Operating modern power grids with stability guarantees is markedly important. Typical methods for analyzing and certifying power grid stability are largely centralized relying on the ability of the system operator to gather network-wide information and accurately compute the system's eigenvalues. These methods are oftentimes not privacy-preserving and computationally burdensome. They are therefore, not well-suited to modern power grids where small-signal stability has to be evaluated timely, efficiently and in a privacy-preserving fashion. Herein, we introduce a distributed methodology for certifying small-signal stability of power grids and designing the local controllers. First, we analytically derive distributed conditions for network-wide stability that bus agents can inspect using local information. By leveraging these conditions, we then introduce a distributed control design algorithm (DCDA) that can guide the local control design so that stability of the interconnected system is guaranteed. The agents that adopt the proposed distributed algorithm are responsible for tuning their local controllers, producing their local control commands and ensuring that their local stability condition is met. The system operator is only responsible for verifying network-wide stability upon receiving affirmative responses from all agents and, announcing, that the overall system is stable. The proposed DCDA algorithm is numerically validated via simulations using the IEEE 39-bus system.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Prevention of Overcurrent and Admissible Voltage Limit Violation in Electrical Network Nodes
In a centralized emergency control system (CECS) with a third-generation algorithm, emergency control actions (CA) are selected under conditions of preventing disruption of dynamic stability, providing the required margin of stability in post-emergency condition, as well as preventing overcurrent and restricting voltage in network nodes to admissible limits. A methodology for selecting CAs for these last two conditions is described.
42 ENGINEERING↗
Cyber-Physical System Implementation for Manufacturing With Analytics in the Cloud Layer
Effective and efficient modern manufacturing operations require the acceptance and incorporation of the fourth industrial revolution, also known as Industry 4.0. Traditional shop floors are evolving their production into smart factories. To continue this trend, a specific architecture for the cyber-physical system is required, as well as a systematic approach to automate the application of algorithms and transform the acquired data into useful information. This work makes use of an approach that distinguishes three layers that are part of the existing Industry 4.0 paradigm: edge, fog, and cloud. Each of the layers performs computational operations, transforming the data produced in the smart factory into useful information. Trained or untrained methods for data analytics can be incorporated into the architecture. A case study is presented in which a real-time statistical control process algorithm based on control charts was implemented. The algorithm automatically detects changes in the material being processed in a computerized numerical control (CNC) machine. The algorithm implemented in the proposed architecture yielded short response times. The performance was effective since it automatically adapted to the machining of aluminum and then detected when the material was switched to steel. The data were backed up in a database that would allow traceability to the line of g-code that performed the machining.
97 MATHEMATICS AND COMPUTING↗
Tracking Error Learning Control for Precise Mobile Robot Path Tracking in Outdoor Environment
This article presents a Tracking-Error Learning Control (TELC) algorithm for precise mobile robot path tracking in off-road terrain. In traditional tracking error-based control approaches, feedback and feedforward controllers are designed based on the nominal model which cannot capture the uncertainties, disturbances and changing working conditions so that they cannot ensure precise path tracking performance in the outdoor environment. In TELC algorithm, the feedforward control actions are updated by using the tracking error dynamics and the plant-model mismatch problem is thus discarded. Therefore, the feedforward controller gradually eliminates the feedback controller from the control of the system once the mobile robot has been ontrack. In addition to the proof of the stability, it is proven that the cost functions do not have local minima so that the coefficients in TELC algorithm guarantee that the global minimum is reached. The experimental results show that the TELC algorithm results in better path tracking performance than the traditional tracking error-based control method. The mobile robot controlled by TELC algorithm can track a target path precisely with less than 10 cm error in off-road terrain.
42 ENGINEERING↗