Supervisory Control Level Research Articles

An Optimal Wind Farm Operation Strategy for the Provision of Frequency Containment Reserve Incorporating Active Wake Control

This study proposes a novel operation strategy for wind farms' optimal Frequency Containment Reserve (FCR) provision that simultaneously distributes FCR and optimally controls wake formation. The power reserve allocation is dynamically decided at the wind farm supervisory control level, considering the intermittent wind power and direction, grid frequency stochasticity, and the aerodynamic complexity of the wake. A two-stage stochastic programming approach supports decision-making for an optimal contribution to day-ahead energy/FCR markets considering sub-hourly wind power and grid frequency uncertainty. Moreover, a novel method is used to reduce the computational complexity by employing a data-driven surrogate model of wake formation in the optimizer. This surrogate model consists of a neural network trained on the Gauss-Curl-Hybrid wake model in FLORIS. This deep learning approach allows fast estimation of the wake control parameters, i.e., the optimal yaw angles and axial induction factors. Then, a coevolutionary-based multi-objective particle swarm optimization searches for the optimal deloading of the WTs and maximizes the total power production and kinetic energy while minimizing wake. The performance of the proposed algorithm is evaluated on the C-Power wind farm layout in the North Sea. Simulation results demonstrate its effectiveness in improving the wind farm's overall performance for different operational conditions.

Cyberphysical System Modeled with Complex Networks and Hybrid Automata to Diagnose Multiple and Concurrent Faults in Manufacturing Systems

Cyber–physical systems use digital twins to provide advanced monitoring and control functions, including self-diagnosis. The digital twin is often conceptualized as a 3D model, but mathematical models implemented in numerical simulations are required to reproduce the dynamical and functional characteristics of physical systems. In this work, a cyber–physical system scheme is proposed to monitor and diagnose failures. The virtual system, embedded at the supervisory control level, combines concepts from complex networks and hybrid automata to detect failures in the hardware components and in the execution of the sequential logic control. An automated storage and retrieval system is presented as a case study to show the applicability of the proposed scheme. The functional test and the obtained results validate the implemented system that is shown to be capable of fault diagnosis and location in real time. The online execution of the digital twin present several advantages for diagnosing multiple concurrent failures in sensors, actuators, and the control unit. This approach can be incorporate into diverse manufacturing systems.

Benchmarking high performance HVAC Rule-Based controls with advanced intelligent Controllers: A case study in a Multi-Zone system in Modelica

The design, commissioning, and retrofit of heating, ventilation, and air-conditioning (HVAC) control systems are crucially important for energy efficiency. However, designers and control contractors adopt ad-hoc control sequences based on diffused and fragmented information; therefore, most of the existing control sequences are diverse and sub-optimal. ASHRAE Guideline 36 (GDL36), High-performance Sequences of Operation (SOO) for HVAC Systems, was thus developed to provide standardized and high-performance rule-based HVAC control sequences with the main focus on maximizing energy efficiency. However, only limited studies verify the performance of these high-performance rules-based control and most of these studies focus on the single-zone systems. In this Modelica-based simulation study, the high-performance rule-based control sequences from GDL36 were compared to the state-of-the-art intelligent controllers (i.e., optimization-based controller (OBC) and deep reinforcement-learning-based controller (DRLC)) in terms of the energy efficiency and thermal comfort. The performance evaluation was conducted in a medium-sized office building with a multi-zone Variable Air Volume (VAV) cooling system. The OBC and DRLC replaced the GDL36 airside supervisory level control loops. In other words, the optimal supply air temperature (SAT) and static differential pressure (DP) setpoints were determined by the optimization problems in OBC and trained control policy by DRLC. The OBC and DRLC were formulated to minimize the HVAC energy consumption and zone air temperature violation. The OBCs with different control intervals and DRLCs with different hyperparameters in the Proximal Policy Optimization (PPO) algorithm were exploited and fine-tuned. The simulation results show that the GDL36 has a comparable energy performance (within a 3% deviation) with DRLC in the cases with high or mild cooling loads. It also has a comparable energy performance (within a 3% deviation) with OBC in the case of a high cooling load. However, the case with GDL36 consumed 7% more energy in the testing shoulder period. For the thermal comfort metric, the GDL36 has slightly more zone air temperature violations in all testing scenarios compared to the two intelligent controllers. From this case study, we can conclude that the GDL36 has demonstrated its comparable performance in terms of energy efficiency and thermal comfort with the two intelligent controllers. The GDL36 airside SOO is good enough considering the complexity and tuning efforts of the intelligent controllers.

Control synthesis for multiple mobile robot systems

This work continues our study on the control synthesis for multiple mobile robot systems (MMRS). We assume a hybrid approach that comprises the supervisory control level, based on a discrete event model of MMRS, and the robot control level, based on a continuous time model of the robot motion. Our objective is to further develop the control concept towards its implementation in a real-world application – a testbed for a fleet of six laboratory robots. In the first part of the paper, we develop a methodology of the supervisory control synthesis, that employs the Petri net formalism and formally ensures the required logics of MMRS operation, as well as propose a relevant architecture of the supervisor. The second part is focused on the low-level robot control and control procedures enabling modification of the robot motion according to the supervisor’s decisions. A simulation case is presented that illustrates the operation of the system.

Modelling and control of an integrated high purity methanol distillation configuration

Process integration in distillation unit operations promises improved process efficiencies. However, to realize these benefits fit for purpose control structures are needed to ensure stable and on-specification operations. In practice, this requires the development of regulatory and advanced process controls accounting for nuances and intricacies of a given design and application. This manuscript focuses on developing a control structure for a novel industrial mass-integrated twin-column distillation configuration, where a multi-component crude methanol feed is refined into AA grade (ultra-high purity) methanol, while meeting many technical and economic requirements. To this end, a Digital Model (a dynamic process model) of the column configuration was developed and used to carry out a thorough dynamic analysis. Based on the understanding gained, a regulatory control layer was introduced to achieve stable operations. An advanced supervisory level control structure and a Model Predictive Control (MPC) structure were then developed and compared on the Digital model where both alternatives achieved on-specification (technical and economic) operations. It was illustrated that a well-designed control structure could achieve stable and on-specification control for this specific application. Further disturbance testing showed the MPC is marginal efficient than a supervisory control but has similar robustness.

Low global warming potential (GWP) refrigerant supermarket refrigeration system modeling and its application

• A high fidelity model for a low -GWP refrigerant supermarket refrigeration system was developed. • The model was validated by manufacture and lab testing data with accuracy within ±4%. • A near-optimal high side pressure was obtained through extensive simulation running of the model. • The model was also used to generate battery equivalent model for using in grid interactive controls. As an environmentally friendly low global warming potential (GWP) refrigerant, Carbon dioxide (CO 2 ) has continuously gained popularity and research attention as alternative refrigerant for supermarket refrigeration system. In this paper, to fulfill the increasing need of accurate Low-GWP supermarket refrigeration models for development of supervisory level control and optimization strategies, a high fidelity model is developed for CO 2 transcritical supermarket refrigeration system which includes compressor rack of low temperature (LT) compressors and medium temperature (MT) compressors, air-cooled gas cooler, evaporator, expansion valves and other auxiliary equipment. A resistance-capacity model structure is proposed to simulate the display cases. Semi-thermodynamic models are proposed to estimate reciprocating compressors volumetric efficiency and power consumption. The zone modeling approach is used for evaporator simulation, and air-cooled gas cooler is modeled with distributed modeling method. The expansion valve simulation is based on orifice flow model. To calibrate these models, both manufacture performance data and experimental data are used. The experiments are conducted with a full instrumental CO 2 supermarket refrigeration system installed in Oak Ridge National Lab Flexible Research Platform (FRP). The simulation model can predict the system performance, including power consumption, cooling capacity, mass flow rate, temperature, and pressure, with high accuracy (within ±4%) compared to experimental data. In addition, this developed model has been used to create the system optimum high side pressure for high side expansion valve control, and to generate wide operating range simulation data for developing the batter-equivalent commercial refrigeration system model which can be used in grid interactive control development.

Fault tolerant control of islanded AC microgrids under sensor and communication link faults using online recursive reduced-order estimation

Cyber-Physical Systems (CPSs) have become an inseparable part of the new industrial era referred to as Industry 4.0 the quintessential example of which are smart grids. With a cyber communication level integrated with the physical components, besides considerable merits came along some reliability and security concerns. To address these issues, there ought to be a supervisory level monitoring and control system to identify any faults within the system and balance out their potential damaging impacts. In this paper, we consider fault tolerant control (FTC) problem in one of the building blocks of smart grids i.e. microgrids. The FTC method is based on online recursive reduced-order model estimation and Klaman Filter (KF) residual generation. Then, two-sided cumulative sum (CUSUM) sequential change detection algorithm is employed for sensor fault or communication link failure detection. Lastly, a fault-tolerant logic is constructed to switch between actual data and the internal model for the control system.

A Supervisory Control System for Nanogrids Operating in the Stand-Alone Mode

This article presents a new droop-based supervisory-level controller for nanogrids (NGs) in the islanded mode of operation. The proposed control system is able to significantly improve the dynamics of power electronic interfaces in NGs. Unlike the existing techniques, the proposed controller takes into account the input-side (i.e., source-side) and the output-side (i.e., load-side) parameters as well as the dc-bus voltage dynamics. Theoretical analysis, simulation results, and experimental results confirm the feasibility of the proposed control system and demonstrate its superior performance.

Optimization of Fuel Economy Using Optimal Controls on Regulatory and Real-World Driving Cycles

<div class="section abstract"><div class="htmlview paragraph">In recent years, electrification of vehicle powertrains has become more mainstream to meet regulatory fuel economy and emissions requirements. Amongst the many challenges involved with powertrain electrification, developing supervisory controls and energy management of hybrid electric vehicle powertrains involves significant challenges due to multiple power sources involved. Optimizing energy management for a hybrid electric vehicle largely involves two sets of tasks: component level or low-level control task and supervisory level or high-level control task. In addition to complexity within powertrain controls, advanced driver assistance systems and the associated chassis controls are also continuing to become more complex. However, opportunities exist to optimize energy management when a cohesive interaction between chassis and powertrain controls can be realized. To optimize energy management along a given route, certain information such as the projected vehicle route, driver behavior, and battery charge level should be considered. In this paper, simulation models of a parallel P0/P4 hybrid electric vehicle are presented, which optimize powertrain controls using the Dynamic Programming approach. This virtual vehicle model is exercised through the HWFET and FTP-75 regulatory driving cycles to establish a performance baseline in a controlled driving environment. For comparison to off-cycle driving, the virtual vehicle is then also exercised through a real-world driving scenario over real-world roads, with similar trip characteristics to the regulatory tests, but with real traffic conditions during the day. This comparison provides insights into how optimized real-world fuel economy results can differ compared to the controlled testing environment, and how predictive powertrain controls can offer “in-situ” optimization of energy management.</div></div>

Implementation of a Robust Electric Brake Actuator Design Based on H-infinity Control Theory

This paper deals with the robust control system design and implementation of a brake actuator for a smart car. To deliver robust performance, an H∞ controller had been chosen for the task. This allows excellent disturbance rejection while requiring low computational needs. In order to realize the controller a nominal model of the system has been identified, then, the parameter uncertainties were taken into account to find the stabilizing controller. The brake system consists of a low level H∞ controller sustaining robustness, a mid-level serial compensator for effective setpoint tracking and a high level supervisory control logic to deliver a reliable system. The implementation was tested and verified on a test bench using rapid prototyping tools and HIL methods.

Multi-Objective and Multi-Rate Control of the Grinding and Classification Circuit with Electromagnetic Mill

The paper presents an innovative construction and installation of dry grinding in electromagnetic mill that provides a significant reduction of energy consumption when compared to traditional mills. The installation constitutes complex multi-objective plant. To gain control over all the objectives, hierarchical structure of control system was developed where different time-horizons involve multi-rate scheme of steering. The paper presents solutions for the direct regulation, supervisory control, and optimization level. The electromagnetic mill installation constitutes a unique setup that causes open loop instability and implies the application of novel control schemes at all of the levels. The paper presents these solutions and illustrates them with the results of experiments carried out on the electromagnetic mill installations.

An Integrated Framework on Characterization, Control, and Testing of an Electrical Turbocharger Assist

Electrical turbocharger assist is one of the most critical technologies in improving fuel efficiency of conventional powertrain vehicles. However, strong challenges lie in high efficient operations of the device due to its complexity. In this paper, an integrated framework on characterization, control, and testing of the electrical turbocharger assist is proposed. Starting from a physical characterization of the engine, the controllability and the impact of the electrical turbocharger assist on fuel economy and exhaust emissions are both analyzed. A multivariable robust controller is designed to regulate the dynamics of the electrified turbocharged engine in a systematic approach. To minimize the fuel consumption in real time, a supervisory level controller is designed to update the setpoints of key controlled variables in an optimal way. Furthermore, a cutting-edge experimental platform based on a heavy-duty diesel engine is built. The proposed framework has been evaluated in simulations, physical simulations, and experiments. Results are presented for the developed system and the proposed framework that demonstrate excellent tracking performance, high robustness, and the potential for improvements in fuel efficiency.

Hybrid Electric Vehicle Fault Tolerant Control

This paper investigates the supervisory-level, fault tolerant control of a 2004 Prius powertrain. The fault considered is an interturn short circuit (ITSC) fault in the traction drive (a surface mount permanent magnet synchronous machine (SPMSM) for which its rotor is part of the vehicle's driveline). ITSC faults arise from electrical insulation failures in the stator windings where part of a phase winding remains functional while the remaining decoupled windings form a self-contained loop. Because the permanent magnets on the rotor (driveline) shaft are able to induce very large eddy currents in this self-contained loop if its rotational velocity is left unchecked, the maximum allowable driveline speed, and consequently, vehicle speed, must be reduced to avoid exceeding the drive's operational thermal limits. A method for detecting these ITSC faults and the induced eddy current in an SPMSM using a moving horizon observer (MHO) is reviewed. These parameters then determine which previously computed, fault-level-dependent SPMSM input–output power efficiency map and maximum safe operating speed is utilized by the supervisory-level controller. The fault tolerant control is demonstrated by simulating a Prius over a 40 s drive velocity profile with fault levels of 0.5%, 1%, 2%, and 5% detected at the midpoint of the profile. For comparison, the Prius is also simulated without a traction motor fault. Results show that the control reduces vehicle velocity upon detection of a fault to an appropriate safe value.

The Model of Data Exchange and Data Storage in Multi-Level Technological Process Control Systems

The structure of a multilevel process control system (MPCS) with the organization of data exchange via a multiport memory based on a modular principle has been developed in this work. The use of a modular principle enables to effectively organize enhancement and modification of a system designed. The structure of the multilevel system for process control includes three levels: data acquisition and control actuators; supervisory level and process control; operator control and decision-making. The key feature of each MPCS level is its hardware and software components along with the problems to be solved by them. Firstly, the level of data acquisition and control actuators enables performing such functions as data acquisition, data accumulation from the sensors and data processing. This level employs hardware and software tools based on the microcontrollers of STM8 family. Secondly, the supervisory level and process control exhibits data acquisition, accumulation process control data, online data processing, forecasting of a behavior of processes and controlled objects. Data processing of this level reduces the volume of data that have to be transferred to the upper level and thereby lowers bandwidth requirements of communication channels. The hardware and software tools of this level must work in real time. Thirdly, at the level of operator control and decision-making the following tasks are performed: data acquisition, data storage and data processing; processing of video streams, recognition of images and scenes in vision systems; decision-making; synchronization of universal time in a distributed system; synchronization of operation of the distributed subsystems; visualization and representation of the implementation process. In addition, this level contains a SCADA system, the main function of which is to create an operator interface and collect data on process control. As a result of the research requirements for exchanging and storing data were formed, the structure of the device to store and exchange data was designed and multi-port memory controller was synthesized. The method of conflict-free parallel data exchange using multi-port memory was improved by matching the intensity of access to data with the intensity of data flow, which allows determining the necessary performance of the random access memory.

Characterisation, control, and energy management of electrified turbocharged diesel engines

The electrification of engine components offers significant opportunities for fuel efficiency improvements. The electrified turbocharger is one of the most attractive options since it recovers part of the engine exhaust gas mechanical energy to assist boosting. Therefore, the engine can be downsized through improved transient responsiveness. In the electrified turbocharger, an electric machine is mounted on the turbine shaft and changes the air system dynamics, so characterisation of the new layout is essential. A systematic control solution is required to manage energy flows in the hybrid system. In this paper, a framework for characterisation, control, and energy management for an electrified turbocharged diesel engine is proposed. The impacts of the electric machine on fuel economy and air system variables are analysed. Based on the characterisation, a two-level control structure is proposed. A real-time energy management strategy is employed as the supervisory level controller to generate the optimal values of critical variables, while a model-based multi-variable controller is designed as the low level controller to track the values. The two controllers work together in a cascade to address both fuel economy optimisation and battery state-of-charge maintenance. The proposed control strategy is validated on a high fidelity physical engine model. The tracking performance shows the proposed framework is a promising solution in regulating the behavior of electrified engines.

Hybrid-state driven autonomous control for planar bipedal locomotion

The focus of this paper is on the development of a human inspired autonomous control scheme for a planar bipedal robot in a hybrid dynamical framework to realize human-like walking projected onto sagittal plane. In addition, a unified modelling scheme is presented for the biped dynamics incorporating the effects of various locomotion constraints due to varying feet-ground contact states, unilateral ground contact force, contact friction cone, passive dynamics associated with floating base etc. along with a practical impact velocity map on heel strike event. The autonomous control synthesis is formulated as a two-level hierarchical control algorithm with a hybrid-state based supervisory control in outer level and an integrated set of constrained motion control primitives, called task level control, in inner level. The supervisory level control is designed based on a human inspired heuristic approach whereas the task level control is formulated as a quadratic optimization problem with linear constraints. The explicit analytic solution obtained in terms of joint acceleration and ground contact force is used in turn to generate the joint torque command based on inverse dynamics model of the biped. The proposed controller framework is named as Hybrid-state Driven Autonomous Control (HyDAC). Unlike many other bipedal control schemes, HyDAC does not require a preplanned trajectory or orbit in terms of joint variables for locomotion control. Moreover, it is built upon a set of basic motion control primitives similar to those in human walk which provides a transparent and easily adaptable structure for the controller. These features make HyDAC framework suitable for bipedal walk on terrain with step and slope discontinuities without a priori gait optimization. The stability and agility of the proposed control scheme are demonstrated through dynamic model simulation of a 12-link planar biped having similar size and mass properties of an adult sized human being restricted to sagittal plane. Simulation results show that the planar biped is able to walk for a speed range of 0.1–2 m/s on level terrain and for a ground slope range of +/−20 deg for 1 m/s speed.

Multi-agent supervisory control for optimal economic dispatch in DC microgrids

The deepening impact of global warming has stimulated numerous researchers to come up with alternative energy sources and transportation facilities that create fewer emissions. DC distribution systems are attractive candidates for adapting high penetration of distributed generation and electric vehicles. Accordingly, this work proposes a distributed power management scheme to enhance the reliability and economic performance of isolated dc microgrids. In the presented approach, the dc microgrid is assumed to contain both renewable and nonrenewable generating units. At lower loading conditions, the proposed scheme stimulates a power-sharing strategy among renewable sources to increase system reliability, whereas nonrenewable sources are kept operating at their lower power limits. Alternatively, renewable sources are allowed to operate at their maximum power limits while maintaining optimum economic operation among nonrenewable sources to meet higher loading conditions. To achieve these operational criteria, both renewable and nonrenewable DG units apply a consensus-based algorithm in a supervisory control level. Each DG unit exchanges information with its neighbors, thereby locally updating the no-load voltage setting of its primary control according to system voltage sensitivity analysis. The incorporation of DG droop-based primary control renders the proposed algorithms fully distributed with a reduced number of agents. The proposed algorithm has the additional advantage of restoring system voltage to its nominal value. The stability and convergence of the algorithm are analytically addressed, and real-time OPAL-RT simulations are performed in a hardware-in-the-loop (HIL) application to verify its effectiveness.

Real-Time Optimal Energy Management of Electrified Engines

The electrification of engine components offers significant opportunities for fuel economy improvements, including the use of an electrified turbocharger for engine downsizing and exhaust gas energy recovery. By installing an electrical device on the turbocharger, the excess energy in the air system can be captured, stored, and re-used. This new configuration requires a new control structure to manage the air path dynamics. The selection of optimal setpoints for each operating point is crucial for achieving the full fuel economy benefits. In this paper, a control-oriented model for an electrified turbocharged diesel engine is analysed. Based on this model, a structured approach for selecting control variables is proposed. A model-based multi-input multi-output decoupling controller is designed as the low level controller to track the desired values and to manage internal coupling. An equivalent consumption minimization strategy is employed as the supervisory level controller for real-time energy management. The supervisory level controller and low level controller work together in a cascade which addresses both fuel economy optimization and battery state-of-charge maintenance. The proposed control strategy has been successfully validated on a detailed physical simulation model.

Gas Turbine Engine Behavioral Modeling

This paper develops and validates a power flow behavioral model of a gas turbine engine (GTE) composed of a gas generator and free power turbine. The behavioral model is suitable for supervisory level (optimal) controller development of the engine itself or of electrical power systems containing gas-turbine-generator pairs as might be found in a naval ship or terrestrial electric utility plant. First principles engine models do not lend themselves to the supervisory level control development because of their high granularity. For the behavioral model, “simple” mathematical expressions that describe the engine's internal power flows are derived from an understanding of the engine's internal thermodynamic and mechanical interactions. These simple mathematical expressions arise from the balance of energy flow across engine components, power flow being the time derivative of energy flow. The parameter fit of the model to a specific engine such as the GE LM2500 detailed in this work utilizes constants and empirical fits of power conversion efficiencies obtained using data collected from a high-fidelity engine simulator such as the Gas Turbine Simulation Program (GSP). Transient response tests show that the two-norm normalized error between the detailed simulator model and behavioral model outputs to be 2.7% or less for a GE LM2500.

Development of PROTON Electric Vehicle Control Unit (eVCU) Using State Machine Deterministic Rule-Based Approach

There are many types of Electric Vehicle (EV) applied in automotive industry. It can be hybridization or electrification vehicle. Battery Electric Vehicle (BEV) also called as fully electric vehicle using batteries as the only source and an electric motor as a traction motor to move the vehicle. In a world view of BEV, it is still has circumstances that would strand the BEV to have huge commercialization due to range anxiety. This paper is discussing about an electrification vehicle power and energy management (PEM) strategy. PEM strategy has two layer control strategies that consist of low level component control and high level supervisory control. Management and control strategy in BEV is carefully designed due to heavy loads consumption from Propulsion Electrical Load (PEL) and Non Propulsion Electrical Loads (NoPEL) with a single energy source. This is to ensure that power and energy is managed at optimum level that will give some extension of wider kilometer of the vehicle. The BEV performance is typically controlled by high level supervisory control algorithm using event-based condition using state machine deterministic rule-based method. More than one drive mode to be determined in this paper as part of control strategy to get an optimal PEM performance.