Supervisory Control Algorithm Research Articles

Maximum Demand Reduction (MDRed) modelling using MATLAB Stateflow®

Based on renewable energy case studies, the MDRed (Maximum Demand Reduction) Model is created as an optimization apparatus for the solar PV-battery system. In this paper, MATLAB modeling toolbox is used for simulation of the solar PV-BESS design and verification of the MDRed modeling via variations in MD limit and solar PV load. Based on MDRed model, the maximum demand supervisory control algorithm is developed via MATLAB Stateflow® for the battery management system (BMS) to monitor and regulate the load to maintain the net load consumption below MD limit with the solar PV system. In summary, the simulation with the master MD controller shows the effectiveness of the proposed controller on the BMS to cater to MD reductions during solar irradiance and load pattern variations.

Comparative Assessment of Supervisory Control Algorithms for a Plug-In Hybrid Electric Vehicle

The study examines alternative on-board energy management system (EMS) supervisory control algorithms for plug-in hybrid electric vehicles. The optimum fuel consumption was sought between an equivalent consumption minimization strategy (ECMS) algorithm and a back-engineered commercial rule-based (RB) one, under different operating conditions. The RB algorithm was first validated with experimental data. A method to assess different algorithms under identical states of charge variations, vehicle distance travelled, and wheel power demand criteria is first demonstrated. Implementing this method to evaluate the two algorithms leads to fuel consumption corrections of up to 8%, compared to applying no correction. We argue that such a correction should always be used in relevant studies. Overall, results show that the ECMS algorithm leads to lower fuel consumption than the RB one in most driving conditions. The difference maximizes at low average speeds (<40 km/h), where the RB leads to more frequent low load engine operation. The two algorithms lead to fuel consumption differences of 3.4% over the WLTC, while the maximum difference of 24.2% was observed for a driving cycle with low average speed (18.4 km/h). Further to fuel consumption performance optimization, the ECMS algorithm also appears superior in terms of adaptability to different driving cycles.

A self-adjusting and modular supervisory control algorithm for planar dexterous manipulation

Many applications require precise handling and manipulation of delicate objects. In some cases, the object must be transported to a new location following a strict travel path including time-related constraints. This paper presents a self-adjusting modular control algorithm for dexterous manipulation of planar objects using multiple manipulators with precise path and timing deliveries. The popular caging approach is simple, and usually effective when manipulating objects with multiple devices but can fail following complex paths with orientation adjustments under time-critical tracking requirements. The proposed approach exploits the dynamics of the object in real-time using tracking control and allocates the force that needs to be applied by each manipulator based on their current position around the object to maximize their capability to push in the direction of the contact angle. The new algorithm is self-adjusting and modular; It can adjust its force allocation according to configuration changes during operation, and manipulators execute the same algorithm regardless of their number. The advantages of the new approach are successfully demonstrated both with simulations and testbed experiments, including orientation tracking, which is not typically featured with the caging approach. Conditions to check when the new algorithm is most effective are also analyzed. The closed-loop stability and performance of the new algorithm are also studied and necessary conditions are identified.

Decentralized modular hybrid supervisory control for the formation of unmanned helicopters

Formation control of Unmanned Aerial Vehicles (UAVs) requires them to tightly cooperate to reach and keep the formation, while avoiding collision. This paper proposes a novel decentralized hybrid supervisory control approach for the formation control of multiple UAVs. This is achieved by developing a symbolic motion planning technique to polarly partition the motion space resulting in a finite state discrete event model for the motion dynamics of each UAV. Then, a modular discrete supervisor is designed for different components of the formation mission including reaching the formation, keeping the formation, and collision avoidance. Further, for the collision avoidance mechanism, a novel top-down decomposition-based approach is developed to design local supervisors decentralizedly. It is formally proved that with the proposed top-down decomposition-based approach, the (locally) supervised UAVs, as a whole, can cooperatively satisfy the desired (global) collision avoidance specification. The proposed decentralized supervisory control algorithm is also verified through a hardware-in-the-loop simulator for the formation control of unmanned helicopters.

Analysis and Design of a Smart Controller for Managing Penetration of Renewable Energy Including Cybersecurity Issues

This article presents an optimal distributed energy resource management system for a smart grid connected to photovoltaics, battery energy storage, and an electric vehicle aggregator. These management systems are one of the key factors for the optimal control of power converters connected to the grid. The proposed management system includes the communication architecture necessary for realizing the information flow between the individual control of the distributed generators and the master supervisory control algorithm. The work carried out on two levels is first to design a control strategy for energy management and validate it with the grid in real-time hardware-in-the-loop simulation integrating the IEC61850 communication layer and physical intelligent electronic devices. The second is to analyze the vulnerabilities of the designed methodology for cybersecurity threats explicitly with the extension of IEC61850 to electric vehicle aggregators for communication with the master energy management. A man-in-the-middle attack conducted in the supervisory communication layer enabled us to investigate the effects of such an attack on the performance and operation of the smart electric grid.

Supervisory Control of Timed Discrete-Event Systems With Logical and Temporal Specifications

A novel framework is introduced for the supervisory control (SC) of timed discrete event systems based on Time Petri nets. The method encompasses both logical (markings to reach or avoid) and temporal specifications (arrival and departure times in specific markings). It relies on the construction of a partial forward reachability graph of the modified state class graph type and the formulation of integer linear programming problems to establish suitable firing time intervals (FTIs) for the controllable transitions. For each enabled controllable transition, the SC algorithm provides the largest FTI that that the specifications are met, irrespectively of the firing times of the uncontrollable transitions.

Predicting Human Control Adaptation from Statistical Variations in Tracking Error and Error Rate

This paper presents the results of an experiment that was performed to verify the ‘supervisory control algorithm’, a well-known model of human operator adaptation to changes in controlled element dynamics. This model proposes that human adaptive behavior is triggered once the magnitudes of the tracking error or error rate exceed certain decision region limits. In the experiment, a compensatory tracking task with a sudden transition in the controlled element dynamics, as also tested in other recent experiments, was performed by six skilled participants. In addition to performing the control task, participants had to indicate with a button press when they detected a controlled element transition. The results indicate that the published detection limits for the ‘supervisory control algorithm’ are too conservative for our experiment data, as measured detections could be related to error or error rate occurrences that exceeded 2-6 times their respective pre-transition standard deviations. The effectiveness of new detection limits proportional to these pre-transition standard deviations was tested. The best match to our experiment data was obtained with limits at 3.9σ, for which in only 9.38% and 11.5% of cases a (false positive) too early detection or a (false negative) missed detection occurred, respectively. Overall, these results demonstrate that human operator adaptation can indeed be effectively predicted from statistical variations in tracking error and error rate.

Development of EMS for Hybrid Electric Two-Wheeler

Rising the emission level from conventional vehicles (CV) is a major concern of today’s world. Electric vehicle (EV) and hybrid electric vehicles (HEV) technology are two ways to deal with it. EV technology is an emerging technology but they have some challenges in adopting the EV technology. So, this paper deals with modelling and simulation of a hybrid electric vehicle such as series hybrid electric vehicle and parallel hybrid electric vehicle with different energy management strategies. The heart of HEV development is energy management strategy (EMS), on which vehicle’s performance and fuel consumption can be optimized. For series HEV charge depleting charge sustaining (CDCS) EMS has been used and for parallel HEV rule-based (RB) EMS has been used. Supervisory control algorithms play important role to allow smooth power flow between the engine and electric source. There are different EMSs such as rule-based, optimization-based (OB), and learning-based (LB). These EMSs are used to split the power flow. In this paper, fuzzy logic controller (FLC) as an energy management strategy is proposed for a parallel hybrid electric vehicle, further this strategy is compared with RBEMS. The simulation results are compared and analyzed which shows there is improvement in range and reduction fuel consumption. In this study, MATLAB/Simulink and QSS toolbox are used for the simulation.

Comprehensive powertrain modeling for heavy-duty applications: A study of plug-in hybrid electric bus

A comprehensive forward-looking powertrain model with an efficiency-based control strategy was developed to achieve real-time optimization of plug-in hybrid electric buses while considering the real vehicle drivability and the practical operation of all powertrain components under real driving conditions. The control strategy is based on a supervisory control algorithm that alternately employs charge-dominant control and discharge-dominant control to manage multiple powertrain sources, and enable an optimal overall efficiency-based powertrain operation state by maximizing the inherent optimal powertrain efficiency through best all transmission gear choice and the blend of motor and/or engine power. In the model, a component energy efficiency database was developed to rapidly enable an smart and optimal operating state determined from a set of efficiency maps characterizing the component and powertrain control states as a function of the instantaneous vehicle operational condition. The results revealed that the energy savings achieved with the innovative powertrain control improved by 10%–30% compared with the baseline hybrid powertrain control strategy. The benefits of eco-driving with respect to energy consumption were also evaluated using the powertrain model. The powertrain control model was implemented into a real hybrid bus. Measured energy savings with the optimized control strategy were similar to the simulated results.

Supervisory Control for NO$_\text{x}$ Emission Compliance of Heavy-Duty Vehicles

This article provides a detailed description of a supervisory control problem for a heavy-duty vehicle equipped with a diesel engine and an exhaust aftertreatment system (EATS). The control problem considers the coupling between the engine and EATS and assumes the availability of predictive driving information for a long horizon. The article also proposes a computationally efficient supervisory control algorithm, which is implemented in an existing engine electronic control unit. The algorithm is based on distributed economic nonlinear MPC. It minimizes total fuel consumption (by the diesel engine and EATS) and fulfils Euro VI real-driving and world harmonized transient cycle based NO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_\text{x}$</tex-math></inline-formula> emission legislation for the heavy-duty diesel vehicle. The performance of the algorithm is evaluated using a high-fidelity simulation platform, which includes a validated GT-POWER model of a 13 L turbo compound diesel engine and the first-principles model of an EATS. Simulation results show that the supervisory controller has 1.7% less total fuel consumption, and 88.4% less NH <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_3$</tex-math></inline-formula> slip, compared to a baseline controller, to achieve the same real-driving NO <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$_\text{x}$</tex-math></inline-formula> emission.

Autonomous landing of a quadrotor on a moving platform using vision-based FOFPID control

AbstractThis research deals with the autonomous landing maneuver of a quadrotor unmanned aerial vehicle (UAV) on an unmanned ground vehicle (UGV). It is assumed that the UGV moves independently, and there is no communication and collaboration between the two vehicles. This paper aims at the design of a closed-loop vision-based control system for quadrotor UAV to perform autonomous landing maneuvers in the possible minimum time despite the wind-induced disturbance force. In this way, a fractional-order fuzzy proportional-integral-derivative controller is introduced for the nonlinear under-actuated system of a quadrotor. Also, a feedback linearization term is included in the control law to compensate model nonlinearities. A supervisory control algorithm is proposed as an autonomous landing path generator to perform fast, smooth, and accurate landings. On the other hand, a compound AprilTag fiducial marker is employed as the target of a vision positioning system, enabling high precision relative positioning in the range between 10 and 350 cm height. A software-in-the-loop simulation testbed is realized on the windows platform. Numerical simulations with the proposed control system are carried out, while the quadrotor system is exposed to different disturbance conditions and actuator dynamics with saturated thrust output are considered.

Adaptive Parameter Modulation of Deep Brain Stimulation Based on Improved Supervisory Algorithm.

Clinically deployed deep brain stimulation (DBS) for the treatment of Parkinson’s disease operates in an open loop with fixed stimulation parameters, and this may result in high energy consumption and suboptimal therapy. The objective of this manuscript is to establish, through simulation in a computational model, a closed-loop control system that can automatically adjust the stimulation parameters to recover normal activity in model neurons. Exaggerated beta band activity is recognized as a hallmark of Parkinson’s disease and beta band activity in model neurons of the globus pallidus internus (GPi) was used as the feedback signal to control DBS of the GPi. Traditional proportional controller and proportional-integral controller were not effective in eliminating the error between the target level of beta power and the beta power under Parkinsonian conditions. To overcome the difficulties in tuning the controller parameters and improve tracking performance in the case of changes in the plant, a supervisory control algorithm was implemented by introducing a Radial Basis Function (RBF) network to build the inverse model of the plant. Simulation results show the successful tracking of target beta power in the presence of changes in Parkinsonian state as well as during dynamic changes in the target level of beta power. Our computational study suggests the feasibility of the RBF network-driven supervisory control algorithm for real-time modulation of DBS parameters for the treatment of Parkinson’s disease.

Droop control design to minimize losses in DC microgrid for more electric aircraft

Droop control has been widely used as a load-sharing method between paralleled power sources in DC microgrid due to its modularity and reliability. Existing droop gains design methods rely on computationally intensive supervisory control algorithms and knowledge of sub-system parameters.This paper presents a streamlined design approach for optimal droop gains, relying only on the knowledge of the parameters of the local converter power losses model in order to achieve minimum power losses for a More Electric Aircraft (MEA) DC microgrid. Additionally, a simplified, but sufficiently accurate, converter losses model is proposed in this paper for optimal droop gains design. The proposed converter losses model consists of two parts; no-load losses, and losses which are represented by Equivalent Series Resistance (ESR). The proposed design approach analyses show that setting the optimal droop gains equal to the converter ESR will achieve minimum overall DC microgrid power losses without the need for any additional information on the DC transmission line parameters. The effectiveness of the optimal droop gains design method is tested in a simulation environment and evaluated experimentally using a laboratory DC microgrid test rig.

Optimal Management Energy System and Control Strategies for Isolated Hybrid Solar-Wind-Battery-Diesel Power System

This paper proposes optimal control strategies of a standalone Hybrid Power System (HPS) to supply sustainable and optimal energy to an isolated site with improved quality of electrical energy. A topology of Isolated Hybrid Power System (IHPS) is proposed, consists of: a Photovoltaic System (PVS), a Wind Energy Conversion System (WECS), electronic power devices controlled to maximize energy production from renewable sources and to maintain the constant DC-link voltage, a Battery Energy Storage System (BESS), Diesel Generator (DG), and a Pulse Width Modulation (PWM) Voltage Source Inverter (VSI) located at the load-side end. In addition, a novel control strategy has been proposed, in this work, to maximize the power from the PVS. This presented strategy, based on the combination between Perturb and Observe (P&amp;O) algorithm and the Fuzzy PI Controller (FPIC), presents a good performance, especially in the dynamic state compared to the classical algorithm P&amp;O. A supervisory control algorithm has been elaborated to manage the energy flows between the devices of the hybrid system to make the decision of the optimal operating mode in order to ensure a continuous supply of the load with minimum usage of batteries and DG. The simulation results developed in the Matlab/Simulink environment are applied to show the efficiency and performance of the proposed control strategies in terms of power optimization and energy management. Doi: 10.28991/esj-2021-01262 Full Text: PDF

Effective power transfer and reduced-order generalized integrator sequence based fault ride through strategy in grid connected DFIG based WECS

This paper deals with the control strategies of Doubly Fed Induction Generator (DFIG) based grid connected Wind Energy Conversion System (WECS) delivering power during normal and grid fault conditions. The system encompasses DFIG, Rotor Side Converter (RSC) and Grid Side Converter (GSC) connected back-to-back with a dc link, utility grid and local loads at point of common coupling. During normal system conditions, Tip Speed Ratio basedMaximum Power Point Tracking and Instantaneous Power Theory based reference current generation technique are employed for RSC, and GSC respectively to achieve maximum bidirectional power transfer and meeting the demand. Also, the power transfer operations during occasional conditions such as overloading and shorted stator winding are investigated. During grid fault conditions, a Reduced Order Generalized Integrator (ROGI) based Sequence Controller is employed to achieve stabilized real and reactive power injection to the grid by extracting the positive and negative sequence components of the grid voltages and currents. A supervisory control algorithm is developed for effecting a smooth transition of control schemes thereby avoiding system disturbances. The proposed control strategy is implemented in a simulation environment of a 10 MW grid connected DFIG based wind farm in MATLAB 2018b version. Various case studies are performed to validate the effectiveness of the proposed control strategies. The system is subjected to all possible grid faults including the most severe symmetrical fault and the controller is found to provide better grid support with appropriate real power transfer and reactive power injection. Also, the dynamic performance of the control strategy is experimentally investigated in the hardware setup of 1 kW grid connected DFIG based WECS. The system is operated under MPPT and FRT modes of operation under normal and grid fault conditions to prove the efficacious working of the proposed control strategies in DFIG based WECS.

Guaranteed Obstacle Avoidance for Multi-Robot Operations With Limited Actuation: A Control Barrier Function Approach

This letter considers the problem of obstacle avoidance for multiple robotic agents moving in an environment with obstacles. A decentralized supervisory controller is synthesized based on control barrier functions (CBF) that guarantees obstacle avoidance with limited actuation capability. The proposed method is applicable to general nonlinear robot dynamics and is scalable to an arbitrary number of agents. Agent-to-agent communication is not required, yet a simple broadcasting scheme improves the performance of the algorithm. The key idea is based on a control barrier function constructed with a backup controller, and we show that by assuming other agents respecting the same CBF condition, the supervisory control algorithm can be implemented decentrally and guarantees obstacle avoidance for all agents.

Scheduling-Based Optimization for Motion Coordination of Autonomous Vehicles at Multilane Intersections

This paper considers the motion coordination problem of autonomous vehicles in an intersection of a traffic network. The featured challenge is the design of an intersection traffic manager, in the form of a supervisory control algorithm, that regulates the motion of the autonomous vehicles in the intersection. We cast the multivehicle coordination task as an optimization problem, with a one-dimensional search-space. A model- and optimization-based heuristic method is employed to compute the control policy that results in the collision-free motion of the vehicles at the intersection and, at the same time, minimizes their delay. Our approach depends on a computation framework that makes the need for complex analytical derivations obsolete. A complete account of the computational complexity of the algorithm, parameterized by the configuration parameters of the problem, is provided. Extensive numerical simulations validate the applicability and performance of the proposed autonomous intersection traffic manager.

EXTRACT MAXIMUM POWER FROM PV SYSTEM EMPLOYING MPPT WITH FLC CONTROLLER

Extract of maximum power from photo voltaic (PV) system employing with fuzzy logic controller (FLC) based MPPT technique is investigated in this article. Fuzzy is a expert supervisory control algorithm system, provides satisfied acceptable results from PV. Maximum/lower power point tracking (MPPT/LPPT) approaches are adopted to get maximum output power from the PV irrespective of variation in its input source (Solar irradiation and temperature). The performance results have been investigated in MATLAB/Simlink package for different conditions. From the simulation results it is evident that proposed fuzzy control algorithm works well compared to the other traditional MPPT techniques.

Sensor fault control scheme for a variable speed wind energy system in a stand-alone DC micro grid

This paper describes sensor fault control (SFC) scheme for a wound rotor induction generator (WRIG)-based wind energy system (WES) connected to a standalone DC micro grid (DCM). The DCM provides excitation for WRIG through a rotor side converter (RSC). The stator voltage and frequency are regulated using PI controllers. The generated power is supplied to loads, and excess power is supplied to DCM by PWM rectification operation of RSC. During stall mode, DCM supplies load through RSC by PWM inversion operation. To execute a smooth changeover of modes, supervisory control algorithm is employed. The control scheme involves sensors which may not be ideal and if fault occurs might lead to system collapse. To compensate the electrical sensor faults, a fault detection and compensation (FDC) scheme based on adaptive neuro-fuzzy inference system (ANFIS) is employed. For compensating mechanical sensor faults, an electrical sensor is employed. The system is experimentally tested to show the effectiveness of SFC scheme.

Obstacle Avoidance for Low-Speed Autonomous Vehicles With Barrier Function

This paper presents an obstacle avoidance algorithm for low speed autonomous vehicles (AV), with guaranteed safety. A supervisory control algorithm is constructed based on a barrier function method, which works in a plug-and-play fashion with any lower level navigation algorithm. When the risk of collision is low, the barrier function is not active; when the risk is high, based on the distance to an “avoidable set,” the barrier function controller will intervene, using a mixed integer program to ensure safety with minimal control effort. This method is applied to solve the navigation and pedestrian avoidance problem of a low speed AV. Its performance is compared with two benchmark algorithms: a potential field method and the Hamilton–Jacobi method.