Development of a Real-Time Model-Prediction-Based Framework for Reset Controller Design

2014 ◽  
Vol 53 (38) ◽  
pp. 14755-14764 ◽  
Author(s):  
Valiollah Ghaffari ◽  
Paknosh Karimaghaee ◽  
Alireza Khayatian
Keyword(s):  
Real Time ◽  
Model Prediction ◽  
Controller Design ◽  
Time Model

scholarly journals Advanced Control System for Grid-Connected SOFC Hybrid Plants: Experimental Verification in Cyber-Physical Mode

2019 ◽  
Author(s):  
Mario L. Ferrari ◽  
Iacopo Rossi ◽  
Alessandro Sorce ◽  
Aristide F. Massardo
Keyword(s):  
Control System ◽  
Fuel Cell ◽  
Real Time ◽  
Pid Control ◽  
Controller Design ◽  
Experimental Tests ◽  
Cell System ◽  
Hybrid Plant ◽  
Time Model ◽  
Point Data

Abstract This paper presents a Model Predictive Controller (MPC) operating an SOFC Gas Turbine hybrid plant at end-of-life performance condition. Its performance was assessed with experimental tests showing a comparison with a Proportional Integral Derivative (PID) control system. The hybrid system operates in grid-connected mode, i.e. at variable speed condition of the turbine. The control system faces a multivariable constrained problem, as it must operate the plant into safety conditions while pursuing its objectives. The goal is to test whether a linearized controller design for normal operating condition is able to govern a system which is affected by strong performance degradation. The control performance was demonstrated in a cyber-physical emulator test rig designed for experimental analyses on such hybrid systems. This laboratory facility is based on the coupling of a 100 kW recuperated microturbine with a fuel cell emulation system based on vessels for both anodic and cathodic sides. The components not physically present in the rig were studied with a real-time model running in parallel with the plant. Model output values were used as set-point data for obtaining in the rig (in real-time mode) the effect of the fuel cell system. The result comparison of the MPC tool against a PID control system was carried out considering several plant properties and the related constraints. Both systems succeeded in managing the plant, still the MPC performed better in terms of smoothing temperature gradient and peaks.

scholarly journals Advanced Control System for Grid-Connected SOFC Hybrid Plants: Experimental Verification in Cyber-Physical Mode

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Mario L. Ferrari ◽  
Iacopo Rossi ◽  
Alessandro Sorce ◽  
Aristide F. Massardo
Keyword(s):  
Control System ◽  
Fuel Cell ◽  
Real Time ◽  
Pid Control ◽  
Controller Design ◽  
Experimental Tests ◽  
Cell System ◽  
Hybrid Plant ◽  
Time Model ◽  
Point Data

This paper presents a model predictive controller (MPC) operating a solid oxide fuel cell (SOFC) gas turbine hybrid plant at end-of-life performance condition. Its performance was assessed with experimental tests showing a comparison with a proportional integral derivative (PID) control system. The hybrid system (HS) operates in grid-connected mode, i.e., at variable speed condition of the turbine. The control system faces a multivariable constrained problem, as it must operate the plant into safety conditions while pursuing its objectives. The goal is to test whether a linearized controller design for normal operating condition is able to govern a system which is affected by strong performance degradation. The control performance was demonstrated in a cyber-physical emulator test rig designed for experimental analyses on such HSs. This laboratory facility is based on the coupling of a 100 kW recuperated microturbine with a fuel cell emulation system based on vessels for both anodic and cathodic sides. The components not physically present in the rig were studied with a real-time model running in parallel with the plant. Model output values were used as set-point data for obtaining in the rig (in real-time mode) the effect of the fuel cell system. The result comparison of the MPC tool against a PID control system was carried out considering several plant properties and the related constraints. Both systems succeeded in managing the plant, still the MPC performed better in terms of smoothing temperature gradient and peaks.

scholarly journals Real-Time Controller Design Test Bench for High-Voltage Direct Current Modular Multilevel Converters

2020 ◽  
Vol 10 (17) ◽  
pp. 6004 ◽  
Author(s):  
Saddaqat Ali ◽  
Jahangir Badar ◽  
Faheem Akhter ◽  
Syed Sabir Hussain Bukhari ◽  
Jong-Suk Ro
Keyword(s):  
Real Time ◽  
Controller Design ◽  
Simulation Software ◽  
Control Algorithms ◽  
Time Model ◽  
Multilevel Converters ◽  
Novel Approach ◽  
Modular Multilevel Converters ◽  
Labview Fpga ◽  
Complete Circuit

Modular multilevel converters (MMCs), with their inherent features and advantages over other conventional converters, have gained popularity and remain an ongoing topic of research. Many scholars have solved issues related to the operation, control, protection, and reliability of MMCs using simulation software and small hardware prototypes. We propose a novel approach for an MMC controller design with real-time systems. By utilizing a key benefit of LabVIEW Multisim co-simulation, an MMC control algorithm that can be deployed on a field-programmable gate array (FPGA) was developed in LabVIEW. The complete circuit was designed in Multisim, and a co-simulation was performed to drive an MMC model. The benefit of this topology is that control algorithms can be designed in a LabVIEW FPGA and tested with the Multisim co-simulation circuit to obtain simulation results. Once the controller works and provides satisfactory results, the same algorithm can be deployed in any NI (National Instruments) FPGA-based controller, like a compact remote input/output (RIO), to control real-time MMCs designed in an NI PCI eXtensions for Instrumentation (PXI) system. This method saves time and provides flexibility for effectively designing control algorithms and implementing them in an FPGA for real-time model implementation.

A message-based real-time model by object-oriented technique

1997 ◽  
Vol 31 (3) ◽  
pp. 45-51
Author(s):  
Jianmin Hou ◽  
Xuandong Li ◽  
Xiaocong Fan ◽  
Guoliang Zheng
Keyword(s):  
Real Time ◽  
Object Oriented ◽  
Time Model

scholarly journals Real-Time Implementation of the Predictive-Based Control with Bacterial Foraging Optimization Technique for Power Management in Standalone Microgrid Application

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1723
Author(s):  
Félix Dubuisson ◽  
Miloud Rezkallah ◽  
Hussein Ibrahim ◽  
Ambrish Chandra
Keyword(s):  
Real Time ◽  
Power Management ◽  
Renewable Energy Sources ◽  
Optimization Technique ◽  
Euler Method ◽  
Small Scale ◽  
Diesel Generator ◽  
Bacterial Foraging Optimization ◽  
Time Model ◽  
Bacterial Foraging

In this paper, the predictive-based control with bacterial foraging optimization technique for power management in a standalone microgrid is studied and implemented. The heuristic optimization method based on the social foraging behavior of Escherichia coli bacteria is employed to determine the power references from the non-renewable energy sources and loads of the proposed configuration, which consists of a fixed speed diesel generator and battery storage system (BES). The two-stage configuration is controlled to maintain the DC-link voltage constant, regulate the AC voltage and frequency, and improve the power quality, simultaneously. For these tasks, on the AC side, the obtained power references are used as input signals to the predictive-based control. With the help of the system parameters, the predictive-based control computes all possible states of the system on the next sampling time and compares them with the estimated power references obtained using the bacterial foraging optimization (BFO) technique to get the inverter current reference. For the DC side, the same concept based on the predictive approach is employed to control the DC-DC buck-boost converter by regulating the DC-link voltage using the forward Euler method to generate the discrete-time model to predict in real-time the BES current. The proposed control strategies are evaluated using simulation results obtained with Matlab/Simulink in presence of different types of loads, as well as experimental results obtained with a small-scale microgrid.

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