scholarly journals Hardware in the Loop Simulation and Control Design for Autonomous Free Running Ship Models

2020 ◽  
Vol 70 (4) ◽  
pp. 469-476
Author(s):  
Awanish Chandra Dubey ◽  
Anantha V Subramanian
Keyword(s):  
Real Time ◽  
Control Design ◽  
Hardware In The Loop ◽  
Control Logic ◽  
Combined System ◽  
Plant Model ◽  
First Order ◽  
Ship Hydrodynamic ◽  
Free Running ◽  
Hil Simulation

This paper presents an hardware-in-the-loop (HIL) simulation system tool to test and validate an autonomous free running model system for ship hydrodynamic studies with a view to verification of the code, the control logic and system peripherals. The computer simulation of the plant model in real-time computer does not require the actual physical system and reduces the development cost and time for control design and testing purposes. The HIL system includes: the actual programmable embedded controller along with peripherals and a plant model virtually simulated in a real-time computer. With regard to ship controller design for ship model testing, this study describes a plant model for surge and a Nomoto first order steering dynamics, both implemented using Simulink software suit. The surge model captures a quasi-steady state relationship between surge speed and the propeller rpms, obtained from simple forward speed towing tank tests or derived analytically. The Nomoto first order steering dynamics is obtained by performing the standard turning circle test at model scale. The control logic obtained is embedded in a NI-cRIO based controller. The surge and steering dynamics models are used to design a proportional-derivative controller and an LQR controller. The controller runs a Linux based real-time operating system programmed using LabVIEW software. The HIL simulation tool allows for the emulation of standard ship hydrodynamic tests consisting of straight line, turning circle and zigzag to validate the combined system performance, prior to actual for use in the autonomous free-running tests.

scholarly journals Real-Time Hardware in the Loop Simulation Methodology for Power Converters Using LabVIEW FPGA

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 373 ◽  
Author(s):  
Leonel Estrada ◽  
Nimrod Vázquez ◽  
Joaquín Vaquero ◽  
Ángel de Castro ◽  
Jaime Arau
Keyword(s):  
Power Electronics ◽  
Real Time ◽  
High Speed ◽  
Power Converter ◽  
Buck Converter ◽  
Voltage Source ◽  
Hardware In The Loop ◽  
Power Electronics Converters ◽  
Labview Fpga ◽  
Hil Simulation

Nowadays, the use of the hardware in the loop (HIL) simulation has gained popularity among researchers all over the world. One of its main applications is the simulation of power electronics converters. However, the equipment designed for this purpose is difficult to acquire for some universities or research centers, so ad-hoc solutions for the implementation of HIL simulation in low-cost hardware for power electronics converters is a novel research topic. However, the information regarding implementation is written at a high technical level and in a specific language that is not easy for non-expert users to understand. In this paper, a systematic methodology using LabVIEW software (LabVIEW 2018) for HIL simulation is shown. A fast and easy implementation of power converter topologies is obtained by means of the differential equations that define each state of the power converter. Five simple steps are considered: designing the converter, modeling the converter, solving the model using a numerical method, programming an off-line simulation of the model using fixed-point representation, and implementing the solution of the model in a Field-Programmable Gate Array (FPGA). This methodology is intended for people with no experience in the use of languages as Very High-Speed Integrated Circuit Hardware Description Language (VHDL) for Real-Time Simulation (RTS) and HIL simulation. In order to prove the methodology’s effectiveness and easiness, two converters were simulated—a buck converter and a three-phase Voltage Source Inverter (VSI)—and compared with the simulation of commercial software (PSIM® v9.0) and a real power converter.

Hardware-in-the-Loop Simulation for a Heave Compensator of an Offshore Support Vessel

2016 ◽  
Author(s):  
Luman Zhao ◽  
Myung-Il Roh ◽  
Seung-Ho Ham
Keyword(s):  
Control System ◽  
Simulation Model ◽  
Real Time ◽  
Natural Environment ◽  
Programmable Logic ◽  
Hardware In The Loop ◽  
Proportional Integral Derivative ◽  
Plant Model ◽  
Test Conditions ◽  
Final Development

Tune and verification of control system algorithms for offshore installation operations involving complex and advanced machinery and difficult due to its safety factor. It may be also very costly or even impossible to establish certain test conditions in the physical process environment of the control system. To solve this problem, the Hardware-In-the-Loop-Simulation (HILS) can be regarded as an effective method for testing the control system prior to its final development. The sophisticated HILS is composed of a control system and a HIL simulator which is a simulation model of the offshore plant developed by software. In this study, we focus on the application of HILS for a heave compensator which is used to keep the position or the lowing speed of a lifting object. This study contains three components. Firstly, a physics-based analysis component is used to develop a simulation model of an offshore plant, that is, a HIL simulator. Secondly, the programmable logic controller (PLC) component, that is a control system, is used to regulate the offshore plant model, including a proportional-integral-derivative (PID) feedback controller which aims to control the position or lowering speed of the lifting object. Thirdly, an interface component is developed to communicate the data between the HIL simulator and the control system in real-time. To evaluate the applicability of HILS for a heave compensator, it was applied to an example of an offshore support vessel (OSV) crane. In order to verify the control system for the crane operation in case of heave stabilization of the lifting object, two simulation processes had been established with both a software PLC (software-in-the-loop) and a hardware PLC (hardware-in-the-loop). HILS makes it possible to test the heave compensator without building costly prototypes and without endangering natural environment.

Hardware-in-the-Loop Simulation Using Tricon v9 Safety PLC

2008 ◽  
Author(s):  
Drew J. Rankin ◽  
Jin Jiang
Keyword(s):  
Real Time ◽  
Nuclear Power ◽  
Verification And Validation ◽  
Programmable Logic ◽  
Hardware In The Loop ◽  
Design Basis ◽  
Regulatory Standards ◽  
Safety Plc ◽  
Simulation Environments ◽  
Hil Simulation

This paper presents the performance of shutdown system one (SDS1) implemented on a programmable logic controller (PLC) within real-time hardware-in-the-loop (HIL) simulation. SDS1 evaluation is focused on steam generator (SG) level low trip scenarios. A comparison of the findings with simulated expected plant operation is performed. An Invensys Triconex Tricon v9 safety PLC is interfaced to a real-time nuclear power plant (NPP) simulation suite (DarlSIM), replicating the operation of the Darlington NPP SDS1. Design basis accidents (DBA) associated with SDS1 regulatory standards are developed and applied to the two simulation environments. HIL simulation is a preferred method for testing systems prior to installation and is necessary to ensure proper SDS verification and validation. The performance of the Tricon v9 PLC, the HIL simulation platform and the two simulation environments are evaluated.

scholarly journals PHIL Infrastructure in CoSES Microgrid Lab

2021 ◽  
Author(s):  
Anurag Mohapatra ◽  
Vedran S. Peric ◽  
Thomas Hamacher
Keyword(s):  
Real Time ◽  
Renewable Resources ◽  
Control Design ◽  
Control Environment ◽  
Hardware In The Loop ◽  
Real Time Control ◽  
Time Control ◽  
External Connection ◽  
User Friendly ◽  
And Control

This paper describes the Power hardware-in-the-loop (PHIL) architecture and capacities of the CoSES laboratory at TU Munich. The lab brings together renewable resources, flexible grid topologies, fully controllable prosumer emulators, a real-time control environment, and an API access for external connection to the lab. The electrical and control design of the lab allows for sophisticated PHIL experiments with an user-friendly implementation. Two experiments are included, to validate the PHIL performance and demonstrate the use of PHIL infrastructure to investigate an OPF algorithm.

Smith predictor compensation and fuzzy incremental control for delay of space docking hardware-in-the-loop simulation system

2021 ◽  
pp. 095441002110533
Author(s):  
Simiao Yu ◽  
Junwei Han ◽  
Wenming Zhang ◽  
Dongmei Xu
Keyword(s):  
Dynamic Response ◽  
Force Sensor ◽  
Parallel Robot ◽  
Simulation System ◽  
Smith Predictor ◽  
Control Methods ◽  
Hardware In The Loop ◽  
First Order ◽  
First Order Phase ◽  
Hil Simulation

Hardware-in-the-loop (HIL) simulation for space manipulator docking is an important means to simulate real space docking on the ground. The HIL simulation system in this paper utilizes the contact force measured by force sensor to calculate the dynamics of the mechanisms, and the docking process is simulated by the parallel robot. The measurement delay of force sensor and dynamic response delay of the parallel robot are inevitable, which not only affect the accuracy of simulation but also lead to the instability of the HIL simulation system. The traditional first-order phase compensation is the most commonly used force sensor compensator; but when the force changes with a high frequency, its compensation effect becomes bad, which will lead to the divergence of the HIL simulation system. Most control methods of the parallel robot are based on the model of the parallel robot, but the forces of the parallel robot are complex during the docking process, and the system parameters, motion frequency, and dynamic response characteristics are time-varying; thus, it is difficult to design the controller based on the model. In this paper, the Smith predictor compensation (SPC) method and fuzzy incremental control (FIC) method are utilized to decrease the delays of the force sensor and parallel robot, respectively. The effectiveness of the Smith predictor compensation and fuzzy incremental control method in reducing the delay of the HIL system and in improving the stability of the system is verified by simulation and experiment; compared with the traditional first-order phase compensation and proportional-integral-differential control methods, the advantages of the proposed methods are illustrated. The research in this paper provides an important technical means for accurately simulating the real docking process.

Energy Optimal Control Design for Steer-by-Wire Systems and Hardware-in-the-Loop Simulation Evaluation

2015 ◽  
Vol 137 (7) ◽  
Author(s):  
M. Selçuk Arslan ◽  
Naoto Fukushima
Keyword(s):  
Optimal Control ◽  
High Speed ◽  
Control Method ◽  
Full Range ◽  
Control Design ◽  
Hardware In The Loop ◽  
Yaw Rate ◽  
Control Scheme ◽  
Steer By Wire ◽  
Hil Simulation

A Steer-By-Wire (SBW) control scheme is proposed for enhancing the lateral stability and handling capability of a super lightweight vehicle by using the energy optimal control method. Tire dissipation power and virtual power, which is the product of yaw moment and the deviation of actual yaw rate from the target yaw rate, were selected as performance measures to be minimized. The SBW control scheme was tested using Hardware-In-the-Loop (HIL) simulation on an SBW test rig. The case studies performed were high-speed rapid lane change, crosswind, and braking-in-a-turn. HIL simulation results showed that the SBW control scheme was able to maintain vehicle stability. The proposed SBW control design taking advantage of the full range steering of front wheel, significantly improves the vehicle handling capability. The results also demonstrate the importance of SBW control for super lightweight vehicles.

Hardware-in-the-Loop Simulation of Missile Control System Based on Windows Operation System

2013 ◽  
Vol 278-280 ◽  
pp. 1804-1808
Author(s):  
Qian Long Yang
Keyword(s):  
Control System ◽  
Real Time ◽  
Low Cost ◽  
The Novel ◽  
Hardware In The Loop ◽  
Time Requirement ◽  
Operation System ◽  
Simulation Process ◽  
Industrial Pc ◽  
Hil Simulation

A hardware-in-the-loop (HIL) simulation platform in use of windows operation system was successfully established based on general industrial PC combined with an external timer. The following HIL simulation process of missile control system indicated that the novel platform could not only satisfied the real-time requirement of HIL simulation, but also is low-cost and universal, which could provide a convenient new choice in the similar applications.

scholarly journals Real-time optimization of wind farms using modifier adaptation and machine learning

2020 ◽  
Author(s):  
Leif Erik Andersson ◽  
Lars Imsland
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Wind Farms ◽  
Finite Difference Approximation ◽  
Convergence Time ◽  
Plant Model ◽  
First Order ◽  
Time Optimization ◽  
The Cost ◽  
Real Time Optimization

Abstract. Real-time optimization (RTO) covers a family of optimization methods that incorporate process measurements in the optimization to drive the real process (plant) to optimal performance while guaranteeing constraint satisfaction. Modifier Adaptation (MA) introduces zeroth and first-order correction terms (bias and gradients) for the cost and constraint functions. Instead of updating the plant model, in MA the optimization problem is updated directly from data guaranteeing to meet the necessary condition of optimality upon convergence. The main burden of the MA approach is the estimation of the first-order modifiers of the cost and constraint functions at each RTO iteration. Finite-difference approximation is the most common approach that requires at least nu + 1 steady-state operation points to estimate the gradients, where nu is the number of control inputs. Obtaining these can require a long convergence time. For this reason, this work considers the use of Gaussian process (GP) regression to estimate the plant-model mismatch based on plant measurements, and replace the usual modifiers by these high order regression functions. GP is a probabilistic, non-parametric modelling technique well known in the machine learning community. The approach is tested on several numerical test cases simulating wind farms. It is shown that the approach is able to correct the model and converges to the plant optimal point. Several improvements for large inputs spaces, which is a challenging problem for the approach presented in the article, are discussed.

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