Simulation in Control System Design

The Graduate Automotive Technology Education (GATE) Center at the University of Tennessee, Knoxville (UTK) offers courses addressing the simulation, modeling, and control system design of hybrid electric vehicles (HEV). In the Spring of 1999 such a course was conducted to support the UTK FutureCar Challenge entry for 1999. The vehicle modeled is a Dual-configuration Hybrid Electric Vehicle (DHEV) which uses a planetary power-split device similar to the Toyota Hybrid System used in the Toyota “Prius”. The goals of the course included the development of a real-time simulator that could incorporate actual vehicle control hardware in the simulator loop. This “control-hardware-in-the-loop” (CHIL) configuration was used for simulation, control system design, and troubleshooting. This approach allows the simulation of normal vehicle operating conditions as well as emergency fault handling situations in which it may not be desirable to subject the actual prototype vehicle to a given test condition. Additionally, it is possible to do a great deal of control system testing and development without an operating vehicle.

A microstructure flux profiler (MFP) which uses variation of its buoyancy to maintain the speed required for data logging is detailed. The variation of buoyancy to control speed is uncommon among profilers, and the autonomous operation of this device allows a much greater degree of flexibility in the data collection. The complete development of the control system, from modeling through simulation and control system design to commissioning, is examined, and the performance is reviewed. >

Dynamic Operation and Simulation of Post-Combustion CO2 Capture

Modern deep neural networks are being widelyexploited to solve challenging learning tasks, including nonlinearsystem identification. Bayesian system identification intrinsicallyencapsulate uncertainty in model parameters and provides fore-casting distribution enabling enhanced analysis, simulation andcontrol system design. Nevertheless, the application of the full Bayesian approach to articulated models as deep neural networksresults quite challenging in practice. In this work we propose anidentification technique for nonlinear dynamic systems exploitinga deep recurrent neural network with Long-Short Term Memory(LSTM) units retaining a Bayesian framework. To such an aim,we stacked the recurrent neural network with a probabilisticlayer, decomposing the nonlinear dynamic model into a combi-nation of flexible functions. Hence, deterministic and stochasticlayers are trained jointly, forcing the learning algorithm totransform the input data sequences into a deterministic featurespace encoded by the LSTM, useful for predictions. Besides, wedeployed a scalable technique based on Variational Inference todeal with the exact inference intractability. We show the effec-tiveness of the proposed approach by the application to a widelyexploited open benchmark for nonlinear system identification

This paper investigates the control-oriented modeling for turbofan engines. The nonlinear equilibrium manifold expansion (EME) model of the multiple input multiple output (MIMO) turbofan engine is established, which can simulate the variation of high-pressure rotor speed, low-pressure rotor speed and pressure ratio of compressor with fuel flow and throat area of the nozzle. Firstly, the definitions and properties of the equilibrium manifold method are presented. Secondly, the steady-state and dynamic two-step identification method of the MIMO EME model is given, and the effects of scheduling variables and input noise on model accuracy are discussed. By selecting specific path, a small amount of dynamic data is used to identify a complete EME model. Thirdly, modeling and simulation at dynamic off-design conditions show that the EME model has model accuracy close to the nonlinear component-level (NCL) model, but the model structure is simpler and the calculation is faster than that. Finally, the linearization results are obtained based on the properties of the EME model, and the stability of the model is proved through the analysis of the eigenvalues, which all have negative real parts. The EME model constructed in this paper can meet the requirements of real-time simulation and control system design.

Research and design of simulation and verification system of intelligent expressway based on ETC big data

Due to the complexity of heat transfer mechanism, heat transfer model in furnace with simple form and applying to real-time calculation accuracy usually has a low accuracy. By using partial least squares regression (PLSR) method the boiler heat transfer model was established. This method combines the forecasting by means of model with non-model style data connotation analysis, thus, the model by mean of PLSR overcomes multivariable correlativity. The prediction model for boiler heat transfer based on PLSR has high accuracy, clear physical meaning, and more reasonable explanation to heat transfer mechanism of boiler, as compared with the ordinary least-squares regression model. The modeling method presented in this paper can be used for simulation and model-based control system design on ships steam power system and other large power system.

Design and validation of control strategies requires a knowledge of both control theory and the process to be controlled. These activities can be time consuming if a process is complex, involving many interacting equipments with non-linear characteristics. Simulators have proven to be a powerful tool in the design and validation of control structures for this type of complex process. The aim of this chapter is to describe a dynamic simulator for comminution plants implemented in a commercial simulation platform, and to illustrate its use in the design of control strategies for different stages in the comminution process.

Digital Controller Implementation for Electrohydraulic Motion Simulator Based on MAS and RCP

A liquid metal cooled reactor coupled by mixed gas of helium and xenon closed Brayton cycle is designed for space exploration purpose. The design and dynamics of the space reactor differ from those of traditional reactors. Therefore, it is necessary to develop a control system design and simulation platform to investigate its dynamic behavior, design and evaluate the control system. Two system codes are adopted and coupled to develop the platform. The reactor core system code is used to calculate the reactor kinetics and thermal-hydraulics of the coolant. The energy conversion system code is to compute the Brayton cycle and the heat exchanger between the primary and secondary loops. 3KeyMaster simulation platform is used to load the two system codes by an interface program through dynamic link library. The data exchange and time synchronization between 3KeyMaster simulation platform and two system code are performed to realize the coupling of two system code. By introducing typical disturbances, the calculation results are compared with those from the system codes to verify the interface program. The input and output parameters can be visualized by model components in 3KeyMaster simulation platform to analyze the dynamic behaviors, which is useful for the subsequent control system design.

In this paper, the design of simulation software and control system for unmanned aerial vehicle (UAV) during aerial refueling is proposed, in which the simulation software is constructed under Microsoft Visual Studio 2010, and control system is designed based on the linear control theory and assumption of relatively small perturbations during the docking. The proposed model has the merits of explicitly formulating the dynamic states to be controlled. The simulation architecture and control system design procedure was described. Based on the mathematical model, and assigned mission scenario of the UAV, the simulation results demonstrated the visualization of the simulation software and feasibility of the proposed control system design scheme.

Aircraft Control System Design and Analysis Program Package

Dynamic Control Design and Simulation of Biogas Pressurized Water Scrubbing Process

Most systems are nonlinear in nature and generally there aren't analytic control system design methods for them. Using artificial intelligent methods such as fuccy logic is more effective for such systems. But generating fuccy rules is the major problem of fuccy logic controllers. Sometimes fuccy rules are obtained from numerical data and occasionally rules are derived from human experts who have acquired their knowledge through experience. However these experiences may not always be available. Thus selection of an optimal or suboptimal set of rules from the set of all possible rules is an important and an essential step toward the design of any successful fuccy logic controller. We used the genetic algorithm to generate fuccy rules. Therefore we don't require any expert knowledge and input-output data. In this paper, the backing of a track to a loading dock is selected as a nonlinear system. The membership functions with different number of fuccy sets are applied for the input and output linguistic variables. In fuccy logic controller, generally one fuccy rules set is applied during trajectory. Then we apply two approaches to arrive the final point with possible least steps. In first method, the input space is divided into two regions, the vicinity of the set point and the rest. We use fuccy rules with less number of fuccy subsets in away from the set point and we increase the number of fuccy subsets of fuccy rules set in the vicinity of the set point. Therefore we apply two fuccy rules sets in two equal regions. In second method, we vary the set of fuccy rules during trajectory based on least error. The performance of the first method is compared with the performance of second method and general method which one fuccy rules set is applied during trajectory.

Need of use of computer technology for conceptual design of control systems' elements for electric power system was proved. The known approaches to the solution of this task are analysed and need of their development is proved. New approach to conceptual design of control systems' elements on the basis of the fractal theory is observed. Two types of self-similarity for functional fractals were defined: rigid and non-rigid self-similarity. Use of this approach allows to model sensors of new generation, in particular, multiifunctional sensors.