A microstructure flux profiler 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.

Simulation in Control System Design

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

The problem encountered while developing the RKX-200TJ/booster is the measurement of mass vehicle (center of gravity). The thrust line of the rocket booster does not coincide with the center of gravity can induce a pitch disturbance. By controlling the pitch parameter, the pitch disturbance phenomenon can be minimized. In this paper is presented the flight performance and dynamics analysis and the design of pitch and roll control system for RKX200TJ/booster during rocket booster and climb phase. The result indicates that the pitch disturbance can be reduced until decrease about 27% whereas roll angle ( ) can be damped at zero level ( ). Pitch angle ( ) can be maintained at angle 5° for climb phase. Although the one of moment arm case shows the static instability and uncontrollability during rocket booster phase, the control system can control vehicle the further phase. This simulation presented in X-Plane and Simulink. The PID controller is selected in control system design.

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.

Control systems are pervasive in our lives. Our homes have environmental controls. The appliances we use, such as the washing machine, microwave, etc. carry embedded controllers in them. We fly in airplanes and drive automobiles that extensively use control systems. The industrial plants that produce consumer goods run on process control systems. The recent drive toward automation has increased our reliance on control systems technology. This book discusses control systems design from a model-based perspective for dynamic system models of single-input single-output type. The emphasis in this book is on understanding and applying the techniques that enable the design of effective control systems in multiple engineering disciplines. The book covers both time-domain and the frequency-domain design methods, as well as controller design for both continuous-time and discrete-time systems. MATLAB© and its Control Systems Toolbox are extensively used for design. Technical topics discussed in the book include: • Mathematical models of physical systems • Analysis of transfer function and state variable models • Control systems design objectives • Control system design with root locus • Control system design in the state-space • Control system design of sampled-data systems • Compensator design with frequency response methods

Control systems are pervasive in our lives. Our homes have environmental controls. The appliances we use, such as the washing machine, microwave, etc. carry embedded controllers in them. We fly in airplanes and drive automobiles that extensively use control systems. The industrial plants that produce consumer goods run on process control systems. The recent drive toward automation has increased our reliance on control systems technology. This book discusses control systems design from a model-based perspective for dynamic system models of single-input single-output type. The emphasis in this book is on understanding and applying the techniques that enable the design of effective control systems in multiple engineering disciplines. The book covers both time-domain and the frequency-domain design methods, as well as controller design for both continuous-time and discrete-time systems. MATLAB© and its Control Systems Toolbox are extensively used for design. Technical topics discussed in the book include: • Mathematical models of physical systems • Analysis of transfer function and state variable models • Control systems design objectives • Control system design with root locus • Control system design in the state-space • Control system design of sampled-data systems • Compensator design with frequency response methods

PurposeDrawing on Simons's levers of control (LoC) framework, the primary aim of this study is to advance an understanding of the balance between empowerment and constraint in a non-profit UK organisation. In particular, this study examines the antecedents and manifestations of LoC (im)balance, in relation to employees' level of engagement with the control systems in place.Design/methodology/approachFor this study, 27 semi-structured interviews were conducted with different organisational members, from directors to non-managerial staff, to gain an in-depth appreciation of the main differences between managerial intentions in the design of management control systems (MCS) and employee perceptions regarding the role of such systems.FindingsThis research reveals that suppression of interactive systems and internal inconsistencies between different types of controls hinder the balance between empowerment and constraint. This imbalance is then found to have important consequences for employee buy-in, in some cases, defeating the purposes of control.Research limitations/implicationsThis study enhances our understanding of the gap between the design of control systems and the employee perceptions of it in an unusual organisational setting (non-profit and bringing together clinical and non-clinical staff and operations).Originality/valueThe study of MCS and its role in organisations has long been the focus of both academic and practitioner research. Yet, while extant literature focused on management's perspective on MCS, few studies have explored employees' attitudes and behaviours that accompany the implementation of control. What is more, little is known about the specific uses and behavioural outcomes of MCS in the context of non-profit organisations. Drawing on Simons's LoC framework, this paper addresses these gaps in the literature and investigates the balance between control and empowerment of employees in a UK non-profit organisation with significant clinical remit.

Model-based development (MBD), which utilizes system models to design complex products, has received increasing attention. However, an advanced control system design scheme is required to accurately control the developed products under harsh conditions for practical usage. This paper proposes a control system that integrates a data-driven compensator (DDC) with a model-based control (MBC) system design. The proposed method considers a hierarchical control structure comprising an upstream control system based on the MBC design approach and a downstream control system that includes a plant control loop with a DDC. The proposed system can always maintain the desired performance by driving the MBC based on an ideal model because the downstream control system, DDC, maintains the characteristics of the ideal model even if the plant properties change owing to changes in operational and/or environmental conditions. In this study, the design scheme for unifying models and data in the MBD process is called smart MBD (S-MBD), and the proposed control system design scheme is based on the S-MBD approach. The effectiveness of the proposed hierarchical control system is verified by applying it to a hydraulic excavator.

We review practical case studies of a developing method of highly reliable real-time embedded control systems using a CPU model-based hardware/software co-simulation. We take an approach that enables us to fully simulate a virtual mechanical control system including a mechatronics plant, microcontroller hardware, and object code level software. This full virtual system approach simulates control system behavior, especially that of the microcontroller hardware and software. It enables design space exploration of microarchitecture, control design validation, robustness evaluation of the system, software optimization before components design. It also avoids potential problems. The advantage of this work is that it comprises all the components in a typical control system, enabling the designers to analyze effects from different domains, for example mechanical analysis of behavior due to differences in controller microarchitecture. To further improve system design, evaluation and analysis, we implemented an integrated behavior analyzer in the development environment. This analyzer can graphically display the processor behavior during the simulation without affecting simulation results such as task level CPU load, interrupt statistics, and the software variable transition chart. It also provides useful information on the system behavior. This virtual system analysis does not require software modification, does not change the control timing, and does not require any processing power from the target microcontroller. Therefore this method is suitable for real-time embedded control system design, in particular automotive control system design that requires a high level of reliability, robustness, quality, and safety. In this study, a Renesas SH-2A microcontroller model was developed on a CoMET™platform from VaST Systems Technology. An electronic throttle control (ETC) system and an engine control system were chosen to prove this concept. The electronic throttle body (ETB) model on the Saber® simulator from Synopsys® and the engine model on MATLAB®/Simulink®simulator from MathWorks can be simulated with the SH-2A model using a newly developed co-simulation interface between MATLAB®/Simulink® and CoMET™. Though the SH-2A chip was being developed as the project was being executed, we were able to complete the OSEK OS development, control software design, and verification of the entire system using the virtual environment. After releasing a working sample chip in a later stage of the project, we found that such software could run on both actual ETC system and engine control system without critical problem. This demonstrates that our models and simulation environment are sufficiently credible and trustworthy.

In this paper, a systematic approach to design the control system of a commercial-scale integrated gasification combined cycle (IGCC) power plant with CO2 capture is considered. The control system design is developed with the objective of optimizing a desired scalar function while satisfying operational and environmental constraints in the presence of measured and unmeasured disturbances. Various objective functions can be considered for the control system design such as maximization of profit, maximization of the power produced, or minimization of the auxiliary power consumed in the plant. The design of such a control system can make the IGCC plant suitable to play an active role in the smart grid era by enabling operation in the load-following mode as demand for electricity from the grid fluctuates over time. In addition, other penalty functions such as emission penalties for CO2 or other criteria pollutants can be considered in the control system design.;The control system design is performed in two stages. In the first stage, a top-down analysis is used to generate a list of controlled, manipulated, and disturbance variables considering a scalar operational objective and other process constraints. In this section, innovative methods devised for primary and secondary controlled variable selection will be discussed.Exploiting these results, the second stage uses a bottom-up approach for simultaneous design of the control structure and the controllers. In this section, a novel means of control structure design has been proposed.;In this research, the proposed two-stage control system design approach is applied to the IGCC's acid gas removal (AGR) process which uses the physical solvent Selexol(TM) to selectively remove CO2 and H2S from the shifted syngas. Aspen Plus DynamicsRTM is used to develop the AGR process model while MATLABRTM is used to perform the control system design. This work has shown the proposed design procedure for plantwide control yields an optimal control structure. Additionally, the methods proposed in this work for primary and secondary controlled variable selection yield controlled variables which balance economic and control performance. Finally, the method proposed for control structure design has been found to yield a control structure that balance the control performance with controller complexity.

A simulation system was established in the Matlab/Simulink environment based on the bionic underwater vehicle propelled by multiple undulating fins. It was composed of motion and attitude controller and plant model including 6DOF rigid body kinetic model, carrier's hydrodynamic model, and multiple undulating fins' hydrodynamic model. All parts of the simulation system were designed in detailed, and a layered control system was put forward. Finally, the performance of the control channels for speed, depth and yawing were simulated in terms of the real design parameters and hydrodynamic coefficients. Results show that the simulation system has important significance to research on the dynamic of the bionic underwater vehicle and verify the design of control system.

A method is described for using nonlinear programing in the computer-aided design of airplane control systems. It is assumed that the quality of such systems depends on many criteria. These criteria are included in the constraints vector (instead of attempting to combine them into a single scalar criterion, as is usually done), and the design proceeds through a sequence of nonlinear programing solutions in which the designer varies the specification of sets of requirements levels. The method is applied to design of a lateral stability augmentation system (SAS) for a fighter airplane, in which the requirements vector is chosen from the official handling-qualities specifications. Results are shown for several simple SAS configurations designed to obtain desirable handling qualities over all design flight conditions with minimum feedback gains. The choice of the final design for each case is not unique but depends on the designer's decision as to which achievable set of requirements levels represents the best for that system. Results indicate that constant gain systems can substantially exceed the highest required levels of handling qualities in all design flight conditions of the example considered. The role of the designer as a decision maker, interacting with the computer program, is discussed. Advantages of this type of designer-computer interaction are emphasized. Desirable extensions of the method are indicated.