Modeling and Control of Biochemical Reactor

2013 ◽  
Vol 791-793 ◽  
pp. 818-821
Author(s):  
Shi Li ◽  
Xi Ju Zong ◽  
Yan Hu
Keyword(s):  
Transfer Function ◽  
State Space ◽  
State Space Model ◽  
Internal Model Control ◽  
Transfer Function Model ◽  
Function Model ◽  
Modeling And Control ◽  
Space Model ◽  
Biochemical Reactor ◽  
And Control

This paper is concerns with the study of modeling and control of biochemical reactor. Firstly, a mathematical model is established for a typical biochemical reactor, the mass balance equations are established individually for substrate concentration and biomass concentration. Then, the model is linearized at the steady-state point, two linear models are derived: state space model and transfer function model. The transfer function model is used in internal model control (IMC), where the filter parameter is selected and discussed. The state space model is applied in model predictive control (MPC), where controller parameters of control prediction horizon length and constraint of control variable variation are discussed.

Modeling and Control of Sludge Pyrolysis in a Fluidized Bed Reactor

2013 ◽  
Vol 846-847 ◽  
pp. 69-72
Author(s):  
Shi Li ◽  
Xi Ju Zong ◽  
Yan Hu
Keyword(s):  
Transfer Function ◽  
State Space ◽  
Fluidized Bed ◽  
State Space Model ◽  
Fluidized Bed Reactor ◽  
Transfer Function Model ◽  
Function Model ◽  
Modeling And Control ◽  
Space Model ◽  
And Control

This paper is concerns with the study of modeling and control of sludge pyrolysis in a fluidized bed reactor. Firstly, a mathematical model is established for sludge pyrolysis in a fluidized bed furnace, mass balance and energy equations are established. Then, the model is linearized at the steady-state point, two linear models are derived: state space model and transfer function model. The transfer function model is used in internal model control (IMC), where the filter parameter is selected and discussed. The state space model is applied in model predictive control (MPC), where controller parameters of prediction horizon length and control horizon length are discussed.

scholarly journals Implementation of Mathematical Models of Buck Converter using MatLab/Simulink

2021 ◽  
pp. 516-520
Author(s):  
S. Krishnaveni Krishnaveni
Keyword(s):  
Mathematical Model ◽  
Mathematical Models ◽  
State Space Model ◽  
Buck Converter ◽  
Physical Models ◽  
Transfer Function Model ◽  
Function Model ◽  
Space Model ◽  
Modeling Techniques ◽  
And Control

The study of the dynamic behavior, transient response and the characteristics of the converters require deep knowledge in mathematical models. Mathematical models have been adequately used in the design and control of DC-DC converters and also the mathematical models are more suitable than the physical models. This paper describes the four modeling techniques of a buck converter, circuit model, mathematical model, state space model and transfer function model, and their implementation in Simulink environment.

Multivariable modeling and control of an activated sludge process

1998 ◽  
Vol 37 (12) ◽  
pp. 149-156 ◽  
Author(s):  
Carl-Fredrik Lindberg
Keyword(s):  
Activated Sludge ◽  
Nitrate Concentration ◽  
State Space Model ◽  
Activated Sludge Process ◽  
Invariant State ◽  
Linear Quadratic ◽  
Modeling And Control ◽  
Space Model ◽  
And Control ◽  
Time Invariant

This paper contains two contributions. First it is shown, in a simulation study using the IAWQ model, that a linear multivariable time-invariant state-space model can be used to predict the ammonium and nitrate concentration in the last aerated zone in a pre-denitrifying activated sludge process. Secondly, using the estimated linear model, a multivariable linear quadratic (LQ) controller is designed and used to control the ammonium and nitrate concentration.

scholarly journals A state-space model and control of a full-range PMSG wind turbine for real-time simulations

2018 ◽  
Vol 100 (4) ◽  
pp. 2177-2191 ◽  
Author(s):  
Agustín Tobías-González ◽  
Rafael Peña-Gallardo ◽  
Jorge Morales-Saldaña ◽  
Aurelio Medina-Ríos ◽  
Olimpo Anaya-Lara
Keyword(s):  
Wind Turbine ◽  
State Space ◽  
Real Time ◽  
Full Range ◽  
State Space Model ◽  
Space Model ◽  
And Control ◽  
Real Time Simulations

scholarly journals Comparing the Effectiveness of Feedback and Load Compensation Techniques in Controlling the Disturbed State of Nominal-T Configuration

2017 ◽  
Vol 2 (7) ◽  
pp. 48
Author(s):  
Thomas Olabode Ale
Keyword(s):  
Transfer Function ◽  
Transmission Line ◽  
State Space ◽  
Research Work ◽  
Phase Lag ◽  
Transfer Function Model ◽  
Function Model ◽  
Load Compensation ◽  
Unit Impulse ◽  
Compensation Technique

Stability of Power system is the ability of a system, for a given initial operating condition, to regain a state of operating equilibrium after being subjected to a physical disturbance, with most system variables bounded so that practically the entire system remains intact. This research work stated clearly the effectiveness of the feedback and load compensation techniques in stabilizing a disturbed state of a medium transmission line using a Nominal-T configuration network. In order to achieve the set objectives, Osogbo - Akure transmission line data was obtained from Akure 132kV transmission substation.  This configuration was modeled into transfer function and state space models, the compensator circuit which happens to be the phase lag circuit was also modeled. The transfer function model contained the line parameters extracted from this transmission substation logbook. A state space model was obtained from the transfer function model with a code written in MATLAB environment. The effectiveness of these compensation techniques were compared. The result revealed that load compensation technique offered a perfect compensation to unit step disturbance and unit impulse disturbance. While feedback compensation technique provides perfect compensation to unit impulse disturbance only.

Modeling and Control for a Small UAV Helicopter

2014 ◽  
Vol 532 ◽  
pp. 70-73
Author(s):  
Peng Liu ◽  
Sheng Dong Liu ◽  
Zhneg Zhao ◽  
Jia Qi Liu
Keyword(s):  
State Space ◽  
Control Method ◽  
State Space Model ◽  
Path Following ◽  
Data Set ◽  
Loop Control ◽  
Modeling And Control ◽  
Space Model ◽  
Small Uav ◽  
Frequency Identification

A frequency identification modeling method for a small UAV helicopter and its control system design is presented. A modified frequency identification method for a state-space model of helicopter is presented. The overall concept is to extract a complete set of non-parametric input-to-output frequency responses that fully characterizes the coupled helicopter dynamics, conduct a nonlinear search based on secant method for a linear state-space model that matches the frequency-response data set. The accuracy of the identified model is verified by flight experiments. A path following controller is presented for an unmanned helicopter with two-loop control frame. The outer-loop is used to obtain the expected attitude angles through reference path and speed with guidance-based path following control, and the inner-loop is used to control the attitude angles of helicopter tracking the expected ones with loop shaping method. Finally, an 8 trajectory tracking simulation is conducted to illustrate the efficiency of the proposed control method.

Method of Identifying Key Quality Characteristics in Multistage Manufacturing Process Based on PLSR

2012 ◽  
Vol 217-219 ◽  
pp. 2580-2584 ◽  
Author(s):  
Ning Wang ◽  
Ji Chao Xu ◽  
Jian Feng Yang
Keyword(s):  
State Space ◽  
Manufacturing Process ◽  
State Space Model ◽  
Quality Characteristics ◽  
The State ◽  
Quality Analysis ◽  
Least Squares Regression ◽  
Space Model ◽  
Multistage Manufacturing ◽  
And Control

To improve the existing methods of identifying the key quality characteristics in multistage manufacturing process, the partial least squares regression (PLSR) method is combined with the state space model that a new method of identifying the key quality characteristics in multistage manufacturing process based on PLSR is proposed. According to the feature of multistage manufacturing process, the state space model is introduced to build the key quality characteristics identifying model for multistage manufacturing process, using the PLSR method to solve the problem of the quality characteristics such as multicollinearity, do model analyzing and identify the key quality characteristics. At last, the cigarette production process is presented as an example to introduce the application of this method. The result shows that this method can not only identify the key quality characteristics in multistage manufacturing process, but also establish the model of output quality effecting of all levels on the final product quality and its quality characteristics relationship, which reflect the structure of the multistage manufacturing process and causal relationship between quality characteristics at all process levels, provide the basis for quality analysis and control in multistage manufacturing process.

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