scholarly journals Toward Dynamics, Stability and Processing of The Vision System

2019 ◽  
Vol 9 (2) ◽  
pp. 1
Author(s):  
Hooshang Hemami ◽  
Vadim I. Utkin
Keyword(s):  
Inverse Dynamics ◽  
Vision System ◽  
State Space Model ◽  
Human Movement ◽  
Mental Skills ◽  
Skeletal System ◽  
Space Model ◽  
Basic Model ◽  
And Control ◽  
Dynamics Stability

Eye movements are vitally important for living, survival and vision. A basic model of the eye,  including ocular muscles, is considered that may be essential for better understanding the optical and sensory apparatus involved in vision, perception, and processing and control machinery of human movement. A state space model based on Newton-Euler formulation of the dynamics of the head and the eyeballs is presented that can be integrated with the dynamics of the skeletal system. The formulation allows integration of Lyapunov stability, hypothesized  feedback mechanisms and inverse dynamics in more comprehensive neural processing models. Neural networks involved in saccades, gazes and dynamics of  specific  injuries, diseases and defects can be included in this formulation. The development allows a better understanding of the vision system as well as the neural and physiological elements  involved in perception and physical and mental skills. It is also relevant to the design of artificial eyes.

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 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.

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.

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.

A NONLINEAR UNIFIED STATE-SPACE MODEL FOR SHIP MANEUVERING AND CONTROL IN A SEAWAY

2005 ◽  
Vol 15 (09) ◽  
pp. 2717-2746 ◽  
Author(s):  
THOR I. FOSSEN
Keyword(s):  
State Space ◽  
Transfer Functions ◽  
State Space Model ◽  
Low Frequency ◽  
Ship Maneuvering ◽  
Frequency Model ◽  
Space Model ◽  
Classic Theory ◽  
Space Formulation ◽  
And Control

This article presents a unified state-space model for ship maneuvering, station-keeping, and control in a seaway. The frequency-dependent potential and viscous damping terms, which in classic theory results in a convolution integral not suited for real-time simulation, is compactly represented by using a state-space formulation. The separation of the vessel model into a low-frequency model (represented by zero-frequency added mass and damping) and a wave-frequency model (represented by motion transfer functions or RAOs), which is commonly used for simulation, is hence made superfluous.

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.

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