scholarly journals Modeling and Control of a Photovoltaic Energy System Using the State-Space Averaging Technique

2010 ◽  
Vol 7 (5) ◽  
pp. 682-691 ◽  
Author(s):  
Jamri
Keyword(s):  
State Space ◽  
Energy System ◽  
The State ◽  
Averaging Technique ◽  
Modeling And Control ◽  
Photovoltaic Energy ◽  
And Control

Modeling and Control of a Rotary Crane

1985 ◽  
Vol 107 (3) ◽  
pp. 200-206 ◽  
Author(s):  
Y. Sakawa ◽  
A. Nakazumi
Keyword(s):  
Feedback Control ◽  
Equilibrium State ◽  
Open Loop ◽  
The State ◽  
Control Input ◽  
Loop Control ◽  
Modeling And Control ◽  
Control Scheme ◽  
Rotary Crane ◽  
And Control

In this paper we first derive a dynamical model for the control of a rotary crane, which makes three kinds of motion (rotation, load hoisting, and boom hoisting) simultaneously. The goal is to transfer a load to a desired place in such a way that at the end of transfer the swing of the load decays as quickly as possible. We first apply an open-loop control input to the system such that the state of the system can be transferred to a neighborhood of the equilibrium state. Then we apply a feedback control signal so that the state of the system approaches the equilibrium state as quickly as possible. The results of computer simulation prove that the open-loop plus feedback control scheme works well.

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.

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