scholarly journals Predictive Controller Design for a Cement Ball Mill Grinding Process under Larger Heterogeneities in Clinker Using State-Space Models

Designs ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 36
Author(s):  
Sivanandam Venkatesh ◽  
Kannan Ramkumar ◽  
Rengarajan Amirtharajan
Keyword(s):  
Product Quality ◽  
State Space ◽  
Real Time ◽  
Controller Design ◽  
State Space Model ◽  
Grinding Process ◽  
Process Industries ◽  
Viable Solution ◽  
Predictive Controller ◽  
Cement Grinding

Chemical process industries are running under severe constraints, and it is essential to maintain the end-product quality under disturbances. Maintaining the product quality in the cement grinding process in the presence of clinker heterogeneity is a challenging task. The model predictive controller (MPC) poses a viable solution to handle the variability. This paper addresses the design of predictive controller for the cement grinding process using the state-space model and the implementation of this industrially prevalent predictive controller in a real-time cement plant simulator. The real-time simulator provides a realistic environment for testing the controllers. Both the designed state-space predictive controller (SSMPC) in this work and the generalised predictive controller (GPC) are tested in an industrially recognized real-time simulator ECS/CEMulator available at FLSmidthPvt. Ltd., Chennai, by introducing a grindability factor from 33 to 27 (the lower the grindability factor, the harder the clinker) to the clinkers. Both the predictive controllers can maintain product quality for the hardest clinkers, whereas the existing controller maintains the product quality only up to the grindability factor of 30.

Stewart Platform Model with Uncertain Parameters

2010 ◽  
Vol 164 ◽  
pp. 177-182 ◽  
Author(s):  
Lukas Březina ◽  
Tomáš Březina
Keyword(s):  
State Space ◽  
Controller Design ◽  
State Space Model ◽  
Stewart Platform ◽  
Dynamics Model ◽  
H Infinity ◽  
Uncertain Dynamics ◽  
Six Dof ◽  
Linear State ◽  
Nominal Position

The paper deals with development of uncertain dynamics model of a six DOF parallel mechanism (Stewart platform) suitable for H-infinity controller design. The model is based on linear state space models of the machine obtained by linearization of the SimMechanics model. The linearization is performed for two positions of the machine in its workspace. It is the nominal position and the position where each link of the machine reaches its maximal length. The uncertainties are then represented as differences between parameters of corresponding state-space matrices. The uncertain state space model is then obtained using upper linear fractional transformation. There are also mentioned several notes regarding H-infinity controller designed according to the obtained model.

scholarly journals Adaptive Model Predictive Vibration Control of a Cantilever Beam with Real-Time Parameter Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Gergely Takács ◽  
Tomáš Polóni ◽  
Boris Rohal’-Ilkiv
Keyword(s):  
Control System ◽  
Vibration Control ◽  
State Space ◽  
Real Time ◽  
Cantilever Beam ◽  
State Space Model ◽  
Model Parameters ◽  
Extended Kalman Filtering ◽  
Space Model ◽  
Continuous State

This paper presents an adaptive-predictive vibration control system using extended Kalman filtering for the joint estimation of system states and model parameters. A fixed-free cantilever beam equipped with piezoceramic actuators serves as a test platform to validate the proposed control strategy. Deflection readings taken at the end of the beam have been used to reconstruct the position and velocity information for a second-order state-space model. In addition to the states, the dynamic system has been augmented by the unknown model parameters: stiffness, damping constant, and a voltage/force conversion constant, characterizing the actuating effect of the piezoceramic transducers. The states and parameters of this augmented system have been estimated in real time, using the hybrid extended Kalman filter. The estimated model parameters have been applied to define the continuous state-space model of the vibrating system, which in turn is discretized for the predictive controller. The model predictive control algorithm generates state predictions and dual-mode quadratic cost prediction matrices based on the updated discrete state-space models. The resulting cost function is then minimized using quadratic programming to find the sequence of optimal but constrained control inputs. The proposed active vibration control system is implemented and evaluated experimentally to investigate the viability of the control method.

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

State-Space Model for Traffic State Estimation of a Two-Dimensional Network

2018 ◽  
Vol 13 (2) ◽  
pp. 326-337
Author(s):  
Yosuke Kawasaki ◽  
Yusuke Hara ◽  
Masao Kuwahara ◽  
◽  
◽  
...  
Keyword(s):  
State Space ◽  
Real Time ◽  
Traffic Flow ◽  
Flow Model ◽  
State Space Model ◽  
Two Dimensional ◽  
Traffic Flow Model ◽  
Space Model ◽  
Traffic State ◽  
2D Network

This study proposes a real-time monitoring method for two-dimensional (2D) networks via the fusion of probe data and a traffic flow model. In the Great East Japan Earthquake occurring on March 11, 2011, there was major traffic congestion as evacuees concentrated in cities on the Sanriku Coast. A tragedy occurred when a tsunami overtook the stuck vehicles. To evacuate safely and efficiently, the state of traffic must be monitored in real time on a 2D network, where all networks are linked. Generally, the traffic state is monitored only at observation points. However, observation data presents the risk of errors. Additionally, in the estimated traffic state of the 2D network, unlike non-intersecting road sections (i.e., one-dimensional), it is necessary to model user route choice behavior and origin/destination (OD) demand to input in the model. Therefore, in this study, we develop a state-space model that assimilates vehicle density and divergence ratio data obtained from probe vehicles in a traffic flow model that considers route choice. Our state-space model considers observational errors in the probe data and can simultaneously estimate traffic state and destination component ratio of OD demand. The result of simulated traffic model verification shows that the proposed model has good congestion estimation precision in a small-scale test network.

scholarly journals Real-time Monitoring of Dynamic Traffic States by State-Space Model

2017 ◽  
Vol 21 ◽  
pp. 42-55 ◽  
Author(s):  
Yosuke Kawasaki ◽  
Yusuke Hara ◽  
Masao Kuwahara
Keyword(s):  
State Space ◽  
Real Time ◽  
State Space Model ◽  
Real Time Monitoring ◽  
Dynamic Traffic ◽  
Space Model

A New Dynamic State Space Model for the Cylindrical Grinding Process

2005 ◽  
Author(s):  
Cheol W. Lee
Keyword(s):  
Surface Roughness ◽  
State Space ◽  
State Space Model ◽  
Dynamic State ◽  
Grinding Process ◽  
Cylindrical Grinding ◽  
Space Model ◽  
Proposed Model ◽  
Grinding Power ◽  
Actual Part

A new dynamic state space model is proposed for the in-process estimation and prediction of part qualities in the plunge cylindrical grinding process. A through review on various grinding models in literature reveals a hidden dynamic relationship among the grinding conditions, the grinding power, the surface roughness, and the part size due to the machine dynamics and the wheel wear, based on which a nonlinear state space equation is derived. After the model parameters are determined according to the reported values in literature, several simulations are run to verify that the model makes good physical sense. Since some of the output variables, such as the actual part size, may or may not be measured in industry applications, the observability is tested for different sets of outputs in order to see how each set of on-line sensors affects the observability of the model. The proposed model opens a new way of estimating the part qualities such as the surface roughness and the actual part size based on application of the state estimation algorithm to the measured outputs such as the grinding power. In addition, a long term prediction of the part qualities in batch grinding processes would be realized by simulation of the proposed model. Possible applications to monitoring and control of grinding processes are discussed along with several technical challenges lying ahead.

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