Add to ORKGModel predictive control (MPC) makes use of a model of the system, therefore performances are highly dependent on the accuracy of the model chosen. Applications oriented optimal input design enables optimization of the system identification experiments. In this thesis a method of system identification for MPC applications is simulated on a multivariable nonlinear system consisting of four interconnected water tank
In control engineering, system identification is frequently used to create models from inputoutput d...
Abstract — This contribution considers one central aspect of experiment design in system identificat...
A three-tank process has difficulty in controller design because of nonlinear flow and interactions ...
Model predictive control (MPC) has become an increasingly popular control strategy thanks to its abi...
Abstract — This paper considers a method for optimal input design in system identification for contr...
The model predictive control (MPC) technique has been widely applied in a large number of industrial...
Modern control designs are, with few exceptions, in some way model based. In particular, predictive ...
We present a method of performing optimal input design on a process controlled by MPC. Given a model...
A combined nonlinear model predictive control with extended Kalman filter strategy has been proposed...
The main goal of estimating models for industrial applications is to guarantee the cheapest system i...
Models are used in control systems for more than thirty years ago. Among them, Model Predictive Cont...
105 p.Model Predictive Control (MPC) is an advanced control methodology that offers an efficient con...
105 p.Model Predictive Control (MPC) is an advanced control methodology that offers an efficient con...
Abstract — This contribution considers one central aspect of experiment design in system identificat...
Model Predictive Controller (MPC) technology has been researched and developed to meet varied demand...
In control engineering, system identification is frequently used to create models from inputoutput d...
Abstract — This contribution considers one central aspect of experiment design in system identificat...
A three-tank process has difficulty in controller design because of nonlinear flow and interactions ...
Model predictive control (MPC) has become an increasingly popular control strategy thanks to its abi...
Abstract — This paper considers a method for optimal input design in system identification for contr...
The model predictive control (MPC) technique has been widely applied in a large number of industrial...
Modern control designs are, with few exceptions, in some way model based. In particular, predictive ...
We present a method of performing optimal input design on a process controlled by MPC. Given a model...
A combined nonlinear model predictive control with extended Kalman filter strategy has been proposed...
The main goal of estimating models for industrial applications is to guarantee the cheapest system i...
Models are used in control systems for more than thirty years ago. Among them, Model Predictive Cont...
105 p.Model Predictive Control (MPC) is an advanced control methodology that offers an efficient con...
105 p.Model Predictive Control (MPC) is an advanced control methodology that offers an efficient con...
Abstract — This contribution considers one central aspect of experiment design in system identificat...
Model Predictive Controller (MPC) technology has been researched and developed to meet varied demand...
In control engineering, system identification is frequently used to create models from inputoutput d...
Abstract — This contribution considers one central aspect of experiment design in system identificat...
A three-tank process has difficulty in controller design because of nonlinear flow and interactions ...