Add to ORKGAbstract — This paper considers a method for optimal input design in system identification for control. The approach addresses model predictive control (MPC). The objective of the framework is to provide the user with a model which guarantees that a specified control performance is achieved, with a given probability. We see that, even though the system is nonlinear, using linear theory in the input design can reduce the experimental effort. The method is illustrated in a minimum power input signal design in system identification of a water tank system. I
model predictive control, discrete event systems. The present contribution addresses the problem of ...
When system identification methods are used to construct mathematical models of real systems, it is ...
Mathematical models are an essential part of analysis of autonomous systemsas they ease the formulat...
Modern control designs are, with few exceptions, in some way model based. In particular, predictive ...
Model predictive control (MPC) makes use of a model of the system, therefore performances are highly...
A combined nonlinear model predictive control with extended Kalman filter strategy has been proposed...
We present a method of performing optimal input design on a process controlled by MPC. Given a model...
In control engineering, system identification is frequently used to create models from inputoutput d...
There are many aspects to consider when designing system identification experiments in control appli...
The model predictive control (MPC) technique has been widely applied in a large number of industrial...
Model predictive control (MPC) has become an increasingly popular control strategy thanks to its abi...
This paper considers optimal input design when the intended use of the identified model is to constr...
Abstract — This contribution considers one central aspect of experiment design in system identificat...
International audienceIt is well known that the quality of the parameters identified during an ident...
It is well known that the quality of the parameters identified during an identification experiment d...
model predictive control, discrete event systems. The present contribution addresses the problem of ...
When system identification methods are used to construct mathematical models of real systems, it is ...
Mathematical models are an essential part of analysis of autonomous systemsas they ease the formulat...
Modern control designs are, with few exceptions, in some way model based. In particular, predictive ...
Model predictive control (MPC) makes use of a model of the system, therefore performances are highly...
A combined nonlinear model predictive control with extended Kalman filter strategy has been proposed...
We present a method of performing optimal input design on a process controlled by MPC. Given a model...
In control engineering, system identification is frequently used to create models from inputoutput d...
There are many aspects to consider when designing system identification experiments in control appli...
The model predictive control (MPC) technique has been widely applied in a large number of industrial...
Model predictive control (MPC) has become an increasingly popular control strategy thanks to its abi...
This paper considers optimal input design when the intended use of the identified model is to constr...
Abstract — This contribution considers one central aspect of experiment design in system identificat...
International audienceIt is well known that the quality of the parameters identified during an ident...
It is well known that the quality of the parameters identified during an identification experiment d...
model predictive control, discrete event systems. The present contribution addresses the problem of ...
When system identification methods are used to construct mathematical models of real systems, it is ...
Mathematical models are an essential part of analysis of autonomous systemsas they ease the formulat...