Optimal experimental design and some related control problems

  title={Optimal experimental design and some related control problems},
  author={Luc Pronzato},

Figures from this paper

On useful redundancy in experiment design for nonlinear system identification
A recently introduced redundancy property associated to dynamic systems related inverse problems is heavily exploited to guarantee global convergence in general discrete-time nonlinear systems in which measurements are affected by bounded noise.
Experiment design for parameter estimation in nonlinear systems based on multilevel excitation
The parameter estimation based on the input signal obtained in the procedure is shown to outperform the one based on random binary signals.
Adaptive experiment design for LTI systems
This paper proposes an adaptive input design method for general single-input single-output linear-time-invariant systems and aims to address the issues for dynamic systems.
On optimal input design in system identification for control
Insight is provided in the potentials of this approach by finite impulse response model examples, for which it is possible to analyze the optimal input problem and show how the control specifications directly affect the excitation conditions in the system identification experiment.
Experiment design for parameter estimation of a dynamical system in a nonlinear model structure based on multilevel excitation
An experiment design procedure for the estimation of the parameters of a dynamical system in a nonlinear model structure is presented and is shown to outperform the one based on random binary signals.
A combined closed loop optimal design of experiments and online identification control approach
A new approach, based on any specified continuous nonlinear model, for the combined optimal closed loop control of a process and online identification of a given model parameter is proposed.
Simple and Robust Experiment Design for System Identification Using Fractional Models
This paper tackles the problems of simple and robust experiment design for system identification using elementary fractional models. It is based on a frequency domain approach and allows to determine
Evaluation of experiment designs for MIMO system identification by model predictive control
  • K. Häggblom
  • Mathematics
    2015 IEEE Conference on Control Applications (CCA)
  • 2015
Ten different experiment designs for control-oriented MIMO identification are evaluated by the performance of model predictive control (MPC) using the identified models for control of the true


An actively adaptive control policy for linear models
This work considers the quadratic optimal control of a discrete-time linear system with unknown parameters and uses the information matrix to design an actively adaptive control policy in the case of an autoregressive model.
Nonlinear experimental design based on the distribution of estimators
From experiment design to closed-loop control
Wide-sense adaptive dual control for nonlinear stochastic systems
A new approach is presented for the problem of stochastic control of nonlinear systems. It is well known that, except for the linear-quadratic problem, the optimal stochastic controller cannot be
An actively adaptive control for linear systems with random parameters via the dual control approach
A new method is presented for controlling a discrete-time linear system with, possibly time-varying, random parameters in the presence of input and output noise by using an expression of the optimal cost-to-go that exhibits the dual purpose of the control: learning and control.
Asymptotically efficient self-tuning regulators
This paper studies the problem of adaptive regulation of linear systems with white-noise disturbances. The apparent dilemma between the control objective and the need of information for parameter
The choice of estimators and experimental designs in a linear regression model according to a joint criterion of optimality
A decision theoretic model of the problem of optimal experimental design in a linear regression model is given which incldes both the choice of an estimator and the choice of an experimental design.
On the concept of excitation in least squares identification and adaptive control
Making use of martingale theory, stochastic regression theory, and certain properties of matrix polynomials in the unit shift operator, we study the problem concerning how much excitation should be
Optimal experiment design in closed loop
Abstract In this contribution we extend a recently developed framework for open loop input design to closed loop experiment design. More specifically, for the very common situation of a fixed