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Model-based control design plays a key role in today's industrial practice, and industry demands cuttingedge methods for identifying the necessary models. However, additional tools are needed to handle the increasingly stringent conditions on cost and performance related to identifying the models.
This contribution considers one central aspect of experiment design in system identification, namely application set approximation. When a control design is based on an estimated model, the achievable performance is related to the quality of the estimate. The degradation in control performance due to plant-modeling missmatch is quantified by an application cost function. A convex approximation of...
Model predictive control has become an increasingly popular control strategy thanks to the ability to handle constrained systems. Obtaining the required models through system identification is often a time consuming and costly process. Applications oriented experiment design is a means of reducing this effort but is often formulated in terms of the input's spectral properties. Therefore, time domain...
We present an Alternating Direction Method of Multipliers (ADMM) algorithm for solving optimization problems with an ℓ1 regularized least-squares cost function subject to recursive equality constraints. The considered optimization problem has applications in control, for example in ℓ1 regularized MPC. The ADMM algorithm is easy to implement, converges fast to a solution of moderate accuracy, and enables...
This paper extends recent results on minimum variance input signal design for identification of Finite Impulse Response (FIR) models to the Output Error (OE) system identification case. The idea is to use “the useful input parametrization” for OE models proposed by Stoica and Söderstrom (1982). The advantage of this parametrization is that the Toeplitz covariance matrix structure instrumental in the...
This paper considers a method for optimal input design in system identification for model predictive control. The objective is to provide the user with a model that guarantees, with high probability, that a specified control performance is achieved. 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...
This paper addresses the problem of segmenting a time-series with respect to changes in the mean value or in the variance. The first case is when the time data is modeled as a sequence of independent and normal distributed random variables with unknown, possibly changing, mean value but fixed variance. The main assumption is that the mean value is piecewise constant in time, and the task is to estimate...
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