For model-based control design, closed-loop identification gives better performance
Automatica (Journal of IFAC)
Uncertain convex programs: randomized solutions and confidence levels
Mathematical Programming: Series A and B
Robust optimal experiment design for system identification
Automatica (Journal of IFAC)
Closed loop experiment design for linear time invariant dynamical systems via LMIs
Automatica (Journal of IFAC)
Iterative minimization of H2 control performance criteria
Automatica (Journal of IFAC)
On resampling and uncertainty estimation in Linear System Identification
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Brief paper: On the uniqueness of maximum likelihood identification
Automatica (Journal of IFAC)
Brief paper: Some properties of the output error method
Automatica (Journal of IFAC)
From experiment design to closed-loop control
Automatica (Journal of IFAC)
Necessary and sufficient conditions for uniqueness of the minimum in Prediction Error Identification
Automatica (Journal of IFAC)
Hi-index | 22.14 |
The Prediction Error Method (PEM) is related to an optimization problem built on input/output data collected from the system to be identified. It is often hard to find the global solution of this optimization problem because the corresponding objective function presents local minima and/or the search space is constrained to a nonconvex set. The shape of the cost function, and hence the difficulty in solving the optimization problem, depends directly on the experimental conditions, more specifically on the spectrum of the input/output data collected from the system. Therefore, it seems plausible to improve the convergence to the global minimum by properly choosing the spectrum of the input; in this paper, we address this problem. We present a condition for convergence to the global minimum of the cost function and propose its inclusion in the input design. We present the application of the proposed approach to case studies where the algorithms tend to get trapped in nonglobal minima.