Robust H-infinity Controller Design Using Frequency-Domain Data

A new robust controller design method is developed for linear time-invariant single-input single-output systems presented by their frequency response data. The performance specifications are in terms of the upper bounds on the infinity norm of weighted closed-loop frequency responses. The designed controller is robust in terms of frequency-domain disk and polytopic uncertainty as well as multimodel uncertainty. The necessary and sufficient conditions for the existence of such controllers are presented by a set of convex constraints. The practical issues to compute fixed-order rational H-infinity controllers by convex optimization techniques are discussed. The experimental results on an electromechanical system illustrate the effectiveness of the proposed method

A Robust Data-Driven Controller Design Methodology With Applications to Particle Accelerator Power Converters

A new data-driven approach using the frequency response function (FRF) of a system is proposed for designing robust-fixed structure digital controllers for particle accelerators' power converters. This design method ensures that the dynamics of a system are captured and avoid the problem of unmodeled dynamics associated with parametric models. The H ∞ robust performance condition can be represented by a set of convex constraints with respect to the parameters of a two degree of freedom RST controller. This controller is robust with respect to the frequency-dependent uncertainties of the FRF. A convex optimization algorithm is implemented to obtain the controller parameters. The effectiveness of the method is illustrated by considering two case studies that require robust controllers for achieving the desired performance

Frequency response data based optimal control using the data based symmetric root locus

This paper describes a data-based frequency domain optimal control synthesis method. Plant frequency response data is used to compute the frequency response of the controller using a spectral decomposition of the optimal return difference. The underlying cost function is selected from a databased symmetric root-locus, which gives insight in the closedloop pole locations that will be achieved by the controller. A simulation study shows the abilities of the proposed method