Parameter sensitivity analysis is usually required to select the key parameters with high optimal goal before optimization carried out, especially for flux-switching permanent magnet (FSPM) machines where lot of design should be considered. Unlike traditional studies on parameter sensitivity, which are generally experience- or statistics-based, and time-consuming, this paper proposes a method F...

It is critical to estimate control-by-noise interactions in robust parameter design. This can be achieved by using a cross array, which is a cross product of a design for control factors and another design for noise factors. However, the total run size of such arrays can be prohibitively large. To reduce the run size, single arrays are proposed in the literature, where a modified effect hierarc...

A method is proposed for evaluating the effectiveness of robust parameter design methods. A hierarchical probability model is presented that enables an investigator to represent assumptions about regularities in system responses such as effect sparsity, hierarchy, and inheritance. The hierarchical probability model is subsequently used to create a population of responses to which alternative ro...

This paper tackles the problem of simultaneously designing a partition of an uncertain set and its corresponding set of multiple controllers that optimize the worst-case performance of a linear time invariant system under parametric uncertainty. The parametric uncertainty region is assumed to be convex polytopic, which is also partitioned into a set of convex polytopic local regions. It is desi...

The robust stability analysis and robust controller design for linear systems subject to time-invariant uncertainties have attracted considerable attention in robust control theory. Undoubtedly, the Lyapunov theory is one of the main approaches to deal with this problem. Description of uncertain linear time-invariant systems using convex polytope-type uncertainty has found its natural framework...

This paper proposes a Bayesian method to set tolerance or specification limits on one or more responses and obtain optimal values for a set of controllable factors. The existence of such controllable factors (or parameters) that can be manipulated by the process engineer and that affect the responses is assumed. The dependence between the controllable factors and the responses is assumed to be ...

Adjustment factors play a crucial role in Taguchi’s approach to robust parameter design. However, the notion of adjustment factors is not well defined and ambiguities exist in the selection of those factors. In this article, we propose a criterion for selecting adjustment factors. This new criterion can be used to explain and extend Taguchi’s approach. The concept is illustrated using two examp...

This paper addresses the design of robust H2 filters for linear continuoustime systems subject to parameter uncertainty in the state-space model. The uncertain parameters are supposed to belong to a given convex bounded polyhedral domain. Two methods based on parameter-dependent Lyapunov functions are proposed for designing linear stationary asymptotically stable filters that assure asymptotic ...