Recently, probabilistic methods and statistical learning theory have been shown to provide approximate solutions to \diicult" control problems. Unfortunately, the number of samples required (based on VC-dimension results) in order to guarantee stringent performance levels may be prohibitively large. This paper introduces bootstrap learning methods and the… (More)
This paper shows how certain robust multi-objective feedback design problems can be reduced to quantifier elimination (QE) problems. In particular it is shown how robust stabilization and robust frequency domain performance specifications can be reduced to systems of polynomial inequalities with suitable logic quantifiers, ∀ and ∃. Because of computational… (More)
This paper reviews the static output feedback problem in the control of linear, time-invariant (LTI) systems. It includes analytical and computational methods and presents in a uniied fashion, the knowledge gained in the decades of research into this most important problem.
This paper presents a new approach to the synthesis of stabilizing controllers for a class of one-parameter interval plants. The approach is based on the concept of analytic-real-positive (ARP) functions.
It has recently become clear that many control problems are too dicult to admit analytic solutions. New results have also emerged to show that the computational complexity of somè`solved'' control problems is prohibitive. Many of these control problems can be reduced to decidability problems or to optimization questions. Even though such questions may be… (More)
In this brief paper we present sufficient conditions for the existence of a single stable controller to stabilize a set of n As is well known this is equivalent to the existence of a single controller, not necessarily stable, to stabilize n + 1 plants (simultaneous stabilization). The basic assumption required in the current paper is that all the plants… (More)
— This paper considers a high efficiency energy management control strategy for a hybrid fuel cell vehicle. The proposed switching architecture consists of a bank of neural network based controllers designed using statistical learning theory. The use of different power sources and the presence of different constraints make the power management problem… (More)
A two-level fuzzy hierarchical controller which utilizes both spatial and temporal measured data for control of distributed parameter systems is proposed in this paper. The control architecture consists of a high level feature extraction module and a low level controller for determining of the control input. In particular, a uniformly distributed aluminum… (More)