An adaptive reconfigurable flight control system using the mode switching of multiple models is studied. The conventional mode switching method may not guarantee the stability and the performance of the system. In this study, modified adaptive mode switching and decision logic are proposed to improve the adaptiveness of the transient dynamics of a system while maintaining the stability of the c...

We consider the problem of determining an appropriate model set on which to design a set of controllers for a multiple model switching adaptive control scheme. We show that, given mild assumptions on the uncertainty set of linear time-invariant plant models, it is possible to determine a finite set of controllers such that for each plant in the uncertainty set, satisfactory performance will be ...

We evaluate the performance of the RMMAC methodology by considering a mass-spring-dashpot (MSD) system subject to high-frequency disturbances that strongly excite all its lightly-damped oscillatory modes. The results demonstrate the superior performance of the RMMAC and its variant RMMAC/XI architecture for a much more difficult adaptive control problem than that designed and analyzed in Ref. [...

This paper presents a fuzzy logic approach for switching multiple reference models, within the Model Reference Adaptive Control (MRAC) framework, in response to major changes in the plant operating conditions. Following a rule base, the fuzzy switching scheme effectively monitors changes in operating conditions or such drastic changes in plant. A fuzzy inference engine then fires appropriate ru...

The Robust Multiple Model Adaptive Control (RMMAC) methodology was first introduced in Fekri et al. [2006] for open-loop stable plants with parametric uncertainty and unmodeled dynamics subjected to external disturbances and measurement noise. This paper addresses the stability of RMMAC systems. We show, using concepts and analysis tools that borrow from Supervisory Control, that all closed-loo...

We propose a multi-model adaptive control scheme for uncertain linear plants based on the concept of model unfalsification. The approach relies on examining the ability of a precomputed set of plant-controller candidates and choosing the one that is best able to reproduce observed inand output signal samples. The ability to reproduce observations is measured as an easily computable signal norm....

A new robust adaptive control method is proposed, which removes the deficiencies of the classic robust multiple model adaptive control (RMMAC) using benefits of the -gap metric. First, the classic RMMAC design procedure cannot be used for systematic design for unstable plants because it uses the Baram Proximity Measure, which cannot be calculated for open-loop unstable plants. Next, the %FNARC ...

ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water...

It is difficult to realize adaptive control for some complex nonlinear processes which are operated in different environments and when operation conditions are changed frequently. In this paper we propose an identifier-based adaptive control (or indirect adaptive control). The identifier uses two effective tools: multiple models and neural networks. A hysteresis switching algorithm is applied t...