Robust Multiple Model Adaptive Control Research Articles

ROBUST MULTIPLE MODEL ADAPTIVE CONTROL WITH FUZZY POSTERIOR PROBABILITY COMBINATION

This paper proposes a fuzzy posterior probability (FPP) combina-tion in a robust multiple model adaptive control (MMAC) frame-work, for linear systems subject to uncertain real parameters, eitherconstant or slowly time varying.

Algorithms for Optimal Model Distributions in Adaptive Switching Control Schemes

Several multiple model adaptive control architectures have been proposed in the literature. Despite many advances in theory, the crucial question of how to synthesize the pairs model/controller in a structurally optimal way is to a large extent not addressed. In particular, it is not clear how to place the pairs model/controller is such a way that the properties of the switching algorithm (e.g., number of switches, learning transient, final performance) are optimal with respect to some criteria. In this work, we focus on the so-called multi-model unfalsified adaptive supervisory switching control (MUASSC) scheme; we define a suitable structural optimality criterion and develop algorithms for synthesizing the pairs model/controller in such a way that they are optimal with respect to the structural optimality criterion we defined. The peculiarity of the proposed optimality criterion and algorithms is that the optimization is carried out so as to optimize the entire behavior of the adaptive algorithm, i.e., both the learning transient and the steady-state response. A comparison is made with respect to the model distribution of the robust multiple model adaptive control (RMMAC), where the optimization considers only the steady-state ideal response and neglects any learning transient.

Robust control design for automatic regulation of blood pressure

The problem of automatic administration of vasoactive drugs to a patient can be treated as a regulation problem of a system, which is characterised by large parametric uncertainty, non-Gaussian disturbances and unmodelled dynamics, yet carries strict requirements in terms of robustness and performance. A number of approaches have been proposed in the past to tackle this problem, particularly for the postoperative management of blood pressure in cardiac surgery patients. We describe the design of a robust multiple-model adaptive control (RMMAC) architecture and investigate whether this can overcome some issues observed with earlier methods. Key features of RMMAC are robust optimal controller design using an iterative μ synthesis algorithm and improved system estimation. Simulation results indicate that RMMAC is capable of avoiding transient instability and delivering performance in the face of significant parameter changes over time and large disturbances including non-zero-mean signals. The findings support further research into RMMAC as a potentially viable approach to the design of safer automatic closed-loop drug administration technologies capable of operating under challenging clinical conditions such as may arise in an intraoperative setting.

Stability Analysis of Robust Multiple Model Adaptive Control

AbstractThe 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-loop signals in a RMMAC system are bounded. It is further shown that robust performance is recovered in steady state.

Robust multiple model adaptive control: Modified using ν‐gap metric

AbstractA new robust adaptive control method is proposed, which removes the deficiencies of the classic robust multiple model adaptive control (RMMAC) using benefits of the ν‐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 method which is used as a systematic approach for subdividing the uncertainty set makes the RMMAC structure being always companion with the µ‐synthesis design method. Then in case of two or more uncertain parameters, the model set definition in the classic RMMAC is based on cumbersome ad hoc methods. Several methods based on ν‐gap metric for working out the mentioned problems are presented in this paper. To demonstrate the benefits of the proposed RMMAC method, two benchmark problems subject to unmodeled dynamics, stochastic disturbance input and sensor noise are considered as case studies. The first case‐study is a non‐minimum‐phase (NMP) system, which has an uncertain NMP zero; the second case‐study is a mass‐spring‐dashpot system that has three uncertain real parameters. In the first case‐study, five robust controller design methods (H2, H∞, QFT, H∞ loop‐shaping and µ‐synthesis) are implemented and it is shown via extensive simulations that RMMAC/ν/QFT method improves disturbance‐rejection, when compared with the classic RMMAC. In the second case‐study, two robust controller design methods (QFT and mixed µ‐synthesis) are applied and it is shown that the RMMAC/ν/QFT method improves disturbance‐rejection, when compared with RMMAC/ν/mixed−µ. Copyright © 2010 John Wiley & Sons, Ltd.

Robust Adaptive Fault-Tolerant Control of the F-14 Aircraft under Sensor Failures

This paper presents a novel fault detection and isolation (FDI) architecture applied to the lateral-directional axis of an F-14 aircraft during powered approach to landing under sensor failures. The fault-tolerant architecture employed is based on the so-called “Robust Multiple-Model Adaptive Control” (RMMAC) and hence is referred to as “RMMAC/FDI”. The results demonstrate successful stability and performance of the RMMAC/FDI architecture.

Robust multiple model adaptive control (RMMAC): a case study

AbstractWe evaluate the performance of the robust multiple model adaptive control (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 analysed in Reference (Int. J. Adaptive Control Signal Processing, in press). Copyright © 2006 John Wiley & Sons, Ltd.

Issues, progress and new results in robust adaptive control

We overview recent progress in the field of robust adaptive control with special emphasis on methodologies that use multiple-model architectures. We argue that the selection of the number of models, estimators and compensators in such architectures must be based on a precise definition of the robust performance requirements. We illustrate some of the concepts and outstanding issues by presenting a new methodology that blends robust non-adaptive mixed µ-synthesis designs and stochastic hypothesis-testing concepts leading to the so-called robust multiple model adaptive control (RMMAC) architecture. A numerical example is used to illustrate the RMMAC design methodology, as well as its strengths and potential shortcomings. The later motivated us to develop a variant architecture, denoted as RMMAC/XI, that can be effectively used in highly uncertain exogenous plant disturbance environments. Copyright © 2006 John Wiley & Sons, Ltd.