A loop-shaping design procedure using H/sub infinity / synthesis

“…The nominal design of the H ∞ controller is based on the normalised coprime-factor plant description as proposed by [8], which incorporates the simple performance/robustness tradeoff obtained in loop-shaping, with the normalised Left Coprime Factorisation (LCF) robust stabilization method as a means of guaranteeing the closed-loop stability.…”

Section: Sensor Fault Tolerance Via Lsdp Design and Robustness Assessmentioning

confidence: 99%

“…The size of model uncertainty is quantified by the stability radius i.e. the stability margin [8,7]. For values of ≥ 0.25, 25% coprime factor uncertainty is allowable.…”

Section: Sensor Fault Tolerance Via Lsdp Design and Robustness Assessmentioning

confidence: 99%

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Optimised sensor selection for control and fault tolerance of electromagnetic suspension systems: A robust loop shaping approach

2014

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This paper presents a systematic design framework for selecting the sensors in an optimised manner, simultaneously satisfying a set of given complex system control requirements, i.e. optimum and robust performance as well as fault tolerant control for high integrity systems. It is worth noting that optimum sensor selection in control system design is often a non-trivial task. Among all candidate sensor sets, the algorithm explores and separately optimises system performance with all the feasible sensor sets in order to identify fallback options under single or multiple sensor faults. The proposed approach combines modern robust control design, fault tolerant control, multiobjective optimisation and Monte Carlo techniques. Without loss of generality, it's efficacy is tested on an electromagnetic suspension system via appropriate realistic simulations.Keywords: Optimised sensor selection, Robust control, Fault tolerant control, Magnetic levitation, Multi-objective optimisation, Electromagnetic Suspension IntroductionOptimum sensor selection in practical control system design can be complex process especially if the selection is done with respect to a number of properties in order to achieve robust optimum performance and reliability properties.A typical closed-loop control system is shown in Fig. 1. Typically, a system to be controlled has a number of candidate control inputs (actuators) and outputs (sensors) that could be used to control it by proper controller design using one of the existing modern control methods. Moreover the system suffers from input disturbances and uncertainties or model inaccuracies. Additionally, faults highly The problem of sensor/actuator selection has been addressed before in the literature [5] but none of the methods consider simultaneous satisfaction of the aforementioned properties except in [6] where the authors have consider both optimum performance and sensor fault tolerance using Linear Quadratic Gaussian (LQG) control. Therefore the problem is to find the 'best' set of sensors, Y o , subject to the aforementioned control properties i.e. optimum performance, robustness, fault tolerance and minimum number of sensors.The novelty in this paper relies in the fact that optimum robust performance with sensor fault tolerance is achieved by combining robust control methods, Fault Tolerant Control (FTC), MultiObjective OPtimisation (MOOP) and Monte Carlo (MC) method as illustrated in Fig. 2.Robust control design in a practical control system has a vital role because real systems have

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“…Then, several elegant solutions to H ∞ loop shaping [2] and optimal H ∞ control using Riccati equations [3] and Linear Matrix Inequalities (LMIs) [4] in state space framework were proposed. In these approaches the model uncertainty is presented by some weighting filters and they usually lead to high order controllers and so a controller order reduction should follow the design.…”

Section: Introductionmentioning

confidence: 99%

Frequency-domain robust control toolbox

Karimi

2013

52nd IEEE Conference on Decision and Control

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Abstract-A new frequency-domain robust control toolbox is introduced and compared with some features of the robust control toolbox of Matlab. A summary of the theoretical background for H∞ controller design using the spectral models is given. The main advantage of this toolbox is that almost all types of model uncertainties like unmodelled dynamics, multimodel uncertainty, spectral uncertainty and parametric uncertainty can be taken into account without conservatism. As a result, the uncertain parametric or frequency-domain identified models of the identification toolbox of Matlab can be used directly for computing robust controllers. The main commands of the new toolbox are briefly explained and the performance of the designed controllers are illustrated via some simulation and experimental results.

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A loop-shaping design procedure using H/sub infinity / synthesis

Cited by 959 publications

References 15 publications

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Robust control of electrified turbocharged diesel engines

Optimised sensor selection for control and fault tolerance of electromagnetic suspension systems: A robust loop shaping approach

Frequency-domain robust control toolbox

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