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

Abstract

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

Neural Network-Based State Estimation for a Closed-Loop Control Strategy Applied to a Fed-Batch Bioreactor

“…In the control area, Monte Carlo methods are effective tools for the analysis of probabilistically robust control schemes [30,46]. A Monte Carlo simulation is here performed to demonstrate the effectiveness of C2 from a statistical point of view [48][49][50], when uncertainties are introduced by time-varying parameter. To this effect, 1,000 random values of and / are chosen in a range of ±25% of their nominal values.…”

Multivariable Tracking Control of a Bioethanol Process under Uncertainties

“…erefore, the following test demonstrates the technique success from a statistical point of view [67][68][69].…”

Design of a Sliding Mode Observer-Based Fault Tolerant Controller for Automobile Active Suspensions With Parameter Uncertainties and Sensor Faults

“…It is noted that sensor faults or failures may lead to partial performance losses or significant performance degradations for vehicle ASS. Consequently, fault diagnosis and fault-tolerant control (FTC) have been widely and intensively investigated [17][18][19][20] over the past decades. The authors in [17,18] designed an adaptive observer to achieve online estimation of system faults for a faulty vehicle suspension system, and presented a robust state feedback-based fault tolerant controller.…”

“…The authors in [17,18] designed an adaptive observer to achieve online estimation of system faults for a faulty vehicle suspension system, and presented a robust state feedback-based fault tolerant controller. Moreover, as for an active suspension with sensor failures, the authors in [19] designed a robust fault-tolerant controller to improve the insensitivity to sensor failure for the controlled system. Furthermore, in [20], a sliding mode observer-based FTC was designed to improve vehicle dynamics performances and system insensitivities to sensor faults.…”