Robust control of a class of uncertain systems that have disturbances and uncertainties not satisfying “matching” condition is investigated in this paper via a disturbance observer based control (DOBC) approach. In the context of this paper, “matched” disturbances/uncertainties stand for the disturbances/uncertainties entering the system through the same channels as control inputs. By properly designing a disturbance compensation gain, a novel composite controller is proposed to counteract the “mismatched” lumped disturbances from the output channels. The proposed method significantly extends the applicability of the {DOBC} methods. Rigorous stability analysis of the closed-loop system with the proposed method is established under mild assumptions. The proposed method is applied to a nonlinear {MAGnetic} {LEViation} (MAGLEV) suspension system. Simulation shows that compared to the widely used integral control method, the proposed method provides significantly improved disturbance rejection and robustness against load variation
(Review Version)For any given system the number and location of sensors can affect the closed-loop performance as well as the reliability of the system. Hence one problem in control system design is the selection of the sensors in some optimum sense that considers both the system performance and reliability. Although some methods have been proposed that deal with some of the aforementioned aspects, in this work, a design framework dealing with both control and reliability aspects is presented. The proposed framework is able to identify the best sensor set for which optimum performance is achieved even under single or multiple sensor failures with minimum sensor redundancy. The proposed systematic framework combines linear quadratic gaussian control, fault tolerant control and multiobjective optimisation. The efficacy of the proposed framework is shown via appropriate simulations on an electro-magnetic suspension system.
Abstract-A low computational cost method is proposed for detecting actuator/sensor faults. Typical model-based fault detection units for multiple sensor faults, require a bank of estimators (i.e., conventional Kalman estimators or artificial intelligence based ones). The proposed fault detection scheme uses an artificial intelligence approach for developing of a low computational power fault detection unit abbreviated as 'iFD'. In contrast to the bank-of-estimators approach, the proposed iFD unit employs a single estimator for multiple actuator/sensor fault detection. The efficacy of the proposed fault detection scheme is illustrated through a rigorous analysis of the results for a number of sensor fault scenarios on an electromagnetic suspension system. Index Terms-fault tolerant control, actuator/sensor fault detection, reconfigurable control, loop-shaping robust control design, electromagnetic suspension, maglev trains, neural networks, artificial intelligence.
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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