Konstantinos Michail

Optimised configuration of sensors for fault tolerant control of an electro-magnetic suspension system

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.

MAGLEV suspensions - A sensor optimisation framework

In this paper, a systematic framework for optimised sensor configurations is implemented via Hinfin loop shaping procedure. The optimisation framework, gives the sensor sets that satisfy predefined user criteria and the preset constraints required for the MAGnetic LEVitated suspension performance via evolutionary algorithms. The scheme is assessed via appropriate simulations for its efficacy.

AI-Based Actuator/Sensor Fault Detection With Low Computational Cost for Industrial Applications

A low computational cost method is proposed for detecting actuator/sensor faults. Typical model-based fault detection (FD) units for multiple sensor faults require a bank of estimators [i.e., conventional Kalman estimators or artificial intelligence (AI)-based ones]. The proposed FD scheme uses an AI approach for developing of a low computational power FD unit abbreviated as iFD. In contrast to the bank-of-estimators approach, the proposed iFD unit employs a single estimator for multiple actuator/sensor FD. The efficacy of the proposed FD scheme is illustrated through a rigorous analysis of the results for a number of sensor fault scenarios on an electromagnetic suspension system.

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

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, its efficacy is tested on an electromagnetic suspension system via appropriate realistic simulations.

Optimised sensor configurations for a Maglev suspension

Abstract This paper discusses a systematic approach for selecting the minimum number of sensors for an Electromagnetic levitation system that satisfies both deterministic and stochastic performance objectives. The controller tuning is based upon the utilisation of a recently developed genetic algorithm, namely NSGAII. Two controller structures are discussed, an inner loop classical solution for illustrating the efficacy of the NSGAII tuning and a Linear quadratic gaussian structure particularly on sensor optimization.

A model-based embedded control hardware/software co-design approach for optimized sensor selection of industrial systems

In this work, a Field Programmable Gate Array (FPGA)-based embedded software platform coupled with a software-based plant, forming a Hardware-In-the-Loop (HIL), is used to validate a systematic sensor selection framework. The systematic sensor selection framework combines multi-objective optimization, Linear-Quadratic-Gaussian (LQG) control, and the nonlinear model of a maglev suspension. The physical process that represents the suspension plant is realized in a high-level system modeling environment, while the LQG controller is implemented on an FPGA. FPGAs allow to rapidly evaluate algorithms and test designs under real-world scenarios avoiding heavy time penalty associated with Hardware Description Language (HDL) simulators. Moreover, the HIL technique implemented shows a significant speed-up in the required execution time when compared to the software-based model.

Fallback options for airgap sensor fault of an electromagnetic suspension system

The paper presents a method to recover the performance of an electromagnetic suspension under faulty airgap sensor. The proposed control scheme is a combination of classical control loops, a Kalman Estimator and analytical redundancy (for the airgap signal). In this way redundant airgap sensors are not essential for reliable operation of this system. When the airgap sensor fails the required signal is recovered using a combination of a Kalman estimator and analytical redundancy. The performance of the suspension is optimised using genetic algorithms and some preliminary robustness issues to load and operating airgap variations are discussed. Simulations on a realistic model of such type of suspension illustrate the efficacy of the proposed sensor tolerant control method.

Optimal selection for sensor fault tolerant control of an EMS system via loop-shaping robust control

A systematic framework is presented for optimum sensor selection for control and fault tolerance subject to complex system requirements. The framework combines the well known robust control via loop-shaping design, the fault tolerance control concept and multiobjective optimisation. The framework is tested via realistic simulations on an Electro-Magnetic Suspension system which is a non-linear, unstable and safety-critical system with a set of non-trivial requirements.

Disturbance observer based control for nonlinear MAGLEV suspension system

This paper investigates the disturbance rejection problem of nonlinear MAGnetic LEViation (MAGLEV) suspension system with “mismatching” disturbances. Here “mismatching” refers to the disturbances that enter the system via different channel to the control input. The disturbance referring in this paper is mainly on load variation and unmodeled nonlinear dynamics. By linearizing the nonlinear MAGLEV suspension model, a linear state-space disturbance observer (DOB) is designed to estimate the lumped “mismatching” disturbances. A new disturbance compensation control method based on the estimate of DOB is proposed to solve the disturbance attenuation problem. The efficacy of the proposed approach for rejecting given disturbance is illustrated via simulations on realistic track input.

FPGA–Based Efficient Hardware/Software Co–Design for Industrial Systems with Consideration of Output Selection

Abstract This work presents a field programmable gate array (FPGA)-based embedded software platform coupled with a software-based plant, forming a hardware-in-the-loop (HIL) that is used to validate a systematic sensor selection framework. The systematic sensor selection framework combines multi-objective optimization, linear-quadratic-Gaussian (LQG)-type control, and the nonlinear model of a maglev suspension. A robustness analysis of the closed-loop is followed (prior to implementation) supporting the appropriateness of the solution under parametric variation. The analysis also shows that quantization is robust under different controller gains. While the LQG controller is implemented on an FPGA, the physical process is realized in a high-level system modeling environment. FPGA technology enables rapid evaluation of the algorithms and test designs under realistic scenarios avoiding heavy time penalty associated with hardware description language (HDL) simulators. The HIL technique facilitates significant speed-up in the required execution time when compared to its software-based counterpart model.