Transactions of the Institute of Measurement and Control

This paper is concerned with the synchronization problem for Lur’e-type dynamical complex networks with time-varying delay. The problem of non-linear time-varying delayed Lur’e-type dynamical networks are changed into the corresponding stabilization situation of dynamical error systems. Based on the improved Lyapunov–Krasovskii functional, which utilizes the information of time-varying delay adequately, some new synchronization criteria are derived via a linear matrix inequality with different sector conditions. Furthermore, by using an eigenvalue-decoupling method, high-dimension criteria are decoupled into low-dimension ones and the complexity of the criterion computation is reduced. Then the numerical examples are carried out to demonstrate the applicability and effectiveness of the proposed work through simulations.

The paper presents a method of technical state evaluation of working spaces of a marine diesel engine at the limited control susceptibility. The method foresees making a diagnosis of the engine’s working spaces on the basis of measurements of the exhaust gas pressure in the channels connecting engine cylinders with the turbocharger’s turbine. Diagnostic measurements are carried out at representative (comparative) steady loads depending on floating conditions. In the beginning of the article, the research objects are characterized, i.e. a Zviezda marine main engine M401 type and Detroit Diesel marine auxiliary engine DDA149TI type. Then, diagnostic measures used to identify the condition of the considered engines are defined. Moreover, selected results of diagnostic tests carried out on the engines in current operation on warships in the Polish Navy are demonstrated. The tested diagnostic classifiers confirmed that the elaborated method could be successfully applied for the engines not equipped with indicator valves.

The detection of loose particles inside an aerospace power supply is important to improve the reliability of the whole space system. This paper investigates the detection and material identification of loose particles within an aerospace power supply based on the particle impact noise detection (PIND) test. A stochastic resonance algorithm is employed to detect the presence of tiny particles. A learning vector quantization (LVQ)-based material identification method is proposed. Finally, experiments are conducted to demonstrate the effectiveness of the proposed technique. Experimental results show that the accuracies of particle detection and material identification are above 90% and 80%, respectively, which meets end-user requirements.

Piezoelectric actuators are widely used for precise micro-positioning. In most applications, there is contact with the environment. Sensing external forces is a challenging problem in controlling mechanical systems, especially in micro scales. The external force could cause dual hysteresis non-linearity effects, which will disrupt the actuator positioning accuracy. Therefore, an effective controller should be designed to guarantee the trajectory tracking in the presence of external forces and perturbations. In this paper, a modified Prandtl–Ishlinskii operator and its inverse is utilized for both identification and real time compensation of the hysteresis effect. A variable structure controller is proposed for trajectory tracking of the actuator position. A sliding-based observer would estimate full states from the only measurable position trajectory. An adaptive perturbation estimation approach is proposed so that external forces can be separated from disturbances. The stability of the controller in the presence of estimated states and external forces is demonstrated analytically. The experimental results depict that the proposed approach achieves precise trajectory tracking and good external force estimation.

In this paper a novel approach to the modeling and control of flow problems is considered. The main extension over existing methods is the ability to handle a local region of interest, and the capability to deal with the fluid viscosity being non-constant and unmeasurable. For the modeling part, first a number of snapshots of the fluid flow process at different viscosity values are obtained by computational fluid dynamics simulations of the Navier–Stokes equations governing the flow. Wavelet transform, thresholding and reconstruction are applied to these snapshots and it is seen that the flow process can be represented with acceptable accuracy using only the approximation coefficients of the wavelet transform. The support of the basis functions are selected to tightly cover the desired region of interest so that the flow dynamics outside the desired region do not affect the model significantly. Subspace system identification methods are used to fit a low-dimensional dynamical system model to the approximation coefficients, which yields a set of linear time invariant models, one for each breakpoint viscosity. A single uncertain model is built to capture all of the models in this set, and a robust controller is designed for this uncertain model using D-K iteration. The technique is illustrated on a sample flow configuration on a square domain where the input affects the system through the boundary conditions.

This paper aims to develop a CO₂ emission model of an acid gas incinerator using Nelder–Mead least squares support vector regression (LS-SVR). The Malaysia Department of Environment is actively imposing the Clean Air Regulation to mandate heavy industries to comply with emission limits. One of the latest measures is to mandate the installation of analytical instrumentation known as a continuous emission monitoring system (CEMS) to report the emission level online to the Department of Environment office. As a hardware-based analyser, CEMS is expensive, maintenance intensive and often unreliable. Therefore, software predictive techniques are often preferred and considered a feasible alternative to replace the CEMS for regulatory compliance. The LS-SVR model is built based on the emissions from an acid gas incinerator that operates in a liquefied natural gas complex. Simulated annealing is first used to determine the initial hyper-parameters, which are further optimized based on the performance of the model using a Nelder–Mead simplex algorithm. The LS-SVR model is shown to outperform a benchmark model based on back-propagation neural networks in both training and testing data.

In this paper we study a general consensus protocol for directed networks of double integrators with input delays. A necessary and sufficient condition is provided, under which not only the consensus is guaranteed but also the agents’ states are not exponentially diverging. Based on this condition, the delay robustness of the general protocol is analyzed. The maximal allowable upper bound of the input delay is obtained; the analysis is constructively given and different consensus modes can be regarded as special cases with different parameters. Simulation results are presented to illustrate the effectiveness of the theoretical results.

Finite-time synchronization of two different uncertain chaotic systems using a new non-singular terminal sliding mode approach is studied in this paper. Both master and slave systems are perturbed by parameter and model uncertainties, as well as external disturbances. A novel non-singular terminal sliding manifold is introduced. Based on the Lyapunov stability theory and finite-time control idea, a robust sliding mode controller is designed. It is shown that both reaching and sliding phases have the finite-time convergence property. In other words, it is proved that the state trajectories of the slave system can reach the state trajectories of the master system in a given finite time. The robustness and applicability of the proposed technique are demonstrated by two illustrative examples.

In this paper, an output feedback finite-time controller based on a finite-time stable observer is developed for a bioreactor system. The observer is proposed to estimate the biomass concentration in finite time. Based on the estimation of the biomass concentration, an output feedback controller is designed to track the reference signal in finite time. A Lyapunov function is constructed to prove that the observer is a finite-time stable observer. The finite-time stability results for cascaded systems are employed to prove that the closed-loop system satisfies the finite-time stability. Thus, the observer and the controller can be designed separately, both with finite-time convergence. Numerical simulation results demonstrate the effectiveness of the method.

This research work looks at methods for controlling a two-phase distillation column system. The present system consists of a vertical column with a number of trays used for component separation. It also has a reboiler and a condenser; these are used to provide heating and cooling for vaporization and condensation, respectively. The proposed control scheme uses the model-based predictive control (MBPC) scheme with the internal model being produced by a set of Takagi Sugeno Kang (TSK) fuzzy-based models. The internal set of TSK fuzzy-based models are developed from a linear models predictive approach. That is, it makes a prediction of the present system using a set of TSK fuzzy-based models identified, at each instant of time. The control scheme is then evaluated using simulation and compared with a benchmark fuzzy-based PI control scheme. The novelty of the present research is to propose a new organization in system modeling and its control scheme by combining the MBPC scheme and artificial intelligence (AI)-based techniques for the purpose of improving the system performance results.

Finite element analysis has been proved a vital factor for design studies of mechanical elements. The present paper discusses the role of finite element analysis in finding the locations of strain gauges so that the performance of the force transducers may be improved. Two ring-shaped force transducers of capacities 20 and 50 kN have been developed and the strain gauges have been placed suitably at the different locations according to the stress–strain distributions. Both force transducers have been metrologically characterized according to the calibration procedure based on standard ISO 376-2004 and IS 4169-1988 (re-affirmed 2003). The metrological results have ruled that both the force transducers have significant variations in the relative hysteresis deviations, while the differences between other factors like relative deviations because of repeatability error, reproducibility error, resolution error and zero error are not significant and thereby improve metrological properties. The results have been discussed and presented here.