Mahdi Mahfouf

Real-Time Adaptive Automation System Based on Identification of Operator Functional State in Simulated Process Control Operations

This paper proposes a new framework for the online monitoring and adaptive control of automation in complex and safety-critical human-machine systems using psychophysiological markers relating to humans under mental stress. The starting point of this framework relates to the assessment of the so-called operator functional state using psychophysiological measures. An adaptive fuzzy model linking heart-rate variability and task load index with the subjects optimal performance has been elicited and validated offline via a series of experiments involving process control tasks simulated on an automation-enhanced Cabin Air Management System. The elicited model has been used as the basis for an online control system via the predictions of the system performance indicators corresponding to the operator stressful state. These indicators have been used by a fuzzy decision maker to modify the level of automation under which the system may operate. A real-time architecture has been developed as a platform for this approach. It has been validated in a series of human volunteer studies with promising improvement in performance.

A population adaptive based immune algorithm for solving multi-objective optimization problems

The primary objective of this paper is to put forward a general framework under which clear definitions of immune operators and their roles are provided. To this aim, a novel Population Adaptive Based Immune Algorithm (PAIA) inspired by Clonal Selection and Immune Network theories for solving multi-objective optimization problems (MOP) is proposed. The algorithm is shown to be insensitive to the initial population size; the population and clone size are adaptive with respect to the search process and the problem at hand. It is argued that the algorithm can largely reduce the number of evaluation times and is more consistent with the vertebrate immune system than the previously proposed algorithms. Preliminary results suggest that the algorithm is a valuable alternative to already established evolutionary based optimization algorithms, such as NSGA II, SPEA and VIS.

Online elicitation of Mamdani-type fuzzy rules via TSK-based generalized predictive control

Many synergies have been proposed between soft-computing techniques, such as neural networks (NNs), fuzzy logic (FL), and genetic algorithms (GAs), which have shown that such hybrid structures can work well and also add more robustness to the control system design. In this paper, a new control architecture is proposed whereby the on-line generated fuzzy rules relating to the self-organizing fuzzy logic controller (SOFLC) are obtained via integration with the popular generalized predictive control (GPC) algorithm using a Takagi-Sugeno-Kang (TSK)-based controlled autoregressive integrated moving average (CARIMA) model structure. In this approach, GPC replaces the performance index (PI) table which, as an incremental model, is traditionally used to discover, amend, and delete the rules. Because the GPC sequence is computed using predicted future outputs, the new hybrid approach rewards the time-delay very well. The new generic approach, named generalized predictive self-organizing fuzzy logic control (GPSOFLC), is simulated on a well-known nonlinear chemical process, the distillation column, and is shown to produce an effective fuzzy rule-base in both qualitative (minimum number of generated rules) and quantitative (good rules) terms.

A hybrid hierarchical decision support system for cardiac surgical intensive care patients. Part I: Physiological modelling and decision support system design

OBJECTIVEnTo develop a clinical decision support system (CDSS) that models the different levels of the clinicians decision-making strategies when controlling post cardiac surgery patients weaned from cardio pulmonary bypass.nnnMETHODSnA clinical trial was conducted to define and elucidate an expert anesthetists decision pathway utilised in controlling this patient population. This data and derived knowledge were used to elicit a decision-making model. The structural framework of the decision-making model is hierarchical, clearly defined, and dynamic. The decision levels are linked to five important components of the cardiovascular physiology in turn, i.e. the systolic blood pressure (SBP), central venous pressure (CVP), systemic vascular resistance (SVR), cardiac output (CO), and heart rate (HR). Progress down the hierarchy is dependent upon the normalisation of each physiological parameter to a value pre-selected by the clinician via fluid, chronotropes or inotropes. Since interventions at each and every level cause changes and disturbances in the other components, the proposed decision support model continuously refers back decision outcomes back to the SBP which is considered to be the overriding supervisory safety component in this hierarchical decision structure. The decision model was then translated into a computerised decision support system prototype and comprehensively tested on a physiological model of the human cardiovascular system. This model was able to reproduce conditions experienced by post-operative cardiac surgery patients including hypertension, hypovolemia, vasodilation and the systemic inflammatory response syndrome (SIRS).nnnRESULTSnIn all the simulated patients scenarios considered the CDSS was able to initiate similar therapeutic interventions to that of the expert, and as a result, was also able to control the hemodynamic parameters to the prescribed target values.

Imprecise knowledge based design and development of titanium alloys for prosthetic applications

Imprecise knowledge on the composition-processing-microstructure-property correlation of titanium alloys combined with experimental data are used for developing rule based models for predicting the strength and elastic modulus of titanium alloys. The developed models are used for designing alloys suitable for orthopedic and dental applications. Reduced Space Searching Algorithm is employed for the multi-objective optimization to find composition, processing and microstructure of titanium alloys suitable for orthopedic applications. The conflicting requirements attributes of the alloys for this particular purpose are high strength with low elastic modulus, along with adequate biocompatibility and low costs. The Pareto solutions developed through multi-objective optimization show that the preferred compositions for the fulfilling the above objectives lead to β or near β-alloys. The concept of decision making employed on the solutions leads to some compositions, which should provide better combination of the required attributes. The experimental development of some of the alloys has been carried out as guided by the model-based design methodology presented in this research. Primary characterizations of the alloys show encouraging results in terms of the mechanical properties.

A new holistic systems approach to the design of heat treated alloy steels using a biologically inspired multi-objective optimisation algorithm

The primary objective of this paper is to introduce a new holistic approach to the design of alloy steels based on a biologically inspired multi-objective immune optimisation algorithm. To this aim, a modified population adaptive based immune algorithm (PAIA2) and a multi-stage optimisation procedure are introduced, which facilitate a systematic and integrated fuzzy knowledge extraction process. The extracted (interpretable) fuzzy models are able to fully describe the mechanical properties of the investigated alloy steels. With such knowledge in hand, locating the ‘best’ processing parameters and the corresponding chemical compositions to achieve certain pre-defined mechanical properties of steels is possible. The research has also enabled to unravel the power of multi-objective optimisation (MOP) for automating and simplifying the design of the heat treated alloy steels and hence to achieve ‘right-first-time’ production.

Pole-Placement for Collocated Control of Flexible Structures

Pole assignment is one of the central problems in most control systems designs. In relation to low-authority active flexible structural systems where robustness is difficult to achieve primarily due to many closely-spaced low-frequency lightly damped modes, pole-placement techniques are commonly used to design constant gain collocated controllers that will ensure that the transient phenomenon of the structure dies down sufficiently fast. In this paper, the active control of flexible structures is presented for collocated control design using a modified pole-placement approach that reduces the control efforts significantly. The effectiveness of employing the proposed approach is demonstrated using tensegrity structures.

Hybrid-modelling of compact tension energy in high strength pipeline steel using a Gaussian Mixture Model based error compensation

In material science studies, it is often desired to know in advance the fracture toughness of a material, which is related to the released energy during its compact tension (CT) test to prevent catastrophic failure. In this paper, two frameworks are proposed for automatic model elicitation from experimental data to predict the fracture energy released during the CT test of X100 pipeline steel. The two models including an adaptive rule-based fuzzy modelling approach and a double-loop based neural network model, relate the load, crack mouth opening displacement (CMOD) and crack length to the released energies during this test. The relationship between how fracture is propagated and the fracture energy is further investigated in greater detail. To improve the performances of the models, a Gaussian Mixture Model (GMM)-based error compensation strategy which enables one monitor the error distributions of the predicted result is integrated in the model validation stage. This can help isolate the error distribution pattern and to establish the correlations with the predictions from the deterministic models. This is the first time a data-driven approach has been used in this fashion on an application that has conventionally been handled using finite element methods or physical models.

A 3D cellular automata model of the abnormal grain growth in austenite

A three-dimensional model relating to the abnormal grain growth in austenite is developed in the presented work, based on the cellular automata technique with the use of a local transition function. The model allows consideration of both anisotropy of energy and mobility of grain boundaries and shows the oscillation motion of the boundaries at the stagnation stage. Calibration of the model in relation to experimental data for austenite has allowed the calculation of the quantitative parameters of the system, such as Gibbs energy, driving and pinning forces, grain boundary velocity and mobility, duration of the incubation period of the abnormal grain growth and the critical grain size. The derived data allows the quantitative description of the kinetics of the secondary recrystallisation process in austenite and can be used for the deeper understanding of the abnormal grain growth phenomenon in metals.

Transparent predictive modelling of the twin screw granulation process using a compensated interval type-2 fuzzy system

Graphical abstract Figure. No caption available. Abstract In this research, a new systematic modelling framework which uses machine learning for describing the granulation process is presented. First, an interval type‐2 fuzzy model is elicited in order to predict the properties of the granules produced by twin screw granulation (TSG) in the pharmaceutical industry. Second, a Gaussian mixture model (GMM) is integrated in the framework in order to characterize the error residuals emanating from the fuzzy model. This is done to refine the model by taking into account uncertainties and/or any other unmodelled behaviour, stochastic or otherwise. All proposed modelling algorithms were validated via a series of Laboratory‐scale experiments. The size of the granules produced by TSG was successfully predicted, where most of the predictions fit within a 95% confidence interval.