Fuzzy Logic-Based Anti-Sway Control Design for Overhead Cranes

A non-linear model for an overhead crane system is derived which takes into account a combination of a trolley and a pendulum. The overall mathematical model obtained is simulated using MATLAB-SIMULINK. Open-loop simulations run on cases depending on whether the air resistance is taken into account or not, and whether the angle of oscillation is small or large, indicate the validity of such model, hence reflecting similar trends in industries which are concerned with material handling equipment. A hand-crafted fuzzy controller, which includes two rule bases, one for position control, the other for sway-angle control, was designed and successfully implemented on the above simulated model. Preliminary results are very encouraging, and indicate the feasibility of such a two rule base control strategy. The results obtained are presented, analysed and discussed.     

Development of a parsimonious GA-NN ensemble model with a case study for Charpy impact energy prediction

A parsimonious genetic algorithm guided neural network ensemble modelling strategy is presented. Each neural network candidate model to participate in the ensemble model is structurally selected using a genetic algorithm. This provides an effective route to improve the performance of the individual neural network models as compared to more traditional neural network modelling approaches, whereby the neural network structure is selected through some trial-and-error methods or heuristics. The parsimonious neural network ensemble modelling strategy developed in this paper is highly efficient and requires very little extra computation for developing the ensemble model, thus overcoming one of the major known obstacles for developing an ensemble model. The key techniques behind the implementation of the ensemble model, include the formulation of the fitness function, the generation of the qualified neural network candidate models, as well as the specific definitions of the assemble strategies. A case study is presented which exploits a complex industrial data set relating to the Charpy impact energy for heat-treated steels, which was provided by Tata Steel Europe. Modelling results show a significant performance improvement over the previously developed models for the same data set.     

Unconstrained and constrained generalised predictive control of depth of anaesthesia during surgery

The unconstrained and constrained versions of generalised predictive control (GPC) are evaluated for on-line administration of anaesthetic drugs during surgery. First, a patient simulator was developed using a physiological model of the patient and the necessary control software was validated via a series of extensive simulation studies. The validated system was then transferred into the operating theatre for a series of clinical evaluation trials. The trials were performed with little involvement of the design engineers and good regulation of the blood pressure has been achieved using fixed-parameter as well the adaptive versions of the algorithm. Following such trials, the design engineer's assessment was that better regulation and control were obtained with the constrained algorithm and the anaesthetist's assessment was that good anaesthesia was achieved, with all patients recovering well after the operations. Furthermore, the constrained algorithm displayed good robustness properties against disturbances such as high stimulus levels and allowed for safe and economically effective administration of the drug isoflurane.     

The design of supervisory rule-based control in the operating theatre via an anaesthesia simulator

The development of online drug administration strategies in operating theatres represents a highly safety-critical situation. The usefulness of different levels of simulation prior to clinical trials has been shown in previous studies in muscle relaxant anaesthesia. Thus, in earlier work on predictive self-tuning control for muscle relaxation a dual computer real-time simulation was undertaken, subsequent to algorithm validation via off-line simulation. In the present approach a supervised rule-based control algorithm is used. The control software was implemented on the actual machine to be used in theatre, while another computer acted as a real-time patient simulator. This set-up has further advantages of providing accurate timing and also finite data accuracy via the ADC/DAC interface, or the equivalent digital lines. Also, it provides for controller design fast simulation studies compared to the real-time application. In this paper, a new architecture which combines several hierarchical levels for control (a Mamdani-type fuzzy controller), adaptation (self-organizing fuzzy logic control) and performance monitoring (fault detection, isolation and accommodation) is developed and applied to a computer real-time simulation platform for muscle relaxant anaesthesia. Experimental results showed that the proposed algorithm fulfilled successfully the requirements for autonomy, i.e. automatic control, adaptation and supervision, and proved effective in dealing with the faults and disturbances which are normally encountered in operating theatres during surgery.     

3D modelling of ferrite and austenite grain coarsening using real-valued cellular automata based on transition function

A new extended technique for 3D modelling of normal grain growth in low carbon steels is presented in this paper. This technique is based on real-valued cellular automata with the use of a local transition function that allows it to be applied to materials with both fcc and bcc lattices with the grain growth being easily simulated in ferrite as well as in austenite cases. The simulated data were calibrated with four sets of experimental data for isothermal grain coarsening in austenite, alpha- and delta-ferrites. The obtained results cogently demonstrate that there is a good agreement between simulated and experimental data across a wide range of temperatures. The new model developed in this paper, allows for the identification of two different mechanisms of grain growth in austenite. It is also shown in this paper that the newly presented approach can be used to extract additional parameters from the grain growth process, such as grain boundary velocity, mobility and driving force, which are hardly accessible even via real-time experiments.     

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 nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

In this paper, a salient search and optimisation algorithm based on a new reduced space searching strategy, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a ‘real-life’ problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. Furthermore, this new algorithm is extended to the multi-objective optimisation case. Simulation results of optimising some challenging benchmark problems suggest that both the proposed single-objective and multi-objective optimisation algorithms outperform some of the other well-known Evolutionary Algorithms (EAs). The proposed algorithms are further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of ‘right-first-time production’ of metals.     

Model fusion using fuzzy aggregation: Special applications to metal properties

To improve the modelling performance, one should either propose a new modelling methodology or make the best of existing models. In this paper, the study is concentrated on the latter solution, where a structure-free modelling paradigm is proposed. It does not rely on a fixed structure and can combine various modelling techniques in ‘symbiosis’ using a ‘master fuzzy system’. This approach is shown to be able to include the advantages of different modelling techniques altogether by requiring less training and by minimising the efforts relating optimisation of the final structure. The proposed approach is then successfully applied to the industrial problems of predicting machining induced residual stresses for aerospace alloy components as well as modelling the mechanical properties of heat-treated alloy steels, both representing complex, non-linear and multi-dimensional environments.     

Optimal interpolation analysis of leaf area index using MODIS data

A simple data analysis technique for vegetation leaf area index (LAI) using Moderate Resolution Imaging Spectroradiometer (MODIS) data is presented. The objective is to generate LAI data that is appropriate for numerical weather prediction. A series of techniques and procedures which includes data quality control, time-series data smoothing, and simple data analysis is applied. The LAI analysis is an optimal combination of the MODIS observations and derived climatology, depending on their associated errors σ_o and σ_c. The “best estimate” LAI is derived from a simple three-point smoothing technique combined with a selection of maximum LAI (after data quality control) values to ensure a higher quality. The LAI climatology is a time smoothed mean value of the “best estimate” LAI during the years of 2002-2004. The observation error is obtained by comparing the MODIS observed LAI with the “best estimate” of the LAI, and the climatological error is obtained by comparing the “best estimate” of LAI with the climatological LAI value. The LAI analysis is the result of a weighting between these two errors. Demonstration of the method described in this paper is presented for the 15-km grid of Meteorological Service of Canada (MSC)'s regional version of the numerical weather prediction model. The final LAI analyses have a relatively smooth temporal evolution, which makes them more appropriate for environmental prediction than the original MODIS LAI observation data. They are also more realistic than the LAI data currently used operationally at the MSC which is based on land-cover databases.