Volume 3, Number 1, 2006
Special Issue on The UK-China Research Network on Intelligent Automation, Computing and Manufacturing (pp.1-83)
In this paper, the modelling and multi-objective optimal control of batch processes, using a recurrent neuro-fuzzy network, are presented. The recurrent neuro-fuzzy network, forms a global nonlinear long-range prediction model through the fuzzy conjunction of a number of local linear dynamic models. Network output is fed back to network input through one or more time delay units, which ensure that predictions from the recurrent neuro-fuzzy network are long-range. In building a recurrent neural network model, process knowledge is used initially to partition the processes non-linear characteristics into several local operating regions, and to aid in the initialisation of corresponding network weights. Process operational data is then used to train the network. Membership functions of the local regimes are identified, and local models are discovered via network training. Based on a recurrent neuro-fuzzy network model, a multi-objective optimal control policy can be obtained. The proposed technique is applied to a fed-batch reactor.
Multiple mobile agents with double integrator dynamics, following a leader to achieve a flocking motion formation, are studied in this paper. A class of local control laws for a group of mobile agents is proposed. Prom a theoretical proof, the following conclusions are reached: (i) agents globally align their velocity vectors with a leader, (ii) they converge their velocities to the leaders velocity, (iii) collisions among interconnected agents are avoided, and (iv) agents artificial potential functions are minimized. We model the interaction and/or communication relationship between agents by algebraic graph theory. Stability analysis is achieved by using classical Lyapunov theory in a fixed network topology, and differential inclusions and nonsmooth analysis in a switching network topology respectively. Simulation examples are provided.
A novel soft-sensor model which incorporates PCA (principal component analysis), RBF (Radial Basis Function) networks, and MSA (Multi-scale analysis), is proposed to infer the properties of manufactured products from real process variables. PCA is carried out to select the most relevant process features and to eliminate the correlations of input variables; multi-scale analysis is introduced to acquire much more information and to reduce uncertainty in the system; and RBF networks are used to characterize the nonlinearity of the process. A prediction of the melt index (MI), or quality of polypropylene produced in an actual industrial process, is taken as a case study. Research results show that the proposed method provides promising prediction reliability and accuracy.
In this paper, we study the robust fault detection problem of nonlinear systems. Based on the Lyapunov method, a robust fault detection approach for a general class of nonlinear systems is proposed. A nonlinear observer is first provided, and a sufficient condition is given to make the observer locally stable. Then, a practical algorithm is presented to facilitate the realization of the proposed observer for robust fault detection. Finally, a numerical example is provided to show the effectiveness of the proposed approach.
In this paper, a hybrid method based on rough sets and genetic algorithms, is proposed to improve the speed of robot path planning. Decision rules are obtained using rough set theory. A series of available paths are produced by training obtained minimal decision rules. Path populations axe optimised by using genetic algorithms until the best path is obtained. Experiment results show that this hybrid method is capable of improving robot path planning speed.
The identification of functional motifs in a DNA sequence is fundamentally a statistical pattern recognition problem. This paper introduces a new algorithm for the recognition of functional transcription start sites (TSSs) in human genome sequences, in which a RBF neural network is adopted, and an improved heuristic method for a 5-tuple feature viable construction, is proposed and implemented in two RBFPromoter and ImpRBFPromoter packages developed in Visual C++ 6.0. The algorithm is evaluated on several different test sequence sets. Compared with several other promoter recognition programs, this algorithm is proved to be more flexible, with stronger learning ability and higher accuracy.
A Particle Swarm Optimizer (PSO) exhibits good performance for optimization problems, although it cannot guarantee convergence to a global, or even local minimum. However, there are some adjustable parameters, and restrictive conditions, which can affect the performance of the algorithm. In this paper, the sufficient conditions for the asymptotic stability of an acceleration factor and inertia weight are deduced, the value of the inertia weight is enhanced to (-1,1). Furthermore a new adaptive PSO algorithm - Acceleration Factor Harmonious PSO (AFHPSO) is proposed, and is proved to be a global search algorithm. AFHPSO is used for the parameter design of a fuzzy controller for a linear motor driving servo system. The performance of the nonlinear model for the servo system demonstrates the effectiveness of the optimized fuzzy controller and AFHPSO.
This paper describes an approach for Grid service component mining in object-oriented legacy systems, applying Software clustering, architecture recovery, program slicing and wrapping techniques to decompose a legacy system, analyse the concerned components and integrate them into a Grid environment. The resulting components with core legacy code function in a Grid service framework.
Condition monitoring is a very important aspect in automated manufacturing processes. Any malfunction of a machining process will deteriorate production quality and efficiency. This paper presents an application of support vector machines in grinding process monitoring. The paper starts with an overview of grinding behaviour. Grinding force is analysed through a Short Time Fourier Transform (STFT) to identify features for condition monitoring. The Support Vector Machine (SVM) methodology is introduced as a powerful tool for the classification of different wheel wear situations. After training with available signal data, the SVM is able to identify the state of a grinding process. The requirement and strategy for using SVM for grinding process monitoring is discussed, while the result of the example illustrates how effective SVMs can be in determining wheel redress-life.
Machine tool technologies, especially Computer Numerical Control (CNC) High Speed Machining (HSM) have emerged as effective mechanisms for Rapid Tooling and Manufacturing applications. These new technologies are attractive for competitive manufacturing because of their technical advantages, i.e. a significant reduction in lead-time, high product accuracy, and good surface finish. However, HSM not only stimulates advancements in cutting tools and materials, it also demands increasingly sophisticated CAD/CAM software, and powerful CNC controllers that require more support technologies. This paper explores the computational requirement and impact of HSM on CNC controller, wear detection, look ahead programming, simulation, and tool management.
This article first generalizes the basic engineering phases of modern rapid prototyping processes, and then describes the techniques of data capture for data modeling and model making. The article also provides a brief overview of the photogrametric techniques of restitution of 3D objects, and highlights the difficulties and limitations of existing methods. It therefore presents a novel approach to photo-modeling for acquiring 3D model data from single 2D photorealistic images. Implementation of the approach is then described against a background of rapid prototyping processes to demonstrate the effectiveness of photo-modeling practice.
A variety of problems in operations research, performance analysis, manufacturing, and communication networks, etc., can be modelled as discrete event systems with minimum and maximum constraints. When such systems require only maximum constraints (or dually, only minimum constraints), they can be studied using linear methods based on max-plus algebra. Systems with mixed constraints are called min-max systems in which min, max and addition operations appear simultaneously. A significant amount of work on such systems can be seen in literature. In this paper we provide some new results with regard to the balance problem of min-max functions; these are the structure properties of min-max systems. We use these results in the structural stabilization. Our main results are two sufficient conditions for the balance and one sufficient condition for the structural stabilization. The block technique is used to analyse the structure of the systems. The proposed methods, based on directed graph and max-plus algebra are constructive in nature. We provide several examples to demonstrate how the methods work in practice.
This paper presents an observer-based nonlinear control method that was developed and implemented to provide accurate tracking control of a limited angle torque motor following a 50Hz reference waveform. The method is based on a robust nonlinear observer, which is used to estimate system states and perturbations and then employ input-output feedback linearization to compensate for the system nonlinearities and uncertainties. The estimation of system states and perturbations allows input-output linearization of the nonlinear system without an accurate mathematical model of nominal plant. The simulation results show that the observer-based nonlinear control method is superior in comparison with the conventional model-based state feedback linearizing controller.
The general discrete-time Single-Input Single-Output (SISO) mixed H2/l1 control problem is considered in this paper. It is found that the existing results of duality theory cannot be directly applied to this infinite dimension optimisation problem. By means of two finite dimension approximate problems, to which duality theory can be applied, the dual of the mixed H2/l1 control problem is verified to be the limit of the duals of these two approximate problems.
It is a challenge to realise an EKF-based sensorless controller for an Interior Permanent Magnet (IPM) brushless ac motor drive due to the influence of non-linearities and rotor saliency. An improved reduced-order EKF method is presented, reducing the rotor position estimation error of an IPM motor, as evident in  due to the neglect of saliency. The effectiveness of the proposed method is verified experimentally.