A novel approach is proposed to the state estimation of a class of nonlinear systems which consist of known linear part and unknown nonlinear part. A linear observer is first designed then a nonlinear compensation ter...
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A novel approach is proposed to the state estimation of a class of nonlinear systems which consist of known linear part and unknown nonlinear part. A linear observer is first designed then a nonlinear compensation term in the nonlinear observer is determined using the proposed "deconvolution method". The B-spline neural network is used to model the estimated compensation term. Three simulation examples are given to compare the effectiveness of the proposed approach and some analytical approaches.
In the training of B-spline networks, iterative gradient method with a constant learning rate are often used. It is well-known that the training speed depends on the choice of the learning rate, yet few guidelines in ...
In the training of B-spline networks, iterative gradient method with a constant learning rate are often used. It is well-known that the training speed depends on the choice of the learning rate, yet few guidelines in the selection of a suitable learning rate are available in the literature. In this paper, an adaptive learning rate to update the weights of a B-spline network with a scalar or multi-output is proposed. It is shown that under certain conditions, the performance index for a training algorithm using the proposed adaptive learning rate converges to a constant as the number of iterations increases. Also, a method for computing the criterion for terminating the training is presented. Simulation examples are presented, showing that training of the networks using the adaptive training is much faster than that using a constant learning rate.
A prototype large electrical machine running on active magnetic bearings is described. This rig is controlled by a digital signal processor connected by a custom interface to MATLAB/Simulink hosted by a PC. The on-lin...
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A prototype large electrical machine running on active magnetic bearings is described. This rig is controlled by a digital signal processor connected by a custom interface to MATLAB/Simulink hosted by a PC. The on-line tuning of a PID controller is set up as an optimisation problem from MATLAB and a multiobjective genetic algorithm is used to drive the optimisation. The results of an optimisation are presented and analysed.
A block diagonal form about a nonlinear system is defined. Based on the de finition, a design method ca1led block diagonal controller (BDC) is proPOsed bo feedbacklinearization. Thus, a linearization design of a class...
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A block diagonal form about a nonlinear system is defined. Based on the de finition, a design method ca1led block diagonal controller (BDC) is proPOsed bo feedbacklinearization. Thus, a linearization design of a class of nonlinear system can be simply re-alized. The result of design has been proved by mathematical simulation of a certain anti-ship missile.
The goal of this study was to examine the ability of Neural Networks to recognise the levels of anaesthetic state of a patient. Data obtained under different levels of anaesthesia have been modelled for the purpose. I...
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The goal of this study was to examine the ability of Neural Networks to recognise the levels of anaesthetic state of a patient. Data obtained under different levels of anaesthesia have been modelled for the purpose. It is shown that inferential parameters can be used to recognise the levels of anaesthesia. In addition to demonstrating the ability of neural networks for classification we were interested in understanding the classification strategy discovered by the neural networks. Multivariate data analysis techniques, namely Principal Components Analysis and Canonical Discriminant Variates, were applied to analyse the resultant networks. (C) 1997 Elsevier Science B.V.
In this paper we present a new classification and image segmentation system based on the addition of a variational method to a classic clustering algorithm. This system constitutes an improvement respect traditional s...
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The guidance problem with terminal angular constraint is addressed. The guidance goal in the problem is to null miss distance and to achieve a desired impact attitude angle, simultaneously. In order to fulfill the gui...
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The paper presents an application-oriented robust decentralized control design technique based upon the sufficient condition for robust stability (SCRS) of systems under decentralized control (DC) which is fulfilled u...
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The paper presents an application-oriented robust decentralized control design technique based upon the sufficient condition for robust stability (SCRS) of systems under decentralized control (DC) which is fulfilled using parameter tuning of fixed-structure local controllers. Graphical interpretation of the SCRS provides two useful stability criteria used to test stability of the overall system as well as stability of subsystems. Theoretical results are illustrated in a case study.
This paper presents a genetic-based approach to multi-criteria position and configuration optimisation of mobile manipulator systems. Optimisation criteria include obstacle avoidance, least joint torque norm, manipula...
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This paper presents a genetic-based approach to multi-criteria position and configuration optimisation of mobile manipulator systems. Optimisation criteria include obstacle avoidance, least joint torque norm, manipulability and joint torque distribution. Due to the competition among various criteria, the multi-criteria optimisation problem typically exhibits many local minima. The emphasis of the paper is put on using genetic algorithms to search for global optimum and solve the minilnax problem for torque distribution. Various simulations for a system including a three-link lnanipulator mounted on a mobile platform show that the proposed genetic algorithm approach performs better than conventional methods.
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