Visakan Kadirkamanathan

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Foreword The subject of control system synthesis, and in particular robust control, has had a long and rich history. Since the 1980s, the topic of robust control has been on a sound mathematical foundation. The principal aim of robust control is to ensure that the performance of a control system is satisfactory, or nearly optimal, even when the system to be(More)
Previous stability analysis of the particle swarm optimizer was restricted to the assumption that all parameters are nonrandom, in effect a deterministic particle swarm optimizer. We analyze the stability of the particle dynamics without this restrictive assumption using Lyapunov stability analysis and the concept of passive systems. Sufficient conditions(More)
A suboptimal dual adaptive system is developed for control of stochastic, nonlinear, discrete time plants that are tine in the control input. The nonlinear functions are assumed to be unknown and neural networks are used to approximate them. Both Gaussian radial basis function and sigmoidal multilayer perceptron neural networks are considered and parameter(More)
Incremental Net Pro IncNet Pro with local learning feature and statistically controlled growing and pruning of the network is intro duced The architecture of the net is based on RBF networks Extended Kalman Filter algorithm and its new fast version is proposed and used as learning algorithm IncNet Pro is similar to the Resource Allocation Network described(More)
This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis(More)
This paper presents the development of a particle filtering (PF) based method for fault detection and isolation (FDI) in stochastic nonlinear dynamic systems. The FDI problem is formulated in the multiple model (MM) environment, then by combining the likelihood ratio (LR) test with the PF, a new FDI scheme is developed. The simulation results on a highly(More)
An adaptive control technique, using dynamic structure Gaussian radial basis function neural networks, that grow in time according to the location of the system's state in space is presented for the affine class of nonlinear systems having unknown or partially known dynamics. The method results in a network that is "economic" in terms of network size, for(More)
Modern conflicts are characterized by an ever increasing use of information and sensing technology, resulting in vast amounts of high resolution data. Modelling and prediction of conflict, however, remain challenging tasks due to the heterogeneous and dynamic nature of the data typically available. Here we propose the use of dynamic spatiotemporal modelling(More)