[show abstract][hide abstract] ABSTRACT: This paper presents a digital hardware realization of a real-time simulator for a complete induction machine drive using a field-programmable gate array (FPGA) as the computational engine. The simulator was developed using Very High Speed Integrated Circuit Hardware Description Language (VHDL), making it flexible and portable. A novel device-characteristic based model suitable for FPGA implementation has been proposed for the 2-level 6-pulse IGBT-based voltage-source converter (VSC). The VSC model is computed at a fixed time-step of 12.5 ns allowing a highly detailed and precise accounting of gating signals. The simulator also models a squirrel cage induction machine, a direct field-oriented control system, a space-vector pulse-width modulation scheme (SVPWM) and a measurement system. A multirate simulation of the system shows the slow (machine) as well as the fast (VSC and control) dynamic components. Real time simulation results under steady-state and transient conditions demonstrate modeling accuracy and efficiency
IEEE Transactions on Power Delivery 05/2007; · 1.52 Impact Factor
[show abstract][hide abstract] ABSTRACT: This paper presents a new algorithm for on-line artificial neural networks (ANN) training. The network topology is a standard multilayer perceptron (MLP) and the training algorithm is based on the theory of variable structure systems (VSS) and sliding mode control (SMC). The main feature of this novel procedure is the adaptability of the gain (learning rate), which is obtained from sliding mode surface so that system stability is guaranteed.
[show abstract][hide abstract] ABSTRACT: This paper presents a new algorithm for induction motor stator flux observation. The novel procedure is based on a neural network with on-line adaptive training. The network topology is a standard multilayer perceptron (MLP) network and the training algorithm is based on sliding mode control (SMC) theory. The main characteristic of this novel observer is the adaptability of the gain (learning rate), which is obtained from sliding surface so that system stability is guaranteed. The neural network stator flux observer employed here does not require previous training or speed measurement. The on-line adaptive training algorithm for the neural network is described, as well as its application to a stator flux observer of an induction motor drive. Neural observer performance is demonstrated by simulations results
Power Electronics Specialists Conference, 2005. PESC '05. IEEE 36th; 02/2005
[show abstract][hide abstract] ABSTRACT: This work presents a neural network based stator flux observer. Although the network topology is a standard multilayer perceptron network, the training algorithms are new. This paper presents two on-line training algorithms, which are based on Variable Structure Systems (VSS) theory and Sliding Mode Control (SMC). The resulting observer shows good convergence velocity and robustness with respect to the induction motor parameters for both training algorithms tested.
Industrial Electronics Society, 2003. IECON '03. The 29th Annual Conference of the IEEE; 12/2003
[show abstract][hide abstract] ABSTRACT: This paper presents a new sliding mode control algorithm that is able to guide the trajectory of a multilayer perceptron within the plane formed by the two objectives: training set error and norm of the weight vectors. The results show that the neural networks obtained are able to generate the Pareto set, from which a model with the smallest validation error is selected.
[show abstract][hide abstract] ABSTRACT: Based on the classical backpropagation weight update equations, sliding mode control theory is introduced as a technique to adapt weights of a multi-layer perceptron. As will be demonstrated, the introduction of sliding mode has resulted in a much faster version of the standard backpropagation. The results show also that the proposed algorithm presents some important features of sliding mode control, which are robustness and high speed of learning. In addition to that, this paper shows also how control theory can be applied to train neural networks.
International Journal of Neural Systems 07/1999; 9(3):187-93. · 5.05 Impact Factor
[show abstract][hide abstract] ABSTRACT: This paper shows two different methodologies, both based on
sliding mode control to train multilayer perceptron. These two methods
are compared with standard back propagation, momentum and RPROP
algorithms. The results show that the use of this control theory can
reduce the time to train multilayer perceptron and also provide an
interesting tool to analyze the limits for the parameters involved in
Neural Networks, 1999. IJCNN '99. International Joint Conference on; 02/1999
[show abstract][hide abstract] ABSTRACT: Sliding mode control is applied as a procedure to adapt weights of
a multilayer perceptron. Standard backpropagation weight update
equations are used for providing error estimates for the output and
hidden layers, similarly to the classical algorithm. The sliding mode
procedures are then introduced to adapt weights taking into
consideration the standard backpropagation errors. As demonstrated
throughout this paper, the introduction of sliding mode has resulted in
a much faster version of the standard backpropagation. The speed-up
achieved is around two times the standard version
[show abstract][hide abstract] ABSTRACT: On-line learning algorithms for artificial neural networks (ANNs)
are expected to adapt network parameters in order to face new control
situations. A new on-line learning algorithm, based on sliding mode
control (SMC) is presented. The results show that ANN inherits some of
the advantages of SMC: high speed of learning and robustness