A comparative analysis of linear and nonlinear control of wave energy converter for a force control application

The aim of wave energy converters (WECs) is to harvest the energy from the ocean waves and convert into electricity. Optimizing the generator output is a vital point of research. A WEC behaves as a nonlinear system in real ocean waves and a control that approximates the behaviour of the system is required. In order to predict the behaviour of WEC, a controller is implemented with an aim to track the referenced trajectory for a force control application of the WEC. A neural model is implemented for the system identification and control of the nonlinear process with a neural nonlinear autoregressive moving average exogenous (NARMAX) model. The neural model updates the weights to reduce the error by using the Levenberg-Marquardt back-propagation algorithm for a single-input-single-output (SISO) nonlinear system. The performance of the system under the proposed scheme is compared to the same system under a PI-controller scheme, where the PI gains have been tuned accordingly, to verify the control capacity of the proposed controller. The results show a good tracking of dq (direct-quadrature) axes currents by regulating the stator currents, and hence a force control is achieved at different positions of the translator. The dynamic performance of the control is verified in a time domain analysis for the displacement of the translator

Artificial Neural Networks for loudspeaker modelling and fault detection

This thesis is the result of a collaborative project between Cardiff University and Harman/Becker Automotive Systems. It investigates the application of Artificial Neural Networks to loudspeaker fault detection and modelling of the loudspeaker transfer function. The aim was to utilise the ability of artificial neural networks to model high order nonlinear systems to generate a model of the loudspeaker transfer function which could be used in a linearisation scheme to reduce distortion in loudspeaker output. This thesis investigates a practical approach to transfer function modelling through the use of musical excitation signals. This would allow data to be collected during normal operation of the loudspeaker and, as the transfer function changes over time due to time dependent nonlinearities, would facilitate regular updating of the neural network model to incorporate these nonlinearities. It was determined that although very accurate models could be produced over long training periods, a significant compromise in ANN training set size and number of training epochs were required to reduce the ANN training duration to the required time period, which ultimately resulted in a decline in performance. The aim in the case of fault detection was to improve on current end of production line testing for loudspeaker distortion. Neural networks were trained with harmonic distortion data in order to emulate the end of line test result. Excellent classification accuracy was achieved when neural network classification results were compared to the end of line test results. An investigation was also conducted to determine if neural networks could be trained to recognise specific loudspeaker faults. In a development of the end of line test, a system of neural networks were trained to produce an output vector that described which of five frequency regions the loudspeaker distortion levels were above the limits, thus giving an indication of the possible fault.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Artificial Neural Networks for loudspeaker modelling and fault detection

This thesis is the result of a collaborative project between Cardiff University and Harman/Becker Automotive Systems. It investigates the application of Artificial Neural Networks to loudspeaker fault detection and modelling of the loudspeaker transfer function. The aim was to utilise the ability of artificial neural networks to model high order nonlinear systems to generate a model of the loudspeaker transfer function which could be used in a linearisation scheme to reduce distortion in loudspeaker output. This thesis investigates a practical approach to transfer function modelling through the use of musical excitation signals. This would allow data to be collected during normal operation of the loudspeaker and, as the transfer function changes over time due to time dependent nonlinearities, would facilitate regular updating of the neural network model to incorporate these nonlinearities. It was determined that although very accurate models could be produced over long training periods, a significant compromise in ANN training set size and number of training epochs were required to reduce the ANN training duration to the required time period, which ultimately resulted in a decline in performance. The aim in the case of fault detection was to improve on current end of production line testing for loudspeaker distortion. Neural networks were trained with harmonic distortion data in order to emulate the end of line test result. Excellent classification accuracy was achieved when neural network classification results were compared to the end of line test results. An investigation was also conducted to determine if neural networks could be trained to recognise specific loudspeaker faults. In a development of the end of line test, a system of neural networks were trained to produce an output vector that described which of five frequency regions the loudspeaker distortion levels were above the limits, thus giving an indication of the possible fault.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Artificial Neural Networks for loudspeaker modelling and fault detection

This thesis is the result of a collaborative project between Cardiff University and Harman/Becker Automotive Systems. It investigates the application of Artificial Neural Networks to loudspeaker fault detection and modelling of the loudspeaker transfer function. The aim was to utilise the ability of artificial neural networks to model high order nonlinear systems to generate a model of the loudspeaker transfer function which could be used in a linearisation scheme to reduce distortion in loudspeaker output. This thesis investigates a practical approach to transfer function modelling through the use of musical excitation signals. This would allow data to be collected during normal operation of the loudspeaker and, as the transfer function changes over time due to time dependent nonlinearities, would facilitate regular updating of the neural network model to incorporate these nonlinearities. It was determined that although very accurate models could be produced over long training periods, a significant compromise in ANN training set size and number of training epochs were required to reduce the ANN training duration to the required time period, which ultimately resulted in a decline in performance. The aim in the case of fault detection was to improve on current end of production line testing for loudspeaker distortion. Neural networks were trained with harmonic distortion data in order to emulate the end of line test result. Excellent classification accuracy was achieved when neural network classification results were compared to the end of line test results. An investigation was also conducted to determine if neural networks could be trained to recognise specific loudspeaker faults. In a development of the end of line test, a system of neural networks were trained to produce an output vector that described which of five frequency regions the loudspeaker distortion levels were above the limits, thus giving an indication of the possible fault