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

Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science