Many advanced control systems for wave energy converters (WEC's) require knowledge of incoming wave profiles to be implemented. This is due to the non-causal relationship between water elevation and force exerted on a floating body. This study focuses on the use of cascade feedforward neural networks to predict short-term incoming water surface displacements based on recently observed data in real time. Prediction networks are trained with time series data reconstructed from spectral data and recorded time series data from a data buoy deployed off the West Irish Coast. Both training methods are shown to have predictive capabilities with regression coefficients between 0.8-0.9 for a small range of sea states. Both networks prediction accuracies are tested on a large range of sea states as well. For sea states dramatically different from training data prediction accuracies decrease, but less so for the network trained on observed data. The need for accurate wave predictions in the field of WEC control design is also discussed.

Trends in the automotive control systems are toward more comprehensive and electronic control. The objective of this paper is to study the effectiveness of Internal Model Control PID (IMC-PID) method to develop a traction control system based on engine torque control for an automobile. A plant model is developed based on the governing equations and implemented in MATLAB. This is then captured in a Simulink model and simulated to evaluate performance of the open-loop system for both step and ramp inputs. A proportional-integral (PI)-type controller is then developed using IMC-PID tuning method to improve closed-loop system response. Finally, potential future applications and research opportunities are identified.