Neural network backstepping control of OWC wave energy system

This paper investigates the application of Neural Network Backstepping Control (NN-BSC) for enhancing the rotational speed control of Oscillating Water Column (OWC) wave energy systems. Traditional control methods face limitations when dealing with nonlinearities, irregular wave conditions, and actuator disturbances. To address these challenges, this research paper introduces a Chebyshev NN within the BSC framework, leveraging its high approximation accuracy and computational efficiency. The design of the NN-BSC involves estimating the disturbance term using the Chebyshev NN and validating the stability OWC control system through Lyapunov analysis. The proposed NN-BSC law effectively handles nonlinearities and improves system robustness under dynamic conditions. Numerical simulations have been conducted using MATLAB/SIMULINK to compare the performance of the uncontrolled OWC system, conventional PI and BSC, and NN-BSC, under scenarios with and without actuator disturbances. The parameters for PI, BSC, and NN-BSC are optimized using a Particle Swarm Optimization (PSO) algorithm, which minimizes a fitness function defined by the Integral Squared Error (ISE). Results indicate that NN-BSC achieves smoother rotor speed tracking, particularly under actuator disturbances, where the conventional PI and BSC exhibits significant performance degradation in terms of ISE. Under actuator disturbance scenarios: (1) NN-BSC achieved the lowest ISE value of 22.5433, outperforming PI (40.6381) and BSC (37.1192), and (2) NN-BSC demonstrated the lowest maximum peak overshoot (0.9651 rad/s) and fastest settling time (0.0561 s).