Nonlinear predictive control based on echo state network and whale optimization algorithm

In response to the challenge of difficulty in modeling and low prediction accuracy for complex nonlinear processes, this paper proposes a nonlinear predictive control method that combines Echo State Networks and Whale Optimization Algorithm. Firstly, an optimized Echo State Network is utilized to construct a predictive model, capturing the nonlinear dynamic characteristics of the system. Subsequently, this predictive model is integrated with model predictive control to achieve predictive control of the system. During the control process, the Whale Optimization Algorithm is employed to solve the objective function in the rolling optimization loop, facilitating optimization adjustments to the control parameters. Finally, the proposed controller is applied to simulate and validate its effectiveness in a continuous stirred-tank reactor process. Simulation results demonstrate the efficacy of this approach.