Optimal Neural Tracking Control with Metaheuristic Parameter Identification for Uncertain Nonlinear Systems with Disturbances

Applied Sciences

Roxana Recio-Colmenares¹  KellyJoel Gurubel-Tun¹  Virgilio Zúñiga-Grajeda¹ 

[1] School of Engineering and Technological Innovation, University of Guadalajara, Campus Tonalá, Jalisco 45425, Mexico;

关键词: optimal control;    artificial neural network;    metaheuristic optimization;    nonlinear systems;   

DOI  :  10.3390/app10207073

来源: DOAJ

【 摘 要 】

In this paper, we propose an inverse optimal neural control strategy for uncertain nonlinear systems subject to external disturbances. This control strategy is developed based on a neural observer for the estimation of unmeasured states and inverse optimal control theory for trajectory tracking. The stabilization of states along the desired trajectory is ensured via a control Lyapunov function. The optimal parameters of the control law are identified by different nature-inspired metaheuristic algorithms, namely: Ant Lion Optimizer, Grey Wolf Optimizer, Harris Hawks Optimization, and Whale Optimization Algorithm. Finally, a highly nonlinear biological system subject to parameter uncertainties and external disturbances (Activated Sludge Model) is proposed to validate the control strategy. Simulation results demonstrate that the control law with Ant Lion Optimizer outperforms the other optimization methods in terms of trajectory tracking in the presence of disturbances. The control law with Harris Hawks Optimization shows a better convergence of the neural states in presence of parameter uncertainty.

【 授权许可】

Unknown