Sub-optimal Control Design for Second Order Non-linear Systems using Krotov Sufficient Conditions

Kumar, Avinash; Jain, Tushar

doi:10.1016/j.ifacol.2020.06.046

Cited by 1 publication

(1 citation statement)

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“…Researchers solving problems in engineering, computer science, biological, and physical sciences are developing advanced mathematical methods for control purposes. Technological advances have had a significant impact on the implementation of new analytical methods for solving nonlinear problems [1]. In most cases involving nonlinear control systems, states may not be completely measurable, which makes it difficult to solve complex control engineering problems.…”

Section: Introductionmentioning

confidence: 99%

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

Recio-Colmenares¹,

Gurubel²,

Zúñiga-Grajeda³

2020

Applied Sciences

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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.

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Section: Introductionmentioning

confidence: 99%