Robust Near-optimal Control for Constrained Nonlinear System via Integral Reinforcement Learning

Qiu, Yuqing; Li, Yan; Wang, Zhong

doi:10.1007/s12555-021-0674-z

Cited by 4 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…13 with the controls 𝑢 1 , 𝑢 2 . The function that optimizes the performance index has parameters 𝑢 1 , 𝑢 2 that represent the optimal controls [29], [53], [54]. • 𝑢 1 𝐼 : depicts quarantine, the method of controlling contaminated people • 𝑢 2 𝑆 : represents the quarantine that is used to regulate diseased people In this case the number of infected individuals, time of recover, and cost of vaccination and quarantine, can be minimized using the following objective function.…”

Section: Strategy 4 (Quarantine and Vaccination Control)mentioning

confidence: 99%

Identification and Control of Epidemic Disease Based Neural Networks and Optimization Technique

Abougarair,

Elwefati

2023

IJRCS

View full text Add to dashboard Cite

Developing effective strategies to contain the spread of infectious diseases, particularly in the case of rapidly evolving outbreaks like COVID-19, remains a pressing challenge. The Susceptible-Infected-Recovery (SIR) model, a fundamental tool in epidemiology, offers insights into disease dynamics. The SIR system exhibits complex nonlinear relationships between the input variables (e.g., population, infection rate, recovery rate) and the output variables (e.g., the number of infected individuals over time). We employ Recurrent Neural Networks (RNNs) to model the SIR system due to their ability to capture sequential dependencies and handle time-series data effectively. RNNs, with their ability to model nonlinear functions, can capture these intricate relationships, enabling accurate predictions and understanding of the dynamics of the system. Additionally, we apply the Pontryagin Minimum Principle (PMP) based different control strategies to formulate an optimal control approach aimed at maximizing the recovery rate while minimizing the number of affected individuals and achieving a balance between minimizing costs and satisfying constraints. This can include optimizing vaccination strategies, quarantine measures, treatment allocation, and resource allocation. The findings of this research indicate that the proposed modeling and control approach shows potential for a comprehensive analysis of viral spread, providing valuable insights and strategies for disease management on a global level. By integrating epidemiological modeling with intelligent control techniques, we contribute to the ongoing efforts aimed at combating infectious diseases on a larger scale.

show abstract

Section: Strategy 4 (Quarantine and Vaccination Control)mentioning

confidence: 99%

Identification and Control of Epidemic Disease Based Neural Networks and Optimization Technique

Abougarair,

Elwefati

2023

IJRCS

View full text Add to dashboard Cite

show abstract

“…(2) The solving of nonlinear HJB equation is more complicated and diverges easily because of the high dimension and strong nonlinearity. 31 The PPC method has an error transformation process that converts the system dynamic constraint PPF into unconstrained PPF. When the original system state is close to the PPF boundary, the augmented system state tends to infinity.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, compromising three indicators is a difficult problem to solve. (2) The solving of nonlinear HJB equation is more complicated and diverges easily because of the high dimension and strong nonlinearity 31 . The PPC method has an error transformation process that converts the system dynamic constraint PPF into unconstrained PPF.…”

Section: Introductionmentioning

confidence: 99%

Parametrized guaranteed cost method with prescribed performance via critic‐only ADP for uncertain nonlinear systems and disturbances

Liu,

2024

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

In this paper, a novel parametrized guaranteed cost control (PGCC) method with prescribed performance control (PPC) is proposed for nonlinear systems with unknown bounded uncertainties and external disturbances described by Euler‐Lagrange (EL) equations. The proposed method is capable of ensuring a robust optimal performance in the presence of dynamics constraints without the specific information of nonlinear perturbation. As a consequence, the conditions in the form of disturbances and uncertainties are also relaxed. The augmented model is converted by transforming the uncertainties and disturbances in the original system to a mismatched term using asymmetric PPC. By designing and modifying the novel guaranteed cost function to account for the maximum value of the nonlinear perturbation, an infinite‐horizon PGCC problem is proposed by the unconstrained stationary optimal control problem. The relevant proposed linear parametrized Hamilton‐Jacobian‐Bellman (PHJB) equation is approximated to solve by a critic‐only neural network (NN) with efficient computationally. Uniformly ultimately bounded stability is guaranteed via a Lyapunov‐based stability analysis. Finally, numerical simulation results demonstrate the effectiveness of the proposed control scheme.

show abstract

Learning-Based Robust Output Tracking Control for Unknown Discrete-Time Nonlinear Systems with Dynamic Uncertainty

Liu,

Peng

2024

Neurocomputing

View full text Add to dashboard Cite

Robust Near-optimal Control for Constrained Nonlinear System via Integral Reinforcement Learning

Cited by 4 publications

References 33 publications

Identification and Control of Epidemic Disease Based Neural Networks and Optimization Technique

Identification and Control of Epidemic Disease Based Neural Networks and Optimization Technique

Parametrized guaranteed cost method with prescribed performance via critic‐only ADP for uncertain nonlinear systems and disturbances

Learning-Based Robust Output Tracking Control for Unknown Discrete-Time Nonlinear Systems with Dynamic Uncertainty

Contact Info

Product

Resources

About