Hierarchical Learning-Based Integrated Robust Optimal Control for Nonlinear Systems

The optimal control of nonlinear systems is crucial to improve system performance. However, the uncertainties of cost functions and systems dynamics make it difficult to solve the optimal control laws. To cope with this problem, a hierarchical learning-based integrated robust optimal control (HL-IRO...

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Vydáno v:IEEE transactions on systems, man, and cybernetics. Systems Ročník 55; číslo 5; s. 3119 - 3129
Hlavní autoři: Zhang, Jiacheng, Wang, Jingjing, Han, Honggui, Hou, Ying, Huang, Yanting
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.05.2025
Témata:
Cost function
Costs
Data learning
evolutionary computation
Heuristic algorithms
neural network
Nonlinear systems
Optimal control
Optimization
Process control
robust optimization
Robustness
System dynamics
Uncertainty
ISSN:2168-2216, 2168-2232
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Shrnutí:The optimal control of nonlinear systems is crucial to improve system performance. However, the uncertainties of cost functions and systems dynamics make it difficult to solve the optimal control laws. To cope with this problem, a hierarchical learning-based integrated robust optimal control (HL-IROC) method is proposed in this article. The merits of the proposed HL-IROC method are three aspects: First, a hierarchical learning-based integrated optimal control (HL-IOC) scheme, contains a system dynamic learning layer and a cost function learning layer, is designed to transform the original optimal control problem into an integrated optimization problem with control laws as decision variables. Then, the relationships between cost functions and control laws are captured to overcome the difficulties brought by uncertainties in the optimal control process. Second, a global-local cooperative robust evolutionary optimization (GL-CREO) algorithm is proposed to obtain the optimal control laws. Then, a global-local robust searching strategy is employed to deal with two types of uncertainties for improving the robustness of control laws. Third, the convergence analysis of HL-IOC and GL-CREO is discussed in theory. In the experiments, the effectiveness of HL-IROC is illustrated with a nonlinear system and a wastewater treatment process.
ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2025.3538841