Reliable optimal control of a fed-batch fermentation process using ant colony optimisation and bootstrap aggregated neural network models

Zhang, Jie; Feng, Yiting; Al-Mahrouqi, Mahmood Hilal

doi:10.1016/b978-0-444-53711-9.50133-4

Cited by 4 publications

(1 citation statement)

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“…In ref , an adaptive terminal ILC strategy with a neural network was introduced for uncertain fed-batch processes. In ref , a reliable optimal control approach that adopts ant colony optimization was presented. In ref , a multiobjective optimization was further proposed for coke furnace systems.…”

Section: Introductionmentioning

confidence: 99%

Genetic-Algorithm-Optimization-Based Infinite Horizon Linear Quadratic Control for Injection Molding Batch Processes with Uncertainty

Wang

Gao

2018

Ind. Eng. Chem. Res.

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A new genetic-algorithm (GA)-based infinite horizon linear quadratic (IHLQ) control method is developed for injection molding batch processes under partial actuator faults and unknown disturbances. To obtain improved system performance, an extended state space model, which combines the process state and output error information, has been adopted, where extra tuning freedom is gained for the corresponding control system design by adjusting the extended weighting matrix. However, no specified rules are established for the choices of these weighting coefficients. To cope with such situations, GA is employed to optimize these weighting factors in this paper. Furthermore, the robust stability of the control system, which can be regarded as the process under uncertainty, is addressed. Finally, the validity of the proposed approach is tested on the injection velocity regulation process.

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Section: Introductionmentioning

confidence: 99%