Optimal control of sulphur flotation process based on machine vision ⁎. Issue 21 (2018) - He, Mingfang
Abstract: The optimal control of sulphur flotation process is difficult due to the unavailability of key process variables, complex process dynamics and the computational complexity in solving the corresponding Hamilton-Jacobi-Bellman (HJB) equation. In order to circumvent these difficulties, an optimal control scheme that integrates machine vision, control parametrization method and intelligent optimization is proposed. Machine vision is used to obtain the value of the unmeasured process variable, while control parametrization and a modified differential evolution algorithm work in a hierarchical manner to derive the optimal controller. A simulation study illustrates the effectiveness of the proposed scheme.