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The optimization control of nonlinear systems is crucial to improve system performance. However, uncertain disturbances of cost functions and system states make it difficult to solve optimal set points and realize stability control. For this problem, a disturbance-estimation-based robust optimization control (DERO-C) scheme is designed. First, a data-driven disturbance distribution evaluation strategy is proposed to detect the deviations in cost functions caused by disturbances. Then, the deviations are employed to approximate the distribution of uncertain disturbances. Second, an adaptive robust evolutionary algorithm is designed to solve the optimization problem. Then, the adaptive robustness indexes, derived from the disturbance distribution, are designed to guide the evolutionary process to improve the robustness of optimal set points. Third, a sliding mode controller with an adaptive reaching law is employed to track the optimal set points Finally, the optimal control performances of DERO-C are tested in the wastewater treatment process.
Zhang et al. (Fri,) studied this question.