Industrial processes often exhibit multiple operational modes, driven by variations in production conditions, raw material properties, or operational settings. A single predictive model struggles to accommodate multimodal dynamics, leading to suboptimal performance in model predictive control (MPC). In addition, most multimode MPC approaches necessitate the development of separate predictive models for each operational mode, making the effectiveness of MPC heavily reliant on the mode switching strategy. In this paper, a novel adaptive mode-switching nonlinear predictive control (AMSNPC) method is proposed to improve both modeling robustness and control accuracy in dynamic industrial scenarios. First, a multimode modeling method based on continual learning framework is investigated to eliminate the need for explicit mode switching logic. Then, an adaptive error-triggered correction mechanism is designed to automatically detects mode switching based on output prediction errors and accelerate the response speed to the target values during operational mode switching. Finally, a heuristic optimization algorithm named state transition algorithm (STA) is adopted to find the global optimal control solution for nonlinear MPC problem. A numerical simulation experiment and an industrial case study are conducted to demonstrate that the AMSNPC method achieves high control accuracy and minimizes overshoot in multimode processes control.

Data-driven anomaly detection aims to learn a decision boundary, enveloping the normal region, and separating normal data from abnormal data. However, industrial data are fairly complex due to varying feedstock and unclear transfer processes and chemical reactions. This means the decision boundary will be very complex and even intractable. In addition, process variables are high-dimensional in modern industrial processes, which strengthens the difficulty of boundary extraction. Generally, the boundary should exactly exceed the outermost samples for precisely drawing normal regions. However, what we have in most situations is just normal data contaminated by unknown noises. Hence, conventional solutions that use statistical analysis to define a normal region result in a not-so-accurate decision boundary where missing alarms occur frequently. In addition to the conventional solution based entirely on historical data, i.e., passive fault detection (PAD), an alternative detection method, active fault detection (AAD), can circumvent the above problem by stimulating system performance through the intervention of auxiliary signal. While it results in disruption of the normal operation conditions for the process, its method to enhance output performance through additional signals inspires us. In this paper, we resort to the ability of deep neural networks to fit nonlinear data and perform dimension reduction. A fault data generation strategy is proposed and the artificially generated fault data are used to regulate the model training. The new virtual fault data aids in suppressing the decision boundary closest to the outermost periphery. We propose the principles of data generation and form a network structure, implementing information fusion of genuine normal samples and virtual fault samples. Two cases demonstrate the efficiency of the proposed method.

In this article, a new robust optimization method is proposed to simultaneously optimize the expectation and variability of system performance with parametric uncertainties and fuzzy variables. The expectation-entropy model is presented to characterize the fuzzy robust optimization problem as an equivalent biobjective optimization problem. An approximate mapping method is developed to calculate the response of fuzzy variables, which improves the computational efficiency of objective functions. Then, according to the decision makers’ preference for objectives, the optimization framework based on Stackelberg game is established. A leader–follower state transition algorithm is designed to search for the equilibrium solutions. Two practical case studies are provided to show the effectiveness of the new optimization approach in both subjective judgment and objective assessment.

In this article, the problem of robust optimization is considered for dynamical systems with both constraints and uncertainties. Conditions are established to ensure the existence of solutions to the problem with both robust optimality and feasibility. The objective performance with respect to fuzzy uncertainties is evaluated based on the expectation-entropy model. A feasibility robustness analysis method is proposed to handle the uncertainties in the constraints. Using the hierarchy structure in robust design, the optimization framework based on Stackelberg–Nash game is developed. A leader–followers state transition algorithm is designed to search for the equilibrium solution. Two application examples are given to demonstrate that the proposed robust optimization method can accurately evaluate the robustness performance and successfully search for a compromise solution.