One Network Fits All: A Self-Organizing Fuzzy Neural Network Based Explicit Predictive Control Method for Multimode Process

Modern industrial processes often exhibit complex and uncertain operating state fluctuations due to the diversification of production materials, the complexity of production processes, and the harsh production environment. To address the real-time control challenge in multimode processes, this article proposes a learning framework for fuzzy neural network explicit control for full operation conditions. It includes: the fuzzy neural network-based explicit control law (FNNECL) and the neural network-based predictive model (NNPM). By adjusting the structure and parameters of FNNECL, precise control of full operation conditions is achieved. Specifically, a novel truncated radial basis activation function is first presented, and the operation condition change measurement index of data coverage is proposed to identify the mismatch degree between current and historical operation conditions. Then, for small-scale operation condition changes, an elastic weight consolidation (EWC) mechanism is introduced to ensure that FNNECL can learn control strategies for new operation conditions while maintaining control performance for historical conditions. Finally, to address large-scale operation condition changes, a radial basis function (RBF) neuron growth mechanism based on data coverage is proposed. This mechanism calculates the data coverage of fuzzy rules and selectively adds new neurons to the FNNECL. This enables the FNNECL to fit large-scale changes and effectively learn control strategies for new operation conditions. It is worth noting that without knowing the current condition, accurate control sequences can be rapidly obtained based on a single FNNECL. Extensive experiments including numerical simulation and industrial roasting process verified the feasibility and effectiveness of the proposed method.