Physics-informed stochastic configuration network promoted model predictive control with multi-objective optimization

Model predictive control(MPC) has attracted much attention for its superior control performance in industrial processes. However, due to the challenges in building models for industrial processes and the necessary multiple optimization objectives during the MPC optimization steps, it is difficult to achieve satisfactory control results. In this work, we propose a physics-informed stochastic configuration network(PISCN) modeling method, and a predictive control scheme based on PISCN combined with multi-objective optimization(MOO) for a class of nonlinear dynamic systems. We first develop a data-driven and physically guided hybrid modeling method that embeds physical knowledge into the loss function of stochastic configuration networks(SCN) to improve model accuracy. During the model training, we employ a parallel configuration method(PCM) to randomly assign input weights and bias of hidden nodes, reducing the number of training iterations. Secondly, the PISCN model is incorporated into MPC framework and multiple optimization objectives are considered simultaneously. Particularly, the corresponding closed-loop stability is analyzed and proven. Finally, the proposed method is applied in the dehydration reaction stage in sintering process of ternary cathode materials. The results show that compared with SCN based MPC, PISCN can obtain a more accurate model and achieve better control performance by considering multiple objectives. The sintering time and energy consumption are significantly reduced.