Dynamic Error-Triggered Adaptive Control Method and Its Industrial Applications

Industrial systems often undergo dynamic changes during operation, which presents challenges for traditional identification and control methods. These challenges arise in two aspects: variations in model structure and parameters, and differences in control objectives across diverse operating conditions. Traditional static predictive control methods face challenges in meeting the high-precision, real-time requirements in practice. In addition, control schemes with fixed parameters encounter difficulties in adapting to varying control objectives, resulting in suboptimal control performance. To address these problems, this article proposes a dynamic error-triggered adaptive control method, which can identify the operating conditions and objectives in real-time. Specifically, a dynamic error-triggered model updating mechanism is first established to detect changes in operating conditions and update the prediction model. To overcome the model mismatch during the transition process, a novel enhanced transition control (ETC) method is proposed, which designs a transition error corrector to decline prediction error and a high informative pseudo-random binary sequence (HIPRBS) input to enhance the excitation level. Considering the differences in control objectives under varying operating conditions, a fuzzy weight-adaptive method is proposed to balance heterogeneous indicators in different conditions. Two types of systems, high-speed and high-stability, are designed to validate the superiority of the proposed method. Extensive experimental results demonstrate that, compared to some state-of-the-art methods, the proposed method can efficiently and accurately identify emerging operating conditions, dynamically adjust optimization objectives, and achieve real-time control effects under varying operation conditions. Note to Practitioners-The motivation of this paper is to develop a high-precision and real-time control method for industrial systems that operate under frequently changing conditions. The proposed method can adapt to changes in model parameters and control objectives in multiple operating conditions processes. Compared with some state-of-the-art methods, this method significantly enhances the control performance in the transition process of mode switching, meeting the long-term stable operation requirements of industrial systems.