d-step-ahead predictive control of MIMO nonlinear systems with unknown input time-delay in industrial process
Article publication date: 21 June 2022
Issue publication date: 19 July 2022
This paper aims to provide a precise tracking control scheme for multi-input multi-output “MIMO” nonlinear systems with unknown input time-delay in industrial process.
The predictive control scheme based on multi-dimensional Taylor network (MTN) model is proposed. First, for the unknown input time-delay, the cross-correlation function is used to identify the input time-delay through just the input and output data. And then, the scheme of predictive control is designed based on the MTN model. It goes as follows: a recursive d-step-ahead MTN predictive model is developed to compensate the influence of time-delay, and the extended Kalman filter (EKF) algorithm is applied for its learning; the multistep predictive objective function is designed, and the optimal controlled output is determined by iterative refinement; and the convergence of MTN predictive model and the stability of closed-loop system are proved.
Simulation results show that the proposed scheme is of desirable generality and capable of performing the tracking control for MIMO nonlinear systems with unknown input time-delay in industrial process effectively, such as the continuous stirred tank reactor (CSTR) process, which provides a considerably improved performance and effectiveness. The proposed scheme promises strong robustness, low complexity and easy implementation.
For the limitations of proposed scheme, the time-invariant time-delay is only considered in time-delay identification and control schemes. And the CSTR process is only introduced to prove that the proposed scheme can adapt to practical industrial scenario.
The originality of the paper is that the proposed MTN control scheme has good tracking performance, which solves the influence of time-delay, coupling and nonlinearity and the real-time performance for MIMO nonlinear systems with unknown input time-delay.
The authors thank the Editor-in-Chief and Prof. Hong Qiao, Associate Editor and Prof. Chenguang Yang, anonymous reviewers and Prof. Lu Li for their valuable comments and suggestions. This work was supported in part by the National Natural Science Foundation of China under Grants 61673112 and 60934008, the Fundamental Research Funds for the Key Universities of China under Grants 2242017K10003 and 2242014K10031, the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) and the Postdoctoral Science Foundation of Zhejiang Province of China (No. ZJ2021074).
Yan, H.-S. and Li, C.-L. (2022), "MTN-based recursive
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