期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷 32, 期 8, 页码 3377-3390出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2020.3016295
关键词
Batch production systems; Electron tubes; Artificial neural networks; Computational modeling; Trajectory; Predictive control; Nonlinear dynamical systems; Control-affine feedforward neural network (CAFNN); data-driven modeling; iterative learning model predictive control (ILMPC); tube framework
类别
资金
- National Natural Science Foundation of China [61673171, U1709211, 61533013]
- Fundamental Research Funds for the Central Universities [2019QN042, 2020MS016]
The use of a control-affine feedforward neural network (CAFNN) to extract process data features from previous batches and create a nonlinear affine model for current batch control design is proposed in this article. The ILMPC formulated in a tube framework based on the CAFNN model aims to minimize modeling errors and achieve sustained accuracy in tracking the reference trajectory. The offline analytical computation of objective function gradients due to the control-affine structure improves the online computational efficiency and optimization feasibility of the tube ILMPC.
Iterative learning model predictive control (ILMPC) has been recognized as an effective approach to realize high-precision tracking for batch processes with repetitive nature because of its excellent learning ability and closed-loop stability property. However, as a model-based strategy, ILMPC suffers from the unavailability of accurate first principal model in many complex nonlinear batch systems. On account of the abundant process data, nonlinear dynamics of batch systems can be identified precisely along the trials by neural network (NN), making it enforceable to design a data-driven ILMPC. In this article, by using a control-affine feedforward neural network (CAFNN), the features in the process data of the former batch are extracted to form a nonlinear affine model for the controller design in the current batch. Based on the CAFNN model, the ILMPC is formulated in a tube framework to attenuate the influence of modeling errors and track the reference trajectory with sustained accuracy. Due to the control-affine structure, the gradients of the objective function can be analytically computed offline, so as to improve the online computational efficiency and optimization feasibility of the tube ILMPC. The robust stability and the convergence of the data-driven ILMPC system are analyzed theoretically. The simulation on a typical batch reactor verifies the effectiveness of the proposed control method.
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