Neural Networks-Based Iterative Learning Decoupling Control for Discrete-Time Nonlinear MIMO Repetitive Systems

This article proposes a novel data-driven iterative learning decoupling controller aimed at addressing the precise tracking issues in discrete-time nonlinear multi-input-multioutput (MIMO) repetitive systems, caused by difficult-to-characterize nonlinearity, multivariate coupling, and data noise pollution. First, a dynamic linearized data model (DLDM) is established, where the estimation of the pseudo-Jacobian matrix (PJM) in prior methods neglects noise suppression. To overcome this gap, a PJM adaptive robust estimation method using a noise-tolerant zeroing neural network (NN) is designed, guaranteeing residual-free convergence and enhancing robustness against data noise. Furthermore, an iterative sliding mode observer is developed to estimate the coupling between multiple variables, aiming to obtain a decoupled DLDM for synthesizing controllers. Next, the iterative learning controller (ILC) utilizing wavelet NNs is designed, and its convergence characteristics are analyzed theoretically. Integrating ILC with decoupling strategies simplifies controller design and provides new perspectives for analyzing the convergence of the MIMO system. Finally, the developed scheme’s performance are validated through case studies. Real-time feasibility is assessed using the dSPACE rapid prototyping system.