Computationally-Light Non-Lifted Data-Driven Norm-Optimal Iterative Learning Control

Computational complexity and model dependence are two significant limitations on lifted norm optimal iterative learning control (NOILC). To overcome these two issues and retain monotonic convergence in iteration, this paper proposes a computationally-efficient non-lifted NOILC strategy for nonlinear discrete-time systems via a data-driven approach. First, an iteration-dependent linear representation of the controlled nonlinear process is introduced by using a dynamical linearization method in the iteration direction. The non-lifted NOILC is then proposed by utilizing the input and output measurements only, instead of relying on an explicit model of the plant. The computational complexity is reduced by avoiding matrix operation in the learning law. This greatly facilitates its practical application potential. The proposed control law executes in real-time and utilizes more control information at previous time instants within the same iteration, which can help improve the control performance. The effectiveness of the non-lifted data-driven NOILC is demonstrated by rigorous analysis along with a simulation on a batch chemical reaction process.