Photo by jontyson from unsplash
Most of adaptive iterative learning control (AILC) algorithms focus on one-dimensional (1-D) systems, rather than two-dimensional (2-D) systems. This brief is first concerned with AILC for 2-D nonlinear discrete time-varying… Click to show full abstract
Most of adaptive iterative learning control (AILC) algorithms focus on one-dimensional (1-D) systems, rather than two-dimensional (2-D) systems. This brief is first concerned with AILC for 2-D nonlinear discrete time-varying Fornasini-Marchesini system (NDTVFMS) with nonrepetitive reference trajectory under iteration-varying boundary states. By using Lyapunov analysis method, it can guarantee that the ultimate tracking error tends to zero asymptotically, and make all identified parameters and system signals to be bounded as iteration number goes to infinity. Two illustrative examples are used to validate the effectiveness of the designed AILC approach.
Journal Title: International Journal of Control, Automation and Systems
Year Published: 2020
Link to full text (if available)
Share on Social Media: Sign Up to like & get
recommendations!