Spatial-based adaptive iterative learning control of nonlinear rotary systems with spatially periodic parametric variation

Motion control systems have found their application in various industry products. Common issues encountered when designing this type of systems are nonlinearities and uncertainties (e.g., unknown parameters, unmodeled dynamics, and disturbances). In this paper, we study controller design for a class of motion systems with rotary components. The systems are assumed to have unknown parameters which are periodic with respect to the angular displacement (or spatially periodic). The proposed design integrates the spatial-based versions of two control paradigms, i.e., adaptive control and iterative learning control. In addition, we propose a spatial-based periodic parametric adaptation law to minimize the tracking error of the system, i.e., the difference between the output angular velocity and a desired reference signal. Convergence and stability properties of the overall system are analyzed and discussed. Feasibility and effectiveness of the proposed scheme is verified using a design example with simulation.