A Biologically Inspired Modular-Based Iterative Learning Control Design with Provable Convergence

Daniel Hobson, Bing Chu, Xiaohao Cai

Abstract: For control problems that repeat with resets, such as batch processing and robotic trajectory tracking, iterative learning control is an established high-performance control design method. Fast learning is achievable with model-based schemes when the dynamics of the system are known to be described accurately by a prior model, but such accurate models are often difficult or expensive to obtain. The field of sensori-motor control studies the motion control systems of humans and other animals, which appear to quickly achieve accurate trajectory tracking without detailed prior knowledge. In this paper, we present a novel modular-based design inspired by the learning behaviour of these sensorimotor control systems. The ‘modules’ used are a generalisation of pre-defined orthonormal basis functions, and the parameters of these modules are learnt using an alternating direction method of multipliers. We analyse the convergence properties of the proposed design rigorously, and also discuss how this approach may be successfully applied when the reference changes over the trials. Generalising learnt skill in this way is a current challenge in iterative learning control design, and is a key benefit of the modular structure of sensorimotor-inspired schemes.

External IDs:dblp:conf/eucc/HobsonCC25