Sparsity-Promoting Iterative Learning Control for Resource-Constrained Control Systems

TL;DR

This paper introduces sparsity-promoting iterative learning control algorithms designed for resource-constrained systems, effectively reducing control actions while maintaining accurate trajectory tracking.

Contribution

The paper presents novel iterative learning algorithms that generate sparse control sequences, addressing communication and actuation limitations in resource-constrained control systems.

Findings

Effective reduction in control actions demonstrated through simulations

Maintains reference tracking accuracy despite sparsity constraints

Applicable to systems with limited bandwidth or energy resources

Abstract

We propose novel iterative learning control algorithms to track a reference trajectory in resource-constrained control systems. In many applications, there are constraints on the number of control actions, delivered to the actuator from the controller, due to the limited bandwidth of communication channels or battery-operated sensors and actuators. We devise iterative learning techniques that create sparse control sequences with reduced communication and actuation instances while providing sensible reference tracking precision. Numerical simulations are provided to demonstrate the effectiveness of the proposed control method.