Iterative Learning Control for Pareto Optimal Tracking in Incompatible Multisensor Systems

Abstract

In a multisensor system, each sensor typically requires independent reference tracking while conflicts arise due to differing desired inputs for different sensors. This scenario presents an exemplary incompatible multiobjective tracking problem (IMOTP), which can be resolved as a multiobjective optimization problem (MOOP). We propose an iterative learning control strategy to resolve conflicts between sensors. First, we elaborate on the Pareto optimal solution (POS) set associated with the MOOP. Subsequently, we derive an update direction for Pareto improvement based on gradient-based algorithms for MOOP and establish a learning control algorithm ensuring that each update is a Pareto improvement and converges to a POS. These technical advancements effectively overcome tracking conflicts in multisensor systems. Illustrative simulations are provided to validate the theoretical results.