Iterative Learning Control for Pareto Optimal Tracking in Incompatible Multisensor Systems
针对多传感器系统中不同传感器参考输入冲突的问题，提出一种迭代学习控制策略，通过梯度算法确保每次更新都是帕累托改进并收敛到最优解，仿真验证了有效性。
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.