Collaborative learning model predictive control for repetitive tasks

TL;DR

This paper introduces a cloud-based collaborative learning model predictive control framework enabling multiple agents to efficiently learn and perform repetitive tasks with changing constraints, leveraging shared data and task similarity.

Contribution

It proposes a novel cloud-integrated MPC approach that facilitates collaborative learning and task adaptation for agents in repetitive task environments.

Findings

Effective learning acceleration through shared data.

Successful handling of time-varying constraints.

Simulation results demonstrate improved performance.

Abstract

This paper presents a cloud-based learning model predictive controller that integrates three interacting components: a set of agents, which must learn to perform a finite set of tasks with the minimum possible local cost; a coordinator, which assigns the tasks to the agents; and the cloud, which stores data to facilitate the agents' learning. The tasks consist in traveling repeatedly between a set of target states while satisfying input and state constraints. In turn, the state constraints may change in time for each of the possible tasks. To deal with it, different modes of operation, which establish different restrictions, are defined. The agents' inputs are found by solving local model predictive control (MPC) problems where the terminal set and cost are defined from previous trajectories. The data collected by each agent is uploaded to the cloud and made accessible to all their peers. Likewise, similarity between tasks is exploited to accelerate the learning process. The applicability of the proposed approach is illustrated by simulation results.