Health-Aware Model-Predictive Control of a Cooperative AGV-Based Production System

Abstract

In the paper, a new scheduling strategy for assembly systems consisting of cooperating Automated Guided Vehicles (AGVs) based on their remaining operational time is developed. The operational time is associated with state of charge and state of health of the AGV battery. While the latter is defined as a possible number of repetitions of a set of given tasks, both are impossible to measure on-line directly with conventional sensors. Therefore, a novel state-of-charge estimator is proposed, which uses battery current and voltage sensor readings. In contrast to the approaches presented in the literature, a comprehensive analysis of its convergence is provided. Subsequently, a state-of-health predictor is developed. With the above measures, a new control strategy for cooperative AGVs is proposed. It is achieved by the allocation of alternative tasks among two cooperating robots referring to the state of the accomplished tasks from the previous stage of the assembly process. The proposed method allows a predictive control of assembly processes with several constraints, e.g., productivity of each assembly station, speed of the communication, or operation capability of the robots involved in the assembling process. The final part of the paper shows an experimental study exhibiting the performance of the proposed approach.

Resilient Design of Product Service Systems with Automated Guided Vehicles

“…For PSSs with AGVs, advantageous services would ensure that, in the case of AGVs with different states of charge (SOC) or states of health (SOH), the operation load is distributed on the different AGVs in an optimum manner. In earlier research, cooperative redundant AGVs were the focus of investigation and a health-aware model predictive control scheme was developed which was able to balance the operation load-the activities-of AGVs dependent on their SOC and/or SOH [66]. Figure 15 shows an exemplary result.…”

A Novel Methodology Based on a Deep Neural Network and Data Mining for Predicting the Segmental Voltage Drop in Automated Guided Vehicle Battery Cells

“…The authors in [43] proposed a planning strategy with guaranteed convergence for AGV systems. In this strategy, AGVs interact based on their remaining operating time, including SOC and maintainability.…”

“…The authors concluded that DL methods are superior to the mathematical models, because they better account for changes in battery state parameters. The authors of [43] propose a method for setting up and analyzing the charging voltage curve based on artificial neural networks (ANNs). The proposed method was compared with existing methods of polynomial curve selection and estimation.…”

A Fuzzy Virtual Actuator for Automated Guided Vehicles

“…So far, the main points of interest were faults of the steering system [ 17 ]; it was, amongst others, proposed to model the AGVs in the form of switching linear parameter varying (LPV) systems [ 18 ]. Current research initiatives also try to incorporate the consideration of the remaining useful life (RUL) [ 19 ] into AGV control and diagnosis and extend the FDI towards the prognostics of the RUL and the respective confidence intervals [ 20 ]. The precedent research topics are summarized in Figure 1 .…”