In the recent literature, significant and substantial efforts have been dedicated to the important area of multi-agent decision-making problems. Particularly here, the model predictive control (MPC) methodology has demonstrated its effectiveness in various applications, such as mobile robots, unmanned vehicles, and drones. Nevertheless, in many specific scenarios involving the MPC methodology , accurate and effective system identification is a commonly encountered challenge  . As a consequence, the overall system performance could be significantly weakened in outcome when the traditional MPC algorithm is adopted under such circumstances . To cater to this rather major shortcoming, this paper investigates  an alternate data-driven approach to solve the multi-agent decision-making problem . Utilizing an innovative modified methodology with suitable closed-loop input/output measurements that comply with the appropriate persistency of excitation condition, a non-parametric predictive model is suitably constructed . This non-parametric predictive model approach in the work here attains the key advantage of alleviating the rather heavy computational burden  encountered in the optimization procedures typical in alternative methodologies requiring open-loop input/output measurement data collection and parametric system identification. Then with a conservative approximation of probabilistic chance constraints for the MPC problem, a resulting deterministic optimization problem is formulated and solved efficiently and effectively. In the work here, this intuitive data-driven approach is also shown to preserve good robustness properties  . Finally, a multi-drone system is used to demonstrate the practical appeal and highly effective outcome of this promising development in achieving very good system performance .
In many specific scenarios , accurate and effective system identification is a commonly encountered challenge in the model predictive control (MPC) formulation . As a consequence, the overall system performance could be significantly degraded in outcome when the traditional MPC algorithm is adopted under those circumstances when such accuracy is lacking . To cater to this rather major shortcoming, this paper investigates a non-parametric behavior learning method for multi-agent decision making, which underpins an alternate data-driven predictive control framework . Utilizing an innovative methodology with closed-loop input/output measurements of the unknown system, the behavior of the system is learned based on the collected dataset, and thus the constructed non-parametric predictive model can be used for the determination of optimal control actions . This non-parametric predictive control framework attains the noteworthy key advantage of alleviating the heavy computational burden commonly encountered in the optimization procedures otherwise involved. Such requisite optimization procedures are typical in existing methodologies requiring open-loop input/output measurement data collection and parametric system identification. Then with a conservative approximation of probabilistic chance constraints for the MPC problem, a resulting deterministic optimization problem is formulated and solved effectively. This intuitive data-driven approach is also shown to preserve good robustness properties (even in the inevitable existence of parametric uncertainties that naturally arise in the typical system identification process) . Finally, a multi-drone system is used to demonstrate the practical appeal and highly effective outcome of this promising development .
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the recent literature, significant and substantial efforts have been dedicated to the important area of multi-agent decision-making problems. Particularly here, the model predictive control (MPC) methodology has demonstrated its effectiveness in various applications, such as mobile robots, unmanned vehicles, and drones. Nevertheless, in
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involving the MPC methodology
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in the model predictive control (MPC) formulation
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weakened
degraded
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such circumstances
those circumstances when such accuracy is lacking
A
a non-parametric behavior learning method for multi-agent decision making, which underpins
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approach to solve the multi-agent decision-making problem
predictive control framework
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modified methodology with suitable
methodology with
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that comply with the appropriate persistency of excitation condition, a
of the unknown system, the behavior of the system is learned based on the collected dataset, and thus the constructed
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is suitably constructed
can be used for the determination of optimal control actions
R
model approach in the work here attains the
control framework attains the noteworthy
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rather
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commonly
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typical in alternative
otherwise involved. Such requisite optimization procedures are typical in existing
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efficiently and effectively. In the work here, this
effectively. This
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(even in the inevitable existence of parametric uncertainties that naturally arise in the typical system identification process)
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in achieving very good system performance