Incentive feedback Stackelberg strategy for stochastic systems with state-dependent noise

This paper designs an incentive strategy for a class of stochastic Stackelberg games in finite horizon and infinite horizon, respectively. The obtained incentive Stackelberg strategy works well in the sense that the leader will get his desired solution in the end. Different from the existing works, the state-dependent noise is considered in the design of the incentive Stackelberg strategy. Moreover, the mean-square stabilization can be guaranteed by the follower. The algorithm procedure is put forward to obtain effectively the incentive feedback Stackelberg strategy in infinite horizon. Finally, two examples are given to shed light on the effectiveness of the proposed algorithm procedure. (C) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper designs incentive strategies for stochastic Stackelberg games in both finite and infinite horizons. The strategies take into account state-dependent noise and ensure the leader's desired solution. Additionally, the follower can guarantee mean-square stabilization. An algorithm procedure is proposed to effectively obtain the incentive feedback strategy in the infinite horizon. Two examples are provided to demonstrate the effectiveness of the proposed algorithm procedure.