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Data-Driven Dynamic Output Feedback Nash Strategy for Multi-Player Non-Zero-Sum Games
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DOI：10.1007/s11424-025-4535-3.png)
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Abstract
En 中文
This paper investigates the multi-player non-zero-sum game problem for unknown linear continuous-time systems with unmeasurable states. By only accessing the data information of input and output, a data-driven learning control approach is proposed to estimate N-tuple dynamic output feedback control policies which can form Nash equilibrium solution to the multi-player non-zero-sum game problem. In particular, the explicit form of dynamic output feedback Nash strategy is constructed by embedding the internal dynamics and solving coupled algebraic Riccati equations. The coupled policy-iteration based iterative learning equations are established to estimate the N-tuple feedback control gains without prior knowledge of system matrices. Finally, an example is used to illustrate the effectiveness of the proposed approach.
Keywords:
Adaptive dynamic programming
non-zero-sum games
output feedback
policy-iteration
Journal
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2.8
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212
Citations:
2.1K