Data-Driven Dynamic Output Feedback Nash Strategy for Multi-Player Non-Zero-Sum Games

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.