Abstract: The quantum particle swarm optimization algorithm exhibits good performance in solving the global optimization problem of multimodal functions,but it is prone to the "premature" problem caused by local extremum;the dynamically changing weight’s quantun-behaved particle swarm optimization（DCWQPSO）algorithm for inertial weight adaptive adjustment. It can effectively avoid the local extremum problem,but it has oscillation phenomenon in the convergence process. Aiming at the power multi-peak characteristics of PV arrays during local shading,this paper proposes a PV maximum power tracking（MPPT）control algorithm that combines DCWQPSO algorithm and INC algorithm. The algorithm uses the improved DCWQPSO algorithm to perform global search of the maximum power point,and then uses the INC algorithm to locally track the maximum power point to avoid power fluctuation in the dynamic process. The simulation results show that the proposed MPPT control algorithm has fast tracking speed,high precision and small power oscillation,which can effectively improve the maximum power tracking efficiency and dynamic quality of photovoltaic power generation system under uncertain environment,and has good robustness.