The model-free adaptive algorithm is a data-driven methodwhich does not require an accurate model of the systemhas small calculation cost and is easy to implement.The existing model-free adaptive control algorithm often adopts the cut-and-trial method or uses a fixed constant when selecting the penalty factorwhich is difficult to achieve a satisfactory control performance.To solve this problemthis paper proposes a method to online optimize the penalty factor of the control law and the pseudo partial derivative.By using the idea of iterative optimization in the steepest descent methodthe penalty factor is optimizedthe convergence speed is increased significantlyand better system performance parameters are obtained.Hencethe optimal performance of the system can be achieved.On this basisthe stability of the closed-loop system is strictly proved.FinallyMatlab simulation results show that the proposed method has better control quality and stronger anti-disturbance abilities than the existing model-free adaptive control method.