A new model-free adaptive control algorithm is proposed by using a data-driven strategy to address the problem that it is difficult to control a nonlinear system with non-uniformly refreshed input and periodically sampled output.Firstly, the system output data is filtered by using a tracking-differentiator, and then the dynamic model is established at the equivalent dynamic equilibrium point by using this input-output data, and a model-free adaptive controller is designed.Secondly the penalty coefficients in the control law and Pseudo Jacobian Matrix(PJM)estimation are iteratively optimized by using the most rapid descent method, and the stability of the algorithm is analyzed.The advantages of it are:Reducing the influence of disturbance error and improving the ability of parameter optimization.Finally, the effectiveness of the proposed method is verified by an example of simulating a discrete-time nonlinear system with non-uniform sampling.