We apply the Broad Learning System (BLS) to control of nonlinear dynamic systems by proposing an iterative learning algorithm using the gradient descent method. The parameters in the enhancement layer and the top layer consisting of weights connecting the feature nodes to the enhancement nodes and the output nodes, the weights connecting the enhancement nodes to the output nodes, as well as the corresponding bias terms, are adjusted to reduce the error between the plant output and desired signal, while the remain weights and bias terms are randomly generated. The convergence of this learning algorithm is guaranteed by proving a Lyapunov stability theorem. The control performance of BLS with the proposed iterative learning algorithm on two dynamic plants indicates its effectiveness and practicability. Moreover, the BLS based controller produces correct control inputs much faster with fewer parameters, and behaves more accurate than some neuro and fuzzy-neuro models.