A direct NN-approximation control for uncertain nonaffine systems with unknown control direction

In this paper, we present an adaptive neural network (NN) controller for uncertain nonaffine systems with unknown control direction. Most of the previous NN-based controllers included a damping term in the adaptive law of NN weights to ensure the closed-loop stability. The estimated error of the NN weights as well as the tracking error were therefore increased, relying not only on the size of the NN approximation error but also on the ideal NN weights. Compared with those, the proposed controller evades using the damping term through combining a novel adaptive algorithm and a switching mechanism to update the weights. The NN thus can directly approach a target controller with satisfactory accuracy even if the control direction is unknown. Stability analysis shows that the tracking error and the estimated error of NN weights both converge to small neighbors of 0 which solely depend on the NN approximation error. At last, simulations on a Duffing-Holmes chaotic model show the effectiveness of the proposed controller in comparison to another NN-based method.