<p>Although the piezoelectric actuator (PEA) has the advantages of high displacement resolution and fast response speed, the positioning performance is weakened by the hysteresis nonlinearity and the unknown disturbance. To address this issue, a compound control strategy based on a well-designed temporal convolutional network (TCN) is proposed in this paper to achieve accurate trajectory tracking of the PEA. Specifically, the feedforward compensation is implemented via a TCN-based hysteresis inverse model, and the response speed and the tracking accuracy of the system is thus improved due to the powerful time series feature extraction capability of the TCN. Moreover, to reduce the damage of the modeling error and the unknown disturbance to the tracking performance, a single neuron adaptive proportional-integral-derivative controller is designed to improve the tracking accuracy and the robustness through adaptive adjustment of the control parameters, thus achieving accurate tracking of the desired trajectory. Numerous simulations and comparative experiments are conducted to fully validate the effectiveness of the proposed approach.</p>