In this paper, the prescribed tracking performance control problem is addressed for uncertain nonlinear systems with unknown periodically time-varying parameters and arbitrary switching signal. By utilizing radial basis function neural network and fourier series expansion, an approximator is developed to overcome the difficulty of identifying unknown periodically time-varying and nonlinearly parameterized functions. To achieve the ideal tracking control performance and eliminate the influence of filtering error, a performance function is constructed in advance, and then, a novel command filter-based adaptive neural network controller and a new compensating signal are designed.