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.
Differently from the traditional Backstepping technique, the proposed
control scheme eliminates the “explosion of complexity” problem and
relaxes the constraint condition on the reference signal. And then, it
is warranted that the closed-loop system is semi-globally ultimately
uniformly bounded and the tracking error is always limited to the
specified region bounded by the performance functions. Two simulation
examples are used to demonstrate the feasibility of the developed
technique in this paper.