In this paper, the adaptive prescribed performance tracking control of nonlinear asymmetric input saturated systems in strict-feedback form is addressed under the consideration of model uncertainties and external disturbances. A radial basis function neural network (RBF-NN) is utilized to handle the model uncertainties. By prescribed performance functions, the transient performance of the system can be guaranteed. The continuous Gaussian error function is represented as an approximation of asymmetric saturation nonlinearity such that the backstepping technique can be leveraged in the control design. Based on the Lyapunov synthesis, residual function approximation inaccuracies and external disturbances are compensated by constructed adaptive control laws. As a consequence, all the signals in the closed-loop system are uniformly ultimately bounded and the tracking errors bounded by prescribed functions converge to a small neighbourhood of zero. The proposed method is applied to the autonomous underwater vehicles (AUVs) with extensive simulation results demonstrating the effectiveness of the proposed method.

本文在考虑模型不确定性和外部扰动的情况下，研究了严格反馈形式的非线性非对称输入饱和系统的自适应规定性能跟踪控制。径向基函数神经网络 (RBF-NN) 用于处理模型的不确定性。通过规定的性能函数，可以保证系统的瞬态性能。连续高斯误差函数表示为非对称饱和非线性的近似值，因此可以在控制设计中利用反推技术。基于李雅普诺夫综合，残差函数逼近误差和外部干扰通过构建的自适应控制律进行补偿。作为结果，闭环系统中的所有信号最终一致有界，并且由指定函数界定的跟踪误差收敛到零的小邻域。所提出的方法应用于自主水下航行器（AUV），大量的仿真结果证明了所提出方法的有效性。