Decentralized adaptive practical prescribed-time control via command filters

Abstract

This paper proposes a command filter-based decentralized adaptive backstepping practical prescribed-time (PPT) tracking control scheme for a class of non-strict feedback interconnected systems with time varying parameters, unknown control coefficients, unmodeled dynamics, input deadzone and saturation. By the aid of the characteristics of Gaussian functions, the obstacles arising from the non-strict feedback terms are successfully solved. By constructing a novel time-varying scaling function and utilizing nonlinear mapping, the PPT tracking control is developed. The estimations of dynamical uncertainties resulting from unmodeled dynamics are accomplished by employing auxiliary signals, while the unknown continuous terms are characterized by the aid of radial basis function neural networks (RBFNNs). A superposition of two hyperbolic tangent functions is utilized to approximate input nonlinearity. Utilizing the compact set defined in the command filtered backstepping technique, the problem of unknown control direction is solved without using the Nussbaum gain technique. All the signals involved are proved to be semi-global uniform ultimate bounded, and the tracking error can enter the pre-specified convergence region within a pre-specified time. Simulation results are used to demonstrate the effectiveness of the proposed control approach.