Robust adaptive asymmetric state constraint control of brush DC motor systems driving a one-link robot manipulator: A feasibility-condition-free method

Abstract

In this paper, we investigate the tracking control problem for a class of brush direct current (DC) motor systems driving a one-link robot manipulator subject to asymmetric full-state constraints. By constructing a state-dependent nonlinear transformation function (NTF), we present an adaptive robust dynamic surface control (DSC) strategy that can directly address both symmetric and asymmetric state constraints, so that there is no need to convert the problem of state constraint into the constraints on tracking errors as necessitated by the Barrier Lyapunov Function (BLF)-based existing works. Furthermore, by employing the first-order filter and constructing a new coordinate transformation, the demanding feasibility condition imposed on the BLF methods is removed, allowing the designer more freedom to select design parameters. Moreover, it is worth mentioning that under the proposed nonlinear transformation function the extra condition on the constraining function is not required. The effectiveness of the proposed control is verified via the Simulation results.