Disturbance observer-based adaptive fixed-time neural control for uncertain unmanned helicopter with system uncertainties and full-state constraints

Abstract

Purpose This paper aims to design a fixed-time disturbance observer (FTDO)-based adaptive neural fixed-time control strategy for the uncertain medium-scale unmanned autonomous helicopter (UAH) with full-state constraints and external disturbances. Design/methodology/approach First, the tan-type barrier Lyapunov function (BLF) and neural network (NN) are constructed to deal with the full-state constraints and system uncertainties, respectively. Subsequently, the compound disturbances are estimated by FTDOs. Simultaneously, the FTDOs and fixed-time control strategy guarantees that the errors of disturbances and states converge to the desired region in fixed-time, and the upper bound on the convergence time of the FTDOs and the designed controller can be estimated through the devised parameters. In addition, Lyapunov stability theory proves that all states of the UAH system are semiglobally uniform ultimately bounded. Findings The designed controller combines the fixed-time stability theory with tan-type BLF, which proves that all states of the UAH can be constrained in a predefined region. Furthermore, the state and the disturbance errors converge to a desired region in fixed time, which improves the performance of the controller. Practical implications The designed controller keeps the UAH within the safe range, reduces the accident rate and improves the operation efficiency. Originality/value There are few research studies on the constraints and convergence time of UAH. In this paper, based on the FTDO, an adaptive NN control strategy with tan-type BLF is proposed to ensure that the state errors can be constrained to a safety region and deal with the system uncertainties and the influence of external disturbances.