Adaptive prescribed performance control of output constrained hybrid-powered barrel elevator with dual-model uncertainty compensation

This study concerns the position tracking control of the hybrid-powered barrel elevator (HPBE) with dual-channel model uncertainty and output constraints. To realize an outstanding control performance, a dual-extended-state-observer-based command filtered adaptive prescribed performance control (DESO-CFAPPC) strategy is presented based on the dynamic model considering various nonlinearities and model uncertainties. The conjunction of the PPC and barrier Lyapunov function in the DESO-CFAPPC not only prevents violation of output constraints of the barrel, but also prevents the complex calculation caused by the logarithmic ETF in the traditional PPC. The adaptive laws are constructed to estimate uncertain parameters. The DESO further estimates the unknown velocity, mismatched and matched model uncertainties. The obtained estimates are incorporated into the control law to enhance the tracking performance. The stability and convergence of the DESO-CFAPPC are theoretically proved and comparative experimental results indicate the effectiveness of the proposed strategy.