Finite-Time Adaptive Extended State Observer-Based Dynamic Sliding Mode Control for Hybrid Robots

This brief investigates the finite-time adaptive extended state observer (ESO) based dynamic sliding mode control (SMC) for the hybrid robot subject to uncertain problem, e.g., modeling error, joint friction, external disturbance and so on. A barrier function-based adaptive law is defined to adjust the switching gain of SMC system with unknown upper bound of the disturbance. Such an adaptive law-based control method ensures the finite-time convergence of the sliding variable to an adjustable vicinity of zero and realizes chattering reduction of SMC. Then, for estimating and compensating system uncertainties, the time-varying gain ESO (TESO) is designed without the condition of the disturbance change rate tending to zero. A system error-based adaptive law is designed to adjust the observer bandwidth gain, which can reduce the peaking value. Such a TESO reduces the burden of SMC and further weakens the chattering of SMC, so as to improve the control system robustness. The finite-time Lyapunov stability of the sliding variable and the boundness of the TESO error have been proved theoretically. The practical effectiveness is illustrated in simulations and experiments with the prototype of hybrid robot.