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.

This paper considers the finite-time dynamic tracking control for parallel robots with uncertainties and input saturation via a finite-time nonsingular terminal sliding mode control scheme. A disturbance observer is designed to estimate the lumped disturbance in the dynamic model of the parallel robot, including modeling errors, friction and external disturbance. By introducing the fractional exponential powers into the existing asymptotic convergent auxiliary system, a novel finite-time convergent auxiliary system is constructed to compensate for input saturation. The finite-time nonsingular terminal sliding mode control is proposed based on the disturbance estimation and the state of the novel auxiliary system, so that the convergence performance, control accuracy and robustness are improved. Due to the estimation and compensation for the lumped disturbance, the inherent chattering characteristic of sliding mode control can be alleviated by reducing the control gain. The finite-time stability of the closed-loop system is proved with Lyapunov theory. Finally, simulation and experimental research on the dynamic control of a conveying parallel robot are carried out to verify the effectiveness of the proposed method.

Some challenging issues exist in trajectory tracking control of an uncertain bilaterally symmetrical hybrid robot (UBSHR) with variational desired trajectories, mainly the uncertainty problem of UBSHR, the synchronization problem of UBSHR’s active joints and bilateral symmetrical hybrid mechanisms, and the flexible control problem of active adaption to different technological requirements without artificially adjusting the control parameters or switching the hardware system. To solve these problems, an adaptive fuzzy neural network in conjunction with subtractive clustering algorithm (SC-AFNN) for UBSHR is proposed. More specifically, a novel composite error is incorporated into the second-order sliding mode control method to generate ideal training data samples and to improve the uncertain system robustness and synchronization performance simultaneously. Furthermore, the SC-AFNN is introduced to realize self-learning and self-adjusting of control rules and control parameters and to enhance the flexible control performance of UBSHR with variational desired trajectories. Strict theoretical proof of the defined errors’ relationship and the stability of the poposed control method is given. Ultimately, simulations and experiments for the prototype system of an UBSHR are conducted to verify the effectiveness of the proposed control method.