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.

Different coating technological requirements of automobile electro-coating control system have different desired trajectories. For a dual parallel robot used for automobile electro-coating conveying (DPRAEC), an intelligent tracking smooth sliding mode synchronization control method (ITSSMSC) is proposed to achieve better tracking control performance with high synchronization and flexibility for the system under different desired trajectories. According to the dual parallel and symmetrical structure of the robot, the sliding surface is designed based on a composite error. A deep neural network with multi-hidden layer is trained by deep learning algorithms. Then, any given trajectories caused by different coating technological requirements could be tracked without tuning additional parameters and the synchronous performance could be guaranteed. The convergence of the network training process and the stability of the ITSSMSC are proved theoretically. Compared with the synchronization proportion-derivative control, the sliding mode control based on constant velocity reaching law without synchronization control and the smooth sliding mode synchronization control without training, simulation and experimental results demonstrate the effectiveness of the proposed approach. Compared with synchronization proportion-derivative control, the maximum tracking errors of constant velocity reaching law without synchronization control among each active joint are, respectively, reduced 92.2%, 94.7%, 98.8%. The maximum tracking errors of smooth sliding mode synchronization control and ITSSMSC in [ ] direction are, respectively, 9.06×10⁻⁵m and 1.01×10⁻⁹m. The maximum tracking errors of smooth sliding mode synchronization control and ITSSMSC in [ ] angle are, respectively, 4.50×10⁻³rad and 1.45×10⁻⁴rad. Therefore, the proposed control method could improve the tracking control performance and synchronization performance simultaneously under different desired trajectories.