This paper presents a reduced-order extended state observer (ESO) based sliding mode control scheme for friction compensation of a three-wheeled omnidirectional mobile robot. Compared with previous works, the proposed control approach is attractive from an implementation point of view. It does not require any explicit friction model, with quite low computation cost. Firstly, a dynamic model with unknown friction forces is given. Then the controller is designed, consisting of two parts. One part of the control effort is to compensate the friction effects, which are estimated by a reduced-order ESO without using any explicit friction model. The inverse of inertia matrix is also avoided in the proposed reduced-order ESO. The other part of the control effort is designed based on a second order sliding mode technique known as super-twisting algorithm, in presence of parameter uncertainties. In addition, stability analysis of the designed control system is presented. Extensive experiments are conducted to verify the effectiveness and robustness of the proposed control design in compensating different friction effects.

This paper presents an improved backstepping control implementation scheme for a n-dimensional strictfeedback uncertain nonlinear system based on command filtered backstepping and adaptive neural network backstepping. In this approach, n command filters and one neural network are applied to reconstruct the approximations of unknown nonlinearities which are related to the system uncertainties including the system's unmodeled dynamics and external disturbances. Then, one can use the negative feedback of these approximations to compensate the system uncertainties. Moreover, convex optimization and soft computing technique are adopted to design the update law of the weights of the neural network, and Lyapunov stability criterion is used to prove the stability of the closedloop system. Finally, simulation results are given to show the effectiveness of the proposed methods.

Dual-drive gantry stage has been widely applied to various industrial manufacturing fields with its unique structural advantages, and the synchronous control accuracy of the platform is crucial to the performance of the whole motion system. Therefore, an adaptive robust synchronous control scheme based on an improved master-slave structure is proposed, which is not only simple in structure but also easy to implement in engineering. The error dynamics model established in this article makes up for the lag of response of traditional master-slave control and improves the stability of closed-loop system. Online parameter adaptive algorithms deal with parameter uncertainties in the system, while robust control deals with unmodeled dynamics and external disturbances. In addition, nonlinear cogging force compensation is applied to the gantry biaxial system to further improve the control accuracy of tracking and synchronization. Finally, a dual-drive gantry stage system with good tracking and synchronization performance is obtained. The effectiveness and superiority of the proposed control strategy are verified by the comparison of several groups of experiments.

This article’s primary motivation is to propose a nonsingular and continuous terminal sliding-mode (TSM) control with reduced chattering, which is able to rapidly stabilize the second-order plant with high precision. To this end, a novel sliding-mode manifold, coined as practical TSM (PTSM) one, is first constructed. Once the proposed sliding surface is reached, a Lipschitz-continuous but slope-steep generalized velocity will be established at the origin such that the global nonsingularity of the equivalent control is ensured. The fast dynamic response with local high gain characterizes the sliding behavior inside the unit neighborhood of the origin. Importantly, the analytical solution of the proposed sliding-mode reduced-order system is deduced, which indicates that the finite time taken to slide into a preset small neighborhood of the origin can be calculated. The above designability of convergence time is necessary for control practices with accurate time sequence planning. Further, the corresponding globally singular-free PTSM reaching law with reduced chattering is designed based on the super-twisting algorithm. Finally, several groups of linear-motor-based control experiments verify the superiorities of proposed controllers.

This paper presents an adaptive neural network backstepping control design for an experimental helicopter with three rotational degrees-of-freedom. The helicopter system is described by a state-space model with unmodeled dynamics and external disturbances. By resorting to radial basis function neural networks with on-line gradient descent training, such unmodeled dynamics and external disturbances can be compensated in real time. With the help of Lyapunov stability criteria, it is proved that the desired trajectories can be boundedly tracked by the corresponding attitude angles of the helicopter under the proposed adaptive neural network backstepping controller. Compared with robust adaptive integral backstepping technique, our approach has smaller root-mean-squared-error. Finally, four sets of contrastive experiment results are respectively given to show the effectiveness and advantages of the proposed control scheme.