Adaptive Control and Application for Nonlinear Systems With Input Nonlinearities and Unknown Virtual Control Coefficients.

This article is devoted to an adaptive tracking control problem for nonlinear systems with input deadzone and saturation, whose virtual control coefficients include the known and unknown terms. A novel smooth function is first introduced to approximate the input nonlinearities. By utilizing an auxiliary variable and the Nussbaum gain technique, an improved real control signal is constructed to handle the uncertainties of the virtual control coefficients and input nonlinearities. Furthermore, an adaptive tracking controller is constructed and applied to the attitude control of a quadrotor, which guarantees the boundedness of all the signals in the resulting closed-loop system. Finally, both stability analysis and simulation results validate the effectiveness of the developed control strategy.

Event-triggered adaptive tracking control for uncertain nonlinear systems based on a new funnel function.

This paper focuses on the problem of event-triggered funnel control for strict-feedback nonlinear systems with unknown parameters. For the first time, an adjustable funnel function is proposed, whose parameters can be adjusted online according to the change of tracking error. Furthermore, based on event-triggered control, an adaptive event-triggered funnel controller is constructed, which guarantees that all the signals in the closed-loop system are bounded. Besides, the output tracking error is further optimized and always falls within an adjustable funnel which has a faster convergence. Meanwhile, the Zeno behavior also is avoided. Simulation results demonstrate the effectiveness of the developed controller.

Adaptive Absolute Ego-Motion Estimation Using Wearable Visual-Inertial Sensors for Indoor Positioning.

This paper proposes an adaptive absolute ego-motion estimation method using wearable visual-inertial sensors for indoor positioning. We introduce a wearable visual-inertial device to estimate not only the camera ego-motion, but also the 3D motion of the moving object in dynamic environments. Firstly, a novel method dynamic scene segmentation is proposed using two visual geometry constraints with the help of inertial sensors. Moreover, this paper introduces a concept of "virtual camera" to consider the motion area related to each moving object as if a static object were viewed by a "virtual camera". We therefore derive the 3D moving object's motion from the motions for the real and virtual camera because the virtual camera's motion is actually the combined motion of both the real camera and the moving object. In addition, a multi-rate linear Kalman-filter (MR-LKF) as our previous work was selected to solve both the problem of scale ambiguity in monocular camera tracking and the different sampling frequencies of visual and inertial sensors. The performance of the proposed method is evaluated by simulation studies and practical experiments performed in both static and dynamic environments. The results show the method's robustness and effectiveness compared with the results from a Pioneer robot as the ground truth.