This study proposes a robust second-order terminal sliding mode control with perturbation estimation (2OTSMCPE) strategy with application to trajectory tracking control of the flexure-based nanopositioning system. The proposed controller advantages not only lie on its finite-time convergence but also can provide a high tracking precision with a chattering alleviation which is attend by employing a second-order sliding surface with the switching function. The model of the piezo-driven nanopositioning system is presented first. Second, the sliding variable is designed such as proportional–integral–derivative form to enhance the dynamic response of the control system. Then, a non-singular terminal sliding function (NTSM) is used to achieve the finite-time convergence of the linear sliding variable. Next, a perturbation estimation technique is integrated with the control structure for online estimation of the system uncertainties, thus the prior knowledge of the bounds of system uncertainties are not needed in the proposed control design. Afterwards, the theoretical analysis of the 2OTSMCPE with stability proof is investigated herein. Finally, the system performance with the proposed controller is experimentally verified. The results reveal that the 2OTSMCPE has stronger robustness and also has smoother control signals in comparison with both conventional sliding mode control and the NTSM controller.

In this study, the authors propose a novel state and a fault estimation scheme for a class of hyperbolic spatiotemporal dynamic systems in the presence of unknown external disturbance. They consider the occurrence of multiplicative actuator and sensor faults. In detail, they consider two cases of fault occurrence: (i) only one type (actuator or sensor) of fault happens; (ii) two types of faults occur simultaneously. This study discusses the fault detectability conditions by proposing a fault detection observer. To complete the estimation problem, three difficulties arise: (i) no prior information shows the type of faults; (ii) the observer design is non-linear due to multiplication between plant signals (state or input) and unknown fault parameters; (iii) only one boundary measurement is available. They convert the original faulty plant into its observer canonical form. By proposing two filters based on the resulting observer canonical form, they develop novel parameter update laws for fault parameter estimation. With the proposed update laws, the true state of the faulty plant can be estimated by the proposed observers. By selecting appropriate Lyapunov functions, they prove that estimation error of state and fault parameters exponentially decays to an arbitrarily small neighbourhood of zero despite unknown external disturbance.

This study addresses the pose estimation problem of an aircraft runway using visual observations in a landing approach scenario. The authors utilised the fact that the geodetic coordinates of most runways are known precisely with highly visible markers. Thus, the runway observations can increase the level of situational awareness during the landing approach, providing additional redundancy of navigation and less reliance on global positioning system. A novel pose optimisation algorithm is proposed utilising unit dual quaternion for the runway corner observations obtained from a monocular camera. The estimated runway pose is further fused with an inertial navigation system in an extended Kalman filter. An open-source flight simulator is used to collect and process the visual and flight dataset during the landing approach, demonstrating reliable runway pose estimates and the improved inertial navigation solution.

Driverless vision is one of the important applications of robot perception. With the development of driverless vehicles, the perception and understanding of the surrounding environment are becoming more and more important. When the types of surrounding objects are too complex, the ability of the computer to recognise the environment is poor. To improve the recognition accuracy of the computer and enhance the ability of segmentation, in this study, depth estimation is used to predict depth information to assist semantic segmentation, and then edge features of objects are introduced to enhance the contour of objects. A neural network-based semantic segmentation model is proposed. Finally, the intrinsic mechanism of attention is used to increase the correlation between channels. The experimental results on the CamVid data set show that this model can obtain better evaluation results and improve the segmentation accuracy of images compared with other models.

To improve the robustness and discrimination power of the triangle-area representation, a novel shape matching method based on multi-scale angle representation is proposed in this study. By analysing the configurations of different sample points from each shape contour, shape descriptors are constructed by using space angles at different scale levels. With the proposed shape representation, the multi-scale information of shape contours is efficiently described, and the dynamic programming is further used to determine the correspondence between samples from different shapes and calculate the shape distance in the feature matching step. Moreover, to improve the shape retrieval results based on pairwise shape distances, the dynamic label propagation is introduced as the post-processing step. Unlike previous distance learning methods learning the database manifold implicitly, the authors method retrieves relative objects on the shortest paths from near to far explicitly, and the underlying structure can be effectively captured. The proposed method tested on different shape databases provides the performances superior to many other methods, and it can be applied to visual data processing and understanding of the internet of things.