A practical fixed-time adaptive fuzzy control strategy is investigated for uncertain nonlinear systems with time-varying asymmetric constraints and input quantization. To overcome the difficulties of designing controllers under the state constraints, a unified barrier function approach is employed to construct a coordinate transformation that maps the original constrained system to an equivalent unconstrained one, thus relaxing the time-varying asymmetric constraints upon system states and avoiding the feasibility check condition typically required in the traditional barrier Lyapunov function based control approach. Meanwhile, the “explosion of complexity” problem in the traditional backstepping approach arising from repeatedly derivatives of virtual controllers is solved by using the command filter method. It is verified via the fixed-time Lyapunov stability criterion that the system output can track a desired signal within a small error range in a predetermined time, and that all system states remain in the constraint range. Finally, two simulation examples are offered to demonstrate the effectiveness of the proposed strategy.

In this paper, for a class of uncertain nonlinear systems, a low-computation design scheme for fault detection and performance recovery based on deferred replacement actuators is proposed. Different from the existing two mainstream adaptive fault-tolerant control schemes, the proposed method does not require prior knowledge of fault models, nor does it require multiple actuators working in parallel simultaneously to mitigate the impact of faults. The disadvantage of the former is that most of the considered fault models cannot cover all fault types, while the latter adds unnecessary wear and tear to actuators that do not generate faults. In order to overcome the shortcomings of the existing fault-tolerant control, a method with deferred actuator replacement is proposed. By designing a fault detection function and a shifting function, new error variables are reconstructed to achieve performance recovery. In addition, a computationally efficient design scheme is established through the idea of constraint control. Unlike the existing advanced technology, firstly, dealing with the problem of complexity explosion from a new perspective and ensuring the rationality of assumptions; secondly, analyze the controllability of system states during the deferred replacement stage. Finally, simulation results on a twin otter aircraft system verify the effectiveness of the proposed scheme.Note to Practitioners—For widely used modern control systems such as aircraft systems, self-driving systems, etc., it is extremely difficult to diagnose and repair the sudden fault behavior in a short time, and in this process, the adverse impact caused by the serious decline of the system performance is huge. Inspired by the practical application of airbus states in the flight control system, a fault-tolerant control scheme of deferred replacement actuators is designed in this paper. In addition, by designing a low-computation control scheme, the assumptions that are difficult to achieve in practice are relaxed, and the structure of the controller is very simple without the help of any auxiliary design. After fault detection, the replacement actuators strategy successfully realizes the recovery of the system performance within the specified time.

Detecting and recognizing objects in images is one of the most challenging tasks in computer vision, as it seeks to detect subtle objects while ignoring massive numbers of negatives. While deep networks have led to advances in many problems, new representations and approaches are needed for applications without millions of training samples or where explanations are required. This paper focuses on a new representation that can be used for detection/recognition in many applications of computer vision, and demonstrates it on two very different applications: pedestrian detection and ear recognition. This paper proposes the use of Chainlets, ordered oriented data computed from deep contourbased edge detection, as a novel object descriptor. Chainlets address the problem with Histograms of Oriented Gradients, in that HOG does not model edge connectedness. We extend HOG using Histograms of Chain Codes, which improve object descriptiveness and can even provide orientation invariance. These descriptors significantly outperform existing feature sets, including both existing hand-crafted and deep features for human ear recognition, and are near state of the art on pedestrian detection. Results from our Chainlets algorithm underwent independent testing as part of the new Unconstrained Ear Recognition Challenge dataset, where the competition's evaluation showed Chainlets yielded a significant improvement over other state of the art approaches. To show further generality, we performed an evaluation on the INRIA person detection dataset with results that are near state-of-the-art deep network and boosted classifier results. Overall, the experimental results show that the novel Chainlets representation is competitive with, or better than, state-of-the-art algorithms on both pedestrian detection and ear recognition applications.

In this paper we present the results of the Unconstrained Ear Recognition Challenge (UERC), a group benchmarking effort centered around the problem of person recognition from ear images captured in uncontrolled conditions. The goal of the challenge was to assess the performance of existing ear recognition techniques on a challenging large-scale dataset and identify open problems that need to be addressed in the future. Five groups from three continents participated in the challenge and contributed six ear recognition techniques for the evaluation, while multiple baselines were made available for the challenge by the UERC organizers. A comprehensive analysis was conducted with all participating approaches addressing essential research questions pertaining to the sensitivity of the technology to head rotation, flipping, gallery size, large-scale recognition and others. The top performer of the UERC was found to ensure robust performance on a smaller part of the dataset (with 180 subjects) regardless of image characteristics, but still exhibited a significant performance drop when the entire dataset comprising 3,704 subjects was used for testing.