This article focuses on global finite-time output feedback stabilization for uncertain nonlinear systems with unknown measurement sensitivity. The existence of the continuous measurement error resulting from limited accuracy of sensors invalidates the existing design strategies depending on the use of the precise output in the construction of an observer, which highlights the contribution of this article. Essentially, different from related works, we propose a new finite-time convergent observer by avoiding the use of the information on nonlinearities. By combining the homogeneous domination with the addition of a power integrator method, an output feedback controller composed of multiple nested sign functions is successfully developed. Finally, the effectiveness of the presented scheme is exhibited by a numerical example.This article focuses on flexible single-link manipulators (FSLMs) under boundary control and in-domain control. The actuators of the system include the dc motor at the end of the joint and m piezoelectric controllers installed at the flexible link, which is regarded as an Euler-Bernoulli beam. The problem of the infinite number of actuator failures, including the partial loss of the effectiveness and total loss of effectiveness, is solved by the adaptive compensation method. By introducing the relative threshold strategy, the event-triggered control (ETC) scheme is proposed to achieve angle regulation and vibration suppression while reducing the communication burden between the controllers and the actuators. The Lyapunov direct method is utilized to prove that the system is uniformly ultimately bounded and both the angular tracking error and elastic displacement converge to a neighborhood of zero. Numerical simulation results are provided to demonstrate the effectiveness of the proposed control law.In this text, a membership function derivatives (MFDs) extrema-based method is proposed to relax the conservatism both in stability analysis and synthesis problems of Takagi-Sugeno fuzzy systems. By the designed algorithm, the nonpositiveness of the MFDs extrema is conquered. For an open-loop system, based on certain information of the MFs and derivatives, a series of convex stability conditions is derived. Then, an extremum-based construction method is adopted to involve the MF information. For the shape of MFDs, a coordinate transformation algorithm is proposed to involve it in the stability conditions to achieve local stable effects. For a state-feedback control system, conditions guaranteeing the stability and robustness are listed. Finally, simulation examples and comparisons are carried out to clarify the conservatism reduction results of the raised method.This article explores the problem of semisupervised affinity matrix learning, that is, learning an affinity matrix of data samples under the supervision of a small number of pairwise constraints (PCs). By observing that both the matrix encoding PCs, called pairwise constraint matrix (PCM) and the empirically constructed affinity matrix (EAM), express the similarity between samples, we assume that both of them are generated from a latent affinity matrix (LAM) that can depict the ideal pairwise relation between samples. Specifically, the PCM can be thought of as a partial observation of the LAM, while the EAM is a fully observed one but corrupted with noise/outliers. To this end, we innovatively cast the semisupervised affinity matrix learning as the recovery of the LAM guided by the PCM and EAM, which is technically formulated as a convex optimization problem. We also provide an efficient algorithm for solving the resulting model numerically. Extensive experiments on benchmark datasets demonstrate the significant superiority of our method over state-of-the-art ones when used for constrained clustering and dimensionality reduction. The code is publicly available at https//github.com/jyh-learning/LAM.This article provides a solution to tube-based output feedback robust model predictive control (RMPC) for discrete-time linear parameter varying (LPV) systems with bounded disturbances and noises. The proposed approach synthesizes an offline optimization problem to design a look-up table and an online tube-based output feedback RMPC with tightened constraints and scaled terminal constraint sets. In the offline optimization problem, a sequence of nested robust positively invariant (RPI) sets and robust control invariant (RCI) sets, respectively, for estimation errors and control errors is optimized and stored in the look-up table. In the online optimization problem, real-time control parameters are searched based on the bounds of time-varying estimation error sets.  https://www.selleckchem.com/products/ag-221-enasidenib.html  Considering the characteristics of the uncertain scheduling parameter in LPV systems, the online tube-based output feedback RMPC scheme adopts one-step nominal system prediction with scaled terminal constraint sets. The formulated simple and efficient online optimization problem with fewer decision variables and constraints has a lower online computational burden. Recursive feasibility of the optimization problem and robust stability of the controlled LPV system are guaranteed by ensuring that the nominal system converges to the terminal constraint set, and uncertain state trajectories are constrained within robust tubes with the center of the nominal system. A numerical example is given to verify the approach.Adversarial attack can be deemed as a necessary prerequisite evaluation procedure before the deployment of any reinforcement learning (RL) policy. Most existing approaches for generating adversarial attacks are gradient based and are extensive, viz., perturbing every pixel of every frame. In contrast, recent advances show that gradient-free selective perturbations (i.e., attacking only selected pixels and frames) could be a more realistic adversary. However, these attacks treat every frame in isolation, ignoring the relationship between neighboring states of a Markov decision process; thus resulting in high computational complexity that tends to limit their real-world plausibility due to the tight time constraint in RL. Given the above, this article showcases the first study of how transferability across frames could be exploited for boosting the creation of minimal yet powerful attacks in image-based RL. To this end, we introduce three types of frame-correlation transfers (FCTs) (i.e., anterior case transfer, random projection-based transfer, and principal components-based transfer) with varying degrees of computational complexity in generating adversaries via a genetic algorithm.