This article addresses an adaptive neural network (NN) constraint control scheme for a class of fractional-order uncertain nonlinear nonstrict-feedback systems with full-state constraints and input saturation. The radial basis function (RBF) NNs are used to deal with the algebraic loop problem from the nonstrict-feedback formation based on the approximation structure. In order to overcome the problem of input saturation nonlinearity, a smooth nonaffine function is applied to approach the saturation function. To arrest the violation of full-state constraints, the barrier Lyapunov function (BLF) is introduced in each step of the backstepping procedure. By using the fractional-order Lyapunov stability theory and the given conditions, it proves that all the states remain in their constraint bounds, the tracking error converges to a bounded compact set containing the origin, and all signals in the closed-loop system are ensured to be bounded. Finally, the effectiveness of the proposed control scheme is verified by two simulation examples.

This article investigates the event-triggered prescribed performance adaptive fuzzy fault-tolerant control tracking strategy for quadrotor unmanned aerial vehicle (UAV) with actuator failures and saturation. In view of the uncertainty of quadrotor UAV system parameters due to the installation errors, propeller errors and so on, the fuzzy logic systems are adopted to approximate the unknown uncertainties of quadrotor UAV system. To ensure that the tracking error achieves the preset transient performance, the error transformation method and barrier Lyapunov functions based on performance functions are adopted. Nussbaum gain technology is presented to solve the effects of actuator faults, saturation and the event-triggered mechanism, in which the event-triggered scheme is proposed to solve the issue of scarce computing and communication resources in quadrotor UAV systems. Finally, the superiority tracking performance of quadrotor UAV by using the proposed control algorithm is verified by simulation results.

As a useful classification method, generalized eigenvalue proximal support vector machine (GEPSVM) is recently studied extensively. However, it may suffer from the sensitivity to outliers, since the L2-norm is used as a measure distance. In this paper, based on the robustness of the L1-norm, we propose an improved robust L1-norm nonparallel proximal SVM with an arbitrary Lp-norm regularization (LpNPSVM), where p > 0. Compared with GEPSVM, the LpNPSVM is more robust to outliers and noise. A simple but effective iterative technique is introduced to solve the LpNPSVM, and its convergence guarantee is also given when 0 <; p ≤ 2. Experimental results on different types of contaminated data sets show the effectiveness of LpNPSVM. At last, we investigate our LpNPSVM on a real spare parts inspection problem. Computational results demonstrate the effectiveness of the proposed method over the GEPSVM on all the noise data.