Elman Neural Network-Based Direct Lift Automatic Carrier Landing Nonsingular Terminal Sliding Mode Fault-Tolerant Control System Design.

The purpose of this paper is to develop the control system using the Elman neural network (ENN) and nonsingular terminal sliding mode control (NTSMC) to improve the automatic landing capability of carrier-based aircraft based on direct lift control (DLC) when subjected to carrier air-wake disturbance and actuator failure. First, the carrier-based aircraft landing model is derived. Then, the NTSMC is proposed to ensure the system's robustness and achieve accurate trajectory tracking performance in a finite time.

Event-Triggered Adaptive Neural Network Control for Stochastic Nonlinear Systems With State Constraints and Time-Varying Delays.

In this article, we pay attention to develop an event-triggered adaptive neural network (ANN) control strategy for stochastic nonlinear systems with state constraints and time-varying delays. The state constraints are disposed by relying on the barrier Lyapunov function. The neural networks are exploited to identify the unknown dynamics.

Event-based adaptive fuzzy control design for nonstrict-feedback nonlinear time-delay systems with state constraints.

This article considers the issue of event-triggered adaptive fuzzy control for state-constrained nonstrict-feedback nonlinear time-delay systems. The adverse effect of time-delay is effectively overcome by choosing the approximate Lyapunov-Krasovskii functional. The fuzzy logic systems are utilized to address unknown dynamics.

Adaptive neural network asymptotic tracking control for nonstrict feedback stochastic nonlinear systems.

The adaptive neural network asymptotic tracking control issue of nonstrict feedback stochastic nonlinear systems is studied in our article by adopting backstepping algorithm. Compared with the existing research, the hypothesis about unknown virtual control coefficients (UVCC) is overcome in the control design. By using the bound estimation scheme and some smooth functions, associating with approximation-based neural network, the asymptotic tracking controller is recursively constructed.

Adaptive fuzzy tracking of switched nonstrict-feedback nonlinear systems with state constraints based on event-triggered mechanism.

This paper is devoted to develop an adaptive fuzzy tracking control scheme for switched nonstrict-feedback nonlinear systems (SNFNS) with state constraints based on event-triggered mechanism. All state variables are ensured to keep the predefined regions by employing barrier Lyapunov function (BLF). The fuzzy logic systems are exploited to cope with the unknown dynamics existing the SNFNS.

Adaptive Fuzzy Finite-Time Control for Nonstrict-Feedback Nonlinear Systems.

This article presents an adaptive fuzzy finite-time control (AFFTC) method for nonstrict-feedback nonlinear systems (NFNSs) with unknown dynamics. With the aid of the backstepping technique, by establishing the smooth switch function (SSF), a novel C AFFTC strategy is recursively constructed, which counteracts the effect of nonstrict-feedback structure and unknown dynamics. Different from the reporting finite-time control achievements, the singularity hindrance derived from the differentiating virtual control law is availably surmounted.

Adaptive Tracking Control for Perturbed Strict-Feedback Nonlinear Systems Based on Optimized Backstepping Technique.

In this article, an adaptive optimized control scheme based on neural networks (NNs) is developed for a class of perturbed strict-feedback nonlinear systems. An optimized backstepping (OB) technique is employed for breaking through the limitation of the matching condition. The disturbance of existing nonlinear systems may degrade system performance or even lead to instability.

Adaptive neural network output feedback control for stochastic nonlinear systems with full state constraints.

This paper presents an adaptive neural network output feedback control method for stochastic nonlinear systems with full state constraints. The barrier Lyapunov functions are used to conquer the effect of state constraints to system performance. The neural network state observer is established to estimate the unmeasured states.

Motion Planning of Autonomous Mobile Robot Using Recurrent Fuzzy Neural Network Trained by Extended Kalman Filter.

This paper proposes a novel motion planning method for an autonomous ground mobile robot to address dynamic surroundings, nonlinear program, and robust optimization problems. A planner based on the recurrent fuzzy neural network (RFNN) is designed to program trajectory and motion of mobile robots to reach target. And, obstacle avoidance is achieved.

Automatic Rectification of the Hybrid Stereo Vision System.

By combining the advantages of 360-degree field of view cameras and the high resolution of conventional cameras, the hybrid stereo vision system could be widely used in surveillance. As the relative position of the two cameras is not constant over time, its automatic rectification is highly desirable when adopting a hybrid stereo vision system for practical use. In this work, we provide a method for rectifying the dynamic hybrid stereo vision system automatically.

Three Landmark Optimization Strategies for Mobile Robot Visual Homing.

Visual homing is an attractive autonomous mobile robot navigation technique, which only uses vision sensors to guide the robot to the specified target location. Landmark is the only input form of the visual homing approaches, which is usually represented by scale-invariant features. However, the landmark distribution has a great impact on the homing performance of the robot, as irregularly distributed landmarks will significantly reduce the navigation precision.

Efficient Force Control Learning System for Industrial Robots Based on Variable Impedance Control.

Learning variable impedance control is a powerful method to improve the performance of force control. However, current methods typically require too many interactions to achieve good performance. Data-inefficiency has limited these methods to learn force-sensitive tasks in real systems.

Course Control of Underactuated Ship Based on Nonlinear Robust Neural Network Backstepping Method.

The problem of course control for underactuated surface ship is addressed in this paper. Firstly, neural networks are adopted to determine the parameters of the unknown part of ideal virtual backstepping control, even the weight values of neural network are updated by adaptive technique. Then uniform stability for the convergence of course tracking errors has been proven through Lyapunov stability theory.

A novel robot visual homing method based on SIFT features.

Warping is an effective visual homing method for robot local navigation. However, the performance of the warping method can be greatly influenced by the changes of the environment in a real scene, thus resulting in lower accuracy. In order to solve the above problem and to get higher homing precision, a novel robot visual homing algorithm is proposed by combining SIFT (scale-invariant feature transform) features with the warping method.

Feature optimization for long-range visual homing in changing environments.

This paper introduces a feature optimization method for robot long-range feature-based visual homing in changing environments. To cope with the changing environmental appearance, the optimization procedure is introduced to distinguish the most relevant features for feature-based visual homing, including the spatial distribution, selection and updating. In the previous research on feature-based visual homing, less effort has been spent on the way to improve the feature distribution to get uniformly distributed features, which are closely related to homing performance.