Observer-based adaptive sliding mode fault-tolerant control for the underactuated space robot with joint actuator gain faults

summary:An adaptive sliding mode fault-tolerant controller based on fault observer is proposed for the space robots with joint actuator gain faults. Firstly, the dynamic model of the underactuated space robot is deduced combining conservation law of linear momentum with Lagrange method. Then, the dynamic model of the manipulator joints is obtained by using the mathematical operation of the block matrices, hence the measurement of the angular acceleration of the base attitude can be omitted. Subsequently, a fault observer which can accurately estimate the gain faults is designed, and the estimated results are fed back to the adaptive sliding mode fault-tolerant controller. It is proved that the proposed control algorithm can guarantee the global asymptotic stability of the closed-loop system through the Lyapunov theorem. The simulation results authenticate the effectiveness and feasibility of the control strategy and observation scheme

Nutrient inversion and hyperspectral feature extraction of sea rice at diff erent growth stages

Nitrogen is a large amount of essential elements for the growth and development of sea rice. Monitoring the nitrogen nutrition status of sea rice timely and accurately, and rational fertilization of sea rice is of great signifi cance for increasing yield, optimizing quality and reducing water pollution. The remote sensing diagnosis technology of sea rice nutrition has the characteristics of simple, non_x005fdestructive and rapid, and has been widely studied and applied by experts in various countries. In this experiment, the sea red rice varieties were taken as an example. Through field experiment, the leaves of sea rice in four growth stages were collected by using chlorophyll analyzer and near infrared spectrometer, and the chlorophyll value and spectral refl ectance of sea rice leaves were determined. The results showed that the spectral refl ectance of sea rice leaves in diff erent growth stages had obvious changes. The sensitive band of sea rice leaves was further found by combining the spectral curve, which laid the foundation for the future nitrogen nutrition diagnosis of sea rice

A Normalized Fuzzy Neural Network and its Application

A normal fuzzy neural network(NFNN) with five layers is proposed. Focusing on the structure optimization of network, a new node selection method and corresponding back propagation learning algorithm rules are presented In the case with fewer input nodes, the training is more fast in this kind of neural network. Water-flooded zone identification in measure-well explanation is an important problem in the oil field development; especially in its later period. Complex geology conditions lead to many fuzzy characters in measure-well curves. In the combination of all kinds of fuzzy conditions, oil water-flooded behaves as strong water-flooded, middle water-flooded, weak water-flooded and no water-flooded, etc. NFNN is applied to water-flooded identification in oil well measure-well to find its mapping relation between well measure-well and water-flooded level,accordingly realize the water-flooded zone identification in measure-well explanation of fuzzy oil. Test results illustrate its practicabilit

Stochastic spin-orbit-torque device as the STDP synapse for spiking neural networks

Neuromorphic hardware as a non-Von Neumann architecture has better energy efficiency and parallelism than the conventional computer. Here, with numerical modeling spin-orbit torque (SOT) device using current-induced SOT and Joule heating effects, we acquire its magnetization switching probability as a function of the input current pulses and use it to mimic the spike-timing-dependent plasticity learning behavior like actual brain working. We further demonstrate that the artificial spiking neural network (SNN) built by this SOT device can perform unsupervised handwritten digit recognition with the accuracy of 80% and logic operation learning. Our work provides a new clue to achieving SNN-based neuromorphic hardware using high-energy efficiency and nonvolatile spintronics nanodevicesComment: 8 pages, 5 figure