Wei He

Bio: Wei He is an academic researcher from University of Science and Technology Beijing. The author has contributed to research in topics: Medicine & Lyapunov function. The author has an hindex of 68, co-authored 399 publications receiving 16294 citations. Previous affiliations of Wei He include National University of Singapore & University of Electronic Science and Technology of China.

Adaptive Neural Network Control of an Uncertain Robot With Full-State Constraints

Abstract: This paper studies the tracking control problem for an uncertain ${n}$ -link robot with full-state constraints The rigid robotic manipulator is described as a multiinput and multioutput system Adaptive neural network (NN) control for the robotic system with full-state constraints is designed In the control design, the adaptive NNs are adopted to handle system uncertainties and disturbances The Moore–Penrose inverse term is employed in order to prevent the violation of the full-state constraints A barrier Lyapunov function is used to guarantee the uniform ultimate boundedness of the closed-loop system The control performance of the closed-loop system is guaranteed by appropriately choosing the design parameters Simulation studies are performed to illustrate the effectiveness of the proposed control

Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation

Abstract: In this paper, adaptive impedance control is developed for an ${n}$ -link robotic manipulator with input saturation by employing neural networks. Both uncertainties and input saturation are considered in the tracking control design. In order to approximate the system uncertainties, we introduce a radial basis function neural network controller, and the input saturation is handled by designing an auxiliary system. By using Lyapunov’s method, we design adaptive neural impedance controllers. Both state and output feedbacks are constructed. To verify the proposed control, extensive simulations are conducted.

Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning

Abstract: This paper investigates adaptive fuzzy neural network (NN) control using impedance learning for a constrained robot, subject to unknown system dynamics, the effect of state constraints, and the uncertain compliant environment with which the robot comes into contact. A fuzzy NN learning algorithm is developed to identify the uncertain plant model. The prominent feature of the fuzzy NN is that there is no need to get the prior knowledge about the uncertainty and a sufficient amount of observed data. Also, impedance learning is introduced to tackle the interaction between the robot and its environment, so that the robot follows a desired destination generated by impedance learning. A barrier Lyapunov function is used to address the effect of state constraints. With the proposed control, the stability of the closed-loop system is achieved via Lyapunov’s stability theory, and the tracking performance is guaranteed under the condition of state constraints and uncertainty. Some simulation studies are carried out to illustrate the effectiveness of the proposed scheme.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Electrospinning: A Fascinating Method for the Preparation of Ultrathin Fibers

Abstract: Electrospinning is a highly versatile method to process solutions or melts, mainly of polymers, into continuous fibers with diameters ranging from a few micrometers to a few nanometers. This technique is applicable to virtually every soluble or fusible polymer. The polymers can be chemically modified and can also be tailored with additives ranging from simple carbon-black particles to complex species such as enzymes, viruses, and bacteria. Electrospinning appears to be straightforward, but is a rather intricate process that depends on a multitude of molecular, process, and technical parameters. The method provides access to entirely new materials, which may have complex chemical structures. Electrospinning is not only a focus of intense academic investigation; the technique is already being applied in many technological areas.