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This article presents the analysis and optimization method of equivalent impedances specifically for multichannel wireless power transfer (WPT) systems. By establishing the equivalent impedance model and the calculation scheme about output power and system efficiency for multichannel WPT system, the proposed analysis and optimization method is capable to determine the proper range of the load resistances in each channel thereby achieving an ideal transmission performance for multichannel WPT systems. Besides, the optimal selection of the load resistances is capable of offering the great power transmission passageway without cross interference between different channels. Moreover, the proper value of the load resistances can achieve certain power output while maintaining the system efficiency at a relatively high level. Both the simulation and experimental results are provided to demonstrate the feasibility and the performance of the proposed scheme.

During the movement of the mobile robot, except the common scenario, sometimes, the robot has to face the unexpected uncertainty in the environment. Traditional methods to address this problem generally use the task-specific method from the engineering perspective, lacking flexibility in the changing environment and being difficult to respond to the environmental challenges. Because of the function of the brain’s neuromodulatory system, human beings have ability to respond to the ever-changing environment quickly. To simulate the working mechanism of the human brain in responding to the unexpected uncertainty in the environment, this article presents a motivated developmental network (MDN) to offer a control configuration for an artificial agent to face the unexpected uncertainty in the environment, through introducing the acetylcholine/norepinephrine (ACh/NE) systems to the MDN. Taking the regulation role of ACh/NE in serotonin and dopamine (DA) into account, a novel learning rate for the hidden layer neurons of the MDN is proposed. Moreover, a novel composite mechanism is presented to decide the moving direction of the agent. Under the modulation of DA, serotonin, ACh, and NE, the agent can perform specific functions effectively, e.g., chase a target and elude the obstacle, especially the sudden obstacle. Goal-directed pursuing behavior in three simulation cases illustrates the effect of the presented neural modulatory systems, for instance, dealing with the unexpected uncertainty, realizing the attentional effort, reinforcement learning, etc. To the best of our knowledge, this article is the first endeavor to address the unexpected uncertainty with the MDN.

Advanced ceramic sponge materials with temperature-invariant high compressibility are urgently needed as thermal insulators, energy absorbers, catalyst carriers, and high temperature air filters. However, the application of ceramic sponge materials is severely limited due to their complex preparation process. Here, we present a facile method for large-scale fabrication of highly compressible, temperature resistant SiO2-Al2O3 composite ceramic sponges by blow spinning and subsequent calcination. We successfully produce anisotropic lamellar ceramic sponges with numerous stacked microfiber layers and density as low as 10 mg cm−3. The anisotropic lamellar ceramic sponges exhibit high compression fatigue resistance, strain-independent zero Poisson’s ratio, robust fire resistance, temperature-invariant compression resilience from −196 to 1000 °C, and excellent thermal insulation with a thermal conductivity as low as 0.034 W m−1 K−1. In addition, the lamellar structure also endows the ceramic sponges with excellent sound absorption properties, representing a promising alternative to existing thermal insulation and acoustic absorption materials., Temperature-invariant highly compressible ceramic sponges are attractive for thermal insulators and energy absorbers, but development is limited by complex preparation processes. Here the authors report large-scale fabrication of silica-alumina composite ceramic sponges via blow spinning and calcination.

Autonomous navigation in dynamic environment is aprerequisite of the mobile robot to perform tasks, and numerous approaches have been presented, including the supervised learning. Using supervised learning in robot navigation might meet problems, such as inconsistent and noisy data, and high error in training data. Inspired by the advantages of the reinforcement learning, such as no need for desired outputs, many researchers have applied reinforcement learning to robot navigation. This paper presents anovel method to address the robot navigation in different settings, through integrating supervised learning and analogical reinforcement learning into amotivated developmental network. We focus on the effect of the new learning rate on the robot navigation behavior. Experimentally, we show that the effect of internal neurons on the learning rate allows the agent to approach the target and avoid the obstacle as compounding effects of sequential states in static, dynamic, and complex environments. Further, we compare the performance between the emergent developmental network system and asymbolic system, as well as other four reinforcement learning algorithms. These experiments indicate that the reinforcement learning is beneficial for developing desirable behaviors in this set of robot navigation- staying statistically close to its target and away from obstacle.

This paper investigates finite-time adaptive neural tracking control for a class of nonlinear time-delay systems subject to the actuator delay and full-state constraints. The difficulty is to consi...

Spiral wave is closely related to the occurrence of malignant ventricular arrhythmia. It is important and necessary to study the spiral wave dynamics to better analyze and control spiral waves. In this paper, the dynamics of FitzHugh-Nagumo(FHN) model is identified by using a novel method based on deterministic learning and interpolation method. The FHN model, which has been studied extensively in physical and mathematical science, is often used to study spiral waves. It is a distributed parameter (DPS) described by two coupled partial differential equations (PDEs). To identify the underlying system dynamics of the FHN model globally, we first transform the FHN model into a set of ordinary differential equations (ODEs) by applying the method of lines. Then, we identify the dynamics of the approximation system by employing deterministic learning. That is, the FHN dynamics on a set of spatial grid nodes is accurately identified. To achieve the global identification of the FHN model, the underlying system dynamics of the FHN model on any other spatial point is approximated via an algorithm based on the interpolation method. The effectiveness and feasibility of the proposed method are demonstrated theoretically and numerically.

In the paper, we propose a novel approach to identify distributed parameter system (DPS) with recurrent state trajectory. Different from existing literature, the system dynamics rather than parameters or structure of DPS is identified in the study. Due to the infinite-dimensional feature of DPS, the partial differential equation describing the DPS is first approximated by a set of ordinary differential equations. By employing finite difference method, the spatial derivatives at a set of spatial points are approximated. Then, the DPS dynamics at the set of spatial points is identified via deterministic learning. With the identification results, a mechanism based on interpolation method is proposed to approximate the DPS dynamics at any other spatial point. That is, we can accurately identify the DPS dynamics at any spatial point. Numerical results involving the identification of an important mathematical physics equation are presented to illustrate the validity of the approach.

The problem of finite-time decentralized neural adaptive constrained control is studied for large-scale nonlinear time-delay systems in the non-affine form. The main features of the considered system are that 1) unknown unmatched time-delay interactions are considered, 2) the couplings among the nested subsystems are involved in uncertain nonlinear systems, 3) based on finite-time stability approach, asymmetric saturation actuators and output constraints are studied in large-scale systems. First, the smooth asymmetric saturation nonlinearity and barrier Lyapunov functions are used to achieve the input and output constraints. Second, the appropriately designed Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions, and the neural networks are employed to approximate the unknown nonlinearities. Note that, due to unknown time-delay interactions and the couplings among subsystems, the controller design is more meaningful and challenging. At last, based on finite-time stability theory and Lyapunov stability theory, a decentralized adaptive controller is proposed, which decreases the number of learning parameters. It is shown that the designed controller can ensure that all closed-loop signals are bounded and the tracking error converges to a small neighborhood of the origin. The simulation studies are presented to show the effectiveness of the proposed method.

In this paper, the problem concerned with adaptive approximation-based control is discussed for a class of stochastic pure-feedback nonlinear time-delay systems with unknown direction control gains and full-state constraints. In the controller design process, the approximation capability of neural networks is utilized to identify the unknown nonlinearities, the appropriate Lyapunov–Krasovskii functionals are constructed to compensate the unknown time-delay terms, barrier Lyapunov functions (BLFs) are designed to ensure that the state variables are constrained, and the Nussbaum-type gain function is used to solve the difficulties caused by the unknown virtual control gains. Then, based on adaptive backstepping technique and Lyapunov stability theory, a robust control scheme is presented, and the developed controller decreases the number of learning parameters and thus reduces the computational burden. It is shown that the proposed controller can guarantee that all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a compact set of the origin. Finally, two simulation examples are included to validate the effectiveness of the proposed approach.

Ceramic ultra-thin film materials have attracted much attention because of their unique performance compared to the bulk materials and their vital applications in electronic devices and catalysts. An easy, effective and facile method to fabricate high quality ceramic nano membranes will significantly stimulate their practical applications. Herein, we report a new “Blow-bubble” process to produce large area freestanding ultra-thin ceramic material. We successfully used a blow-bubble method to obtain large area freestanding ultra-thin ZnO film after thermal process, and this method can also be used to other materials like TiO2. This synthesizing process relies on sol-gel method and surface tension. In summary, this work demonstrates a facile, economic and effective method to fabricate large area freestanding ultra-thin ZnO film. The photoconductivity of the ZnO film was also tested.

This paper investigates the problem of decentralized adaptive backstepping control for a class of large-scale stochastic nonlinear time-delay systems with asymmetric saturation actuators and output constraints. Firstly, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation nonlinearity, and barrier Lyapunov functions are designed to ensure that the output parameters are restricted. Secondly, the appropriate Lyapunov–Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions, and the neural networks are employed to approximate the unknown nonlinearities. At last, based on Lyapunov stability theory, a decentralized adaptive neural control method is proposed, and the designed controller decreases the number of learning parameters. It is shown that the designed controller can ensure that all the closed-loop signals are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. Two examples are provided to show the effectiveness of the proposed method.

This paper investigates an adaptive neural tracking control problem for a class of multi-input multi-output (MIMO) stochastic nonlinear pure-feedback systems with input saturation and full-state constraints. First, the implicit function and mean value theorems are used to overcome the difficulty in the control of pure-feedback systems. Second, the Gaussian error function is employed to represent a continuous differentiable asymmetric saturation model, the neural network is employed to approximate the unknown nonlinearity, and a barrier Lyapunov function is designed to ensure that the full-state variables are restricted. At last, based on Lyapunov stability theory, a robust adaptive neural control method is obtained, which decreases the number of learning parameters and thus reduces the computational burden. It is shown that the designed controller can guarantee that all the signals in the closed-loop system are 4-Moment (or 2 Moment) semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of the origin. The simulation example is given to further illustrate the effectiveness of the proposed approach.

Aiming at the aerodynamic optimization problem of guided ammunition airfoil, genetic algorithm is used to optimize the lift-drag ratio of the airfoil based CST geometric description method with given inflow velocity and fixed angle of attack. Firstly, the parameter space generated by CST parameterized airfoil description method is performed with binary code and the basic operations of genetic algorithm such as selection, crossover and mutation. Then, the external CFD solver is called to automatically divide the grid and calculate the lift-drag ratio through the control script. Finally, the target solution is obtained by searching. The calculation results show that this method can obtain a optimal target solution in relative less generation for a group of airfoils generated randomly in a certain search space.

This paper proposes an adaptive neural dynamic surface control scheme for a class of strict-feedback stochastic nonlinear systems with guaranteed predefined performance under arbitrary switchings. First, by utilizing the prescribed performance control, the prescribed tracking control performance can be ensured with unknown initial errors, and input constraints are achieved by employing a continuous differentiable asymmetric saturation model. Second, RBF neural networks are used to handle unknown nonlinear functions and stochastic disturbances, and the dynamic surface control technique is used to avoid the problem of ‘explosion of complexity’ in control design. At last, by combining the common Lyapunov function method with the backstepping design principle, a common adaptive neural controller is constructed. The designed controller overcomes the problem of the over-parameterization and further alleviates the computational burden. Under the proposed common adaptive controller, all the signals in the closed-loop system are four-moment (or two-moment) semi-globally uniformly ultimately bounded, and the prescribed transient and steady tracking control performance are guaranteed under arbitrary switchings. The arbitrary switching behaviors among two and three subsystems are performed to demonstrate and verify the effectiveness of the proposed method.