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This paper investigates the motion control of a Stewart parallel robot with uncertain load. The external disturbances caused by load can trigger dynamic coupling among six degree-of-freedoms and have a significant impact on control precision. In practice, the dynamic parameters of load is difficult to be precedent identified and the robot with load is a multi-input multi-output(MIMO) system in physical space, which makes the dynamic coupling problem difficult to solve effectively and the high-performance control is hard to achieve. In this paper, the dynamic effect of the load is first analyzed. Then a novel motion control method, modal space neural network compensation control, is designed with the aim of reducing the effect of the load disturbances. This controller implements the neural network to compensate for the load disturbances. In addition, the modal space control theory is introduced to realize the independent control of each control channel. The feasibility of the proposed controller is evaluated in numerical simulations. Results reveal that the proposed control method gives exceptional tracking performance and dynamic coupling suppression capability.

A ceramic membrane with multichannel generally has a higher specific area and a larger throughput compared to that with a single channel, which is beneficial to the industrialization of ceramic mem...

Postcombustion SO2 capture—from a high-volume flue gas—calls for a separator which should possess a high gas/liquid ratio concurrently without causing liquid flooding and entrainment. This study ai...

This approach has been derived mainly to improve quality and efficiency of global path planning for a mobile robot with unknown static obstacle avoidance features in grid-based environment. The quality of the global path in terms of smoothness, path consistency and safety can affect the autonomous behavior of a robot. In this paper, the efficiency of Ant Colony Optimization (ACO) algorithm has improved with additional assistance of A* Multi-Directional algorithm. In the first part, A* Multi-directional algorithm starts to search in map and stores the best nodes area between start and destination with optimal heuristic value and that area of nodes has been chosen for path search by ACO to avoid blind search at initial iterations. The path obtained in grid-based environment consist of points in Cartesian coordinates connected through line segments with sharp bends. Therefore, Markov Decision Process (MDP) trajectory evaluation model is introduced with a novel reward policy to filter and reduce the sharpness in global path generated in grid environment. With arc-length parameterization, a curvilinear smooth route has been generated among filtered waypoints and produces consistency and smoothness in the global path. To achieve a comfort drive and safety for robot, lateral and longitudinal control has been utilized to form a set of optimal trajectories along the reference route, as well as, minimizing total cost. The total cost includes curvature, lateral and longitudinal coordinates constraints. Additionally, for collision detection, at every step the set of optimal local trajectories have been checked for any unexpected obstacle. The results have been verified through simulations in MATLAB compared with previous global path planning algorithms to differentiate the efficiency and quality of derived approach in different constraint environments.

Background: Gait impairments including shuffling gait and hesitation are common in people with Parkinson's disease (PD), and have been linked to increased fall risk and freezing of gait. Nowadays the gait metrics mostly focus on the spatiotemporal characteristics of gait, but less is known of the angular characteristics of the gait, which may provide helpful information pertaining to the functional status and effects of the treatment in PD. Objective: This study aimed to quantify the angles of steps during walking, and explore if this novel step angle metric is associated with the severity of PD and the effects of the treatment including the acute levodopa challenge test (ALCT) and deep brain stimulation (DBS). Methods: A total of 18 participants with PD completed the walking test before and after the ALCT, and 25 participants with PD completed the test with the DBS on and off. The walking test was implemented under two conditions: walking normally at a preferred speed (single task) and walking while performing a cognitive serial subtraction task (dual task). A total of 17 age-matched participants without PD also completed this walking test. The angular velocity was measured using wearable sensors on each ankle, and three gait angular metrics were obtained, that is mean step angle, initial step angle, and last step angle. The conventional gait metrics (ie, step time and step number) were also calculated. Results: The results showed that compared to the control, the following three step angle metrics were significantly smaller in those with PD: mean step angle (F-1,F-48=69.75, P

The synchronous research for fuzzy coupled neural networks (FCNNs) is studied by a new strategy of column pinning controllers. In this paper, the Lyapunov Krasovskii functional (LKF) is taken as an important element for the pinning control laws. The networks are interconnected by coupling gains that define a physical interaction graph. Different from the preset technique in traditional intermittent control, a novel additional communication control graphs of pinning control law are introduced, which has not been investigated before. The proposed control laws can achieve the control objectives of being introduced as an array of vector with Kronecker produce operation. Under the proposed framework of intermittent control, numerical simulations via MATLAB are used to confirm the availability of the suggested control laws.

Load is the main external disturbance of a parallel robot manipulator. This disturbance will cause dynamic coupling among different degrees of freedom and make heaps of model-based control methods difficult to apply. In order to compensate this disturbance, it is crucial to obtain an accurate dynamic model of load. However, in practice, the load is always uncertain and its dynamic parameters are arduous to know a priori. To cope with this problem, this paper proposes a novel and simple approach to identify the dynamic parameters of load. Firstly, the dynamic model of the parallel robot manipulator with uncertain load is established and the dynamic coupling caused by load is also analyzed. Then, according to the dynamic model, the excitation signal is designed and a weak nonlinear dynamic model is derived. Furthermore, the identification model is presented and the identification algorithm based on the extended Kalman filter is designed. Lastly, numerical simulation results, obtained using a six-degree-of-freedom Gough–Stewart parallel manipulator, demonstrate the good estimation performance of the proposed method.

Quadruped robot dynamic gaits have much more advantages than static gaits on speed and efficiency, however high speed and efficiency calls for more complex mechanical structure and complicated control algorithm. It becomes even more challenging when the robot has more degrees of freedom. As a result, most of the present researches focused on simple robot, while the researches on dynamic gaits for complex robot with more degrees of freedom are relatively limited. The paper is focusing on the dynamic gaits control for complex robot with twenty degrees of freedom for the first time. Firstly, we build a relatively complete 3D model for quadruped robot based on spring loaded inverted pendulum U+0028 SLIP U+0029 model, analyze the inverse kinematics of the model, plan the trajectory of the swing foot and analyze the hydraulic drive. Secondly, we promote the control algorithm of one-legged to the quadruped robot based on the virtual leg and plan the state variables of pace gait and bound gait. Lastly, we realize the above two kinds of dynamic gaits in ADAMS-MATLAB joint simulation platform which testify the validity of above method.

In order to improve the accuracy of localization and mapping, this paper proposes a Rao-Blackwellized particle filter SLAM based on grey wolf optimizer. The estimation performance of the Rao-Blackwellized particle filter is improved by the local exploration and global exploitation capabilities of the grey wolf optimizer. Low-weight particles move towards the “prey” and the estimates of the pose is further optimized in the process. The algorithm overcomes the problem of particle degradation to a certain extent and reduces the number of particles required for precise localization and mapping. The improved algorithm is compared with the gmapping on different data sets and the results prove that the proposed algorithm is effective in different environments.

This paper proposes an improved ant colony algorithm to achieve efficient searching capabilities of path planning in complicated maps for mobile robot. The improved ant colony algorithm uses the characteristics of A* algorithm and MAX-MIN Ant system. Firstly, the grid environment model is constructed. The evaluation function of A* algorithm and the bending suppression operator are introduced to improve the heuristic information of the Ant colony algorithm, which can accelerate the convergence speed and increase the smoothness of the global path. Secondly, the retraction mechanism is introduced to solve the deadlock problem. Then the MAX-MIN ant system is transformed into local diffusion pheromone and only the best solution from iteration trials can be added to pheromone update. And, strengths of the pheromone trails are effectively limited for avoiding premature convergence of search. This gives an effective improvement and high performance to ACO in complex tunnel, trough and baffle maps and gives a better result as compare to traditional versions of ACO. The simulation results show that the improved ant colony algorithm is more effective and faster.

DeepLab v3+ neural network shows excellent performance in semantic segmentation. In this paper, we proposed a segmentation framework based on DeepLab v3+ neural network and applied it to the problem of hyperspectral imagery classification (HSIC). The dimensionality reduction of the hyperspectral image is performed using principal component analysis (PCA). DeepLab v3+ is used to extract spatial features, and those are fused with spectral features. A support vector machine (SVM) classifier is used for fitting and classification. Experimental results show that the framework proposed in this paper outperforms most traditional machine learning algorithms and deep-learning algorithms in hyperspectral imagery classification tasks.

Membrane gas absorption (MGA) based on ceramic membranes is considered to be an effective technology for removing SOx from ship exhaust. To develop a convenient method for designing and amplifying a membrane contactor, the critical gas velocity was defined. Two simple connection modes, series and parallel modules, were assembled and compared with a single-tube membrane contactor. The critical gas velocity is associated with the size of the membrane element and module, based on which an empirical equation can be formulated to optimize the gas velocity in different membrane contactors. In addition, another amplifying method, using multiple tubes in one shell, was also studied with computational fluid dynamics (CFD) simulations. The results imply that the addition of baffles improves the desulfurization performance of the membrane contactor at the critical gas velocity. The methods and conclusions presented herein provide a design basis for using the contactor at an industrial scale.

In wireless local area networks (WLANs), link-layer multicast is a promising technology for many multimedia applications, e.g., video streaming, as multicast frames can reach multiple clients simultaneously. However, the efficiency of multicast in WLANs is usually low since multicast frames are transmitted at a basic data rate to reach clients with poor channel quality. Moreover, the reliability of multicast cannot be guaranteed either, as multicast transmissions are not acknowledged. Some recent works have utilized smart antennas to improve multicast performance. However, most of them require customized hardware and are not designed for the latest IEEE 802.11 standard, 802.11n WLANs. More importantly, these works did not consider the impact of AP association strategies on the performance of link-layer multicast. In this paper, we study the problem of link-layer multicast in large-scale 802.11n WLANs where each AP is equipped with a smart antenna that supports multiple antenna patterns. Based on channel gains of various antenna patterns from different APs, we choose the associated AP for multicast clients, partition associated clients of each AP into multiple groups, select an antenna pattern and data rate for each group of client, and transmit multicast frames to each group once. We first examine the limitation of multicast in 802.11n WLANs and the benefits of smart antennas to multicast via experiments. We then introduce the system model and formulate the problem into an optimization problem, and prove its NP-hardness. The objective is to minimize the time to transmit a multicast frame to all clients while guaranteeing high packet reception ratio (PRR) for each client. For generic WLANs where APs are sparsely deployed, we propose an optimal algorithm under the condition that the PRR of antenna patterns and data rates are known for every client. For generic large-scale WLANs, we further propose an on-line algorithm in which the AP association strategy, the partition of clients, the antenna pattern and data rate of each group are adapted dynamically, based on PRR reports from clients. We have implemented the on-line algorithm on off-the-shelf WLAN products, and conducted extensive experiments and simulations to evaluate the performance. The results show that the proposed algorithm can significantly improve multicast performance compared to other schemes, and at the same time guarantee high PRR for all clients.

This paper deals with the problem of chaotic synchronization for coupled neural networks by a novel control strategy. Additional communication control graphs which prescribe the information flow available for controls purposes are introduced, and such pinning system has not been investigated before. Many relaxed information are used to construct augmented Lyapunov-Krasovskii functional (LKF), which makes use of more relax variables by employing the new type augmented matrices with Kronecker produce operation. By resorting to Lyapunov function methods and analysis techniques, the tasks to get the pinning synchronization of dynamical networks are solved in terms of a set of LMI inequalities, which are easy to be analyzed or tested. Finally, numerical simulations are performed to demonstrate the effectiveness of the analytical results.

This paper presents a neural network for solving the inverse variational inequality problems. The proposed neural network possesses a simple one-layer structure and is suitable for parallel implementation. It is shown that the proposed neural networks are globally convergent to the optimal solution of the inverse variational inequality and are globally asymptotically stable, and globally exponentially stable, respectively under different conditions. Numerical examples are provided to illustrate the effectiveness and satisfactory performance of the neural networks.

Removal of SO2 from ship exhaust gas requires a specifically designed device due to the limitations of narrow space and inconvenient replacement of absorbent. Therefore, the scrubber should be high-efficient and the absorbent for SO2 removal should be efficiently utilized. In this work, a hydrophilic membrane (HIM) contactor was employed to selectively remove SO2 using NaOH solution from simulated ship exhaust gas. Mass transfer analysis demonstrated that the wetted membrane layer contributes to lower mass transfer of CO2 by limiting the diffusion of OH−, but it can improve the mass transfer of SO2 in the membrane phase. Experimental data showed that a SO2/CO2 selectivity factor of ~114 and a SO2 absorption flux over 0.2 mol·m−2·h−1 can be stably obtained using the HIM contactor and 0.4 mol·L−1 NaOH. These values were higher than those obtained in a hydrophobic membrane (HOM) contactor under the same conditions. The performance of selective removal of SO2 under various parameters were also investigated, including NaOH concentration, inlet SO2 concentration and packing density of membrane module.

The problem of non-fragile pinning synchronization for complex networks is studied in this paper. Firstly, by using an inequality, which is introduced by using Newton-Leibniz formula, synchronization criterion is obtained by using novel mathematical skills. Secondly, a novel variable subintervals method is applied to expand the former results. Unlike previous results, the interval delay is divided into some dynamic variable sub-delays, which can introduce more weighting matrices. Thirdly, by resorting to Lyapunov function methods and some analysis techniques, the non-fragile pinning synchronization results are solved in terms of a set of LMI inequalities which are easy to be analyzed or computed. Finally, a numerical example is given to demonstrate the efficacy and efficiency of the proposed methods.

This paper concerns synchronization problems for complex networks with interval delays. By using an inequality that is introduced from Newton-Leibniz formula and combining the Finsler’s Lemma with homogenous matrix, convergent LMI relaxations for synchronization analysis are proposed with matrix-valued coefficients. Finally, a numerical example is provided to illustrate the effectiveness of the proposed methods.

This study proposes an adaptive neural network controller for a 3-DOF robotic manipulator that is subject to backlash-like hysteresis and friction. Two neural networks are used to approximate the dynamics and the hysteresis non-linearity. A neural network, which utilises a radial basis function approximates the robot's dynamics. The other neural network, which employs a hyperbolic tangent activation function, is used to approximate the unknown backlash-like hysteresis. The authors also consider two cases: full state and output feedback control. For output feedback, where system states are unknown, a high gain observer is employed to estimate the states. The proposed controllers ensure the boundedness of the control signals. Simulations are also performed to show the effectiveness of the controllers.

In this paper, a novel pinning synchronization (synchronization with pinning control) scheme for an array of neural networks with hybrid coupling is investigated. The main contributions are as follows: (1) A novel pinning control strategy is proposed for the first time. Pinning control schemes are introduced as an array of column vector. The controllers are designed as simple linear systems, which are easy to be analyzed or tested. (2) Augmented Lyapunov-Krasovskii functional (LKF) is applied to introduce more relax variables, which can alleviate the requirements of the positive definiteness of the matrix. (3) Based on the appropriate LKF, by introducing some free weighting matrices, some novel synchronization criteria are derived. Furthermore, the proposed pinning control scheme described by column vector can also be expanded to almost all the other array of neural networks. Finally, numerical examples are provided to show the effectiveness of the proposed results.

The effect of pore size and uniformity on the response of nanoporous alumina, formed on aluminum thick films through an anodization process, to ammonia and humidity at room temperature is reported. Pore sizes examined range from 13 nm to 48 nm, with pore size standard deviations ranging from 2.6 nm to 7.8 nm. The response of the material to ammonia and humidity is a strong function of pore size and operating frequency. At 5 kHz an alumina sensor with an average pore size of 13.6 nm, standard deviation 2.6 nm, exhibits a factor of two change in impedance magnitude as it is cycled between an ammonia and argon environment. At 5 kHz the same sensor exhibits a well-behaved change in impedance magnitude of 10 3 over 20% to 90% relative humidity. Cole-Cole plots of the 5 Hz to 13 MHz measured impedance spectra, modeled using equivalent circuits, are used to resolve the effects of adsorption and ion migration. Key words: Ammonia, Nanoporous, Impedance spectroscopy, Alumina, Humidity, Mesoporous.

Two wireless, passive remote query magnetism-based glucose sensors, which operate in combination with a mass and volume changing glucose responsive polymer, are presented. One sensor design is based upon the magnetostatic coupling of magnetically soft thin-film elements, as the polymer volume changes in response to glucose concentration so does the magnetostatic coupling between elements. In response to a time varying magnetic field, upon reversal of the magnetization vector of the elements the magnetostatic coupling determines the time rate of change of magnetic flux, and hence the amplitude of the voltage spike generated in a pick-up coil. The other sensor consists of a free-standing magnetoelastic thick-film, coated with a thin layer of the glucose responsive polymer. In response to a time varying magnetic field the sensor mechanically vibrates at a characteristic resonant frequency, the characteristic resonant frequency of the sensor linearly tracks the change in mass of the glucose responsive polymer.