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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.

In this article, a novel model-free dynamic inversion-based Q-learning (DIQL) algorithm is proposed to solve the optimal tracking control (OTC) problem of unknown nonlinear input-affine discrete-time (DT) systems. Compared with the existing DIQL algorithm and the discount factor-based Q-learning (DFQL) algorithm, the proposed algorithm can eliminate the tracking error while ensuring that it is model-free and off-policy. First, a new deterministic Q-learning iterative scheme is presented, and based on this scheme, a model-based off-policy DIQL algorithm is designed. The advantage of this new scheme is that it can avoid the training of unusual data and improve data utilization, thereby saving computing resources. Simultaneously, the convergence and stability of the designed algorithm are analyzed, and the proof that adding probing noise into the behavior policy does not affect the convergence is presented. Then, by introducing neural networks (NNs), the model-free version of the designed algorithm is further proposed so that the OTC problem can be solved without any knowledge about the system dynamics. Finally, three simulation examples are given to demonstrate the effectiveness of the proposed algorithm.

In this paper, a parallel Image-based visual servoing/force controller is developed in order to solve the interaction problem between the collaborative robot and the environment so that the robot can track the position trajectory and the desired force at the same time. This control methodology is based on the image-based visual servoing (IBVS) dynamic computed torque control and couples the force control feedback in parallel. Simulations are performed on a collaborative Delta robot and two types of image features are tested to determine which one is better for this parallel IBVS/force controller. The results show the efficiency of this controller.

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.

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.

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.

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.

The type of road terrain is an important piece of environment perception information for smart vehicles. In this paper, an approach to recognize the road terrain by using road surface images is proposed. The texture features were extracted from images captured by a camera for the road terrain classification task. The support vector machine method was selected for terrain classification. The simulation experiments indicated that this approach worked very well on terrains with distinguished texture. However, this approach could not recognize the road terrain when the damaged captured image changed frequently due to the luminance or when the moving speed of the vehicle was too fast.

This paper deals with the synchronization problem for delayed static neural networks with hybrid couplings. When the static neural networks are affected by hybrid couplings, it is hard to deal with a large number of highly interconnected dynamical units in such a complex system. In order to solve this complicated problem, a new method is proposed to deal with the Kronecker product, and to make the synchronization problem to be easily analyzed. Further, based on the obtained result, by using the augmented Lyapunov–Krasovskii functional (LKF) method, multitude Kronecker product terms can be handled, which can introduce more relaxed conditions by employing the new type of augmented matrices with the Kronecker product operation. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed synchronization scheme.

Recently, several multi-channel MAC protocols have been proposed for wireless sensor networks (WSNs) to improve network capacity and boost energy efficiency. In addition, cooperative multiple-input multiple-output (MIMO) technique has been shown to be able to significantly enhance the energy efficiency of WSNs if properly configured. However, these two promising techniques have not been jointly considered in clustered WSNs. In this paper, we explore such a joint design by proposing a novel MAC protocol for clustered WSNs that takes advantage of both multiple channels and cooperative MIMO to further boost network performance. Specifically, sensor nodes in a WSN are organized into clusters and each cluster head selects some cooperative nodes to help forward traffic to or receive from other clusters by utilizing a cooperative MIMO technique. For intra-cluster communications, different channels are assigned to adjacent clusters to reduce collisions, while for inter-cluster communications, cooperative MIMO links are scheduled to improve energy efficiency and concurrent transmissions are enabled by assigning different channels to them. We also provide a network-wide time synchronization approach to facilitating the functionality of this protocol. We carry out extensive simulations and the results demonstrate that the proposed protocol can significantly increase throughput and improve energy efficiency compared to other schemes. For example, for a WSN with 150 sensors in a 250 i? 250 m 2 field, with five available channels, the protocol can achieve three times saturated throughput while saving 14 % energy per bit for inter-cluster communications. Propose a multi-channel, cooperative MIMO MAC protocol for clustered WSNs.Employ cooperative MIMO links for inter-cluster communications.Assign orthogonal channels to interfering cooperative links to boost throughput.Numerical results show that energy efficiency of WSNs is remarkably improved.Simulation results show that network throughput is significantly enhanced.

As the latest IEEE 802.11 standard, 802.11n applies several new technologies, such as multiple input multiple output (MIMO), channel bonding, and frame aggregation to greatly improve the rate, range and reliability of wireless local area networks (WLANs). In 802.11n WLANs, access points (APs) are often densely deployed to provide satisfactory coverage. Thus nearby APs should operate at non-overlapping channels to avoid mutual interference. It is challenging to assign channels in legacy 802.11a/b/g WLANs due to the limited number of channels. Channel assignment becomes more complex in 802.11n WLANs, as the channel bonding in 802.11n allows WLAN stations (APs and clients) to combine two adjacent, non-overlapping 20MHz channels together for transmission. On the other hand, IEEE 802.11n is backward compatible, such that 802.11n clients will coexist with legacy clients in 802.11n WLANs. Legacy clients may affect the performance of nearby 802.11n clients, and reduce the effectiveness of channel bonding. Based on these observations, in this paper, we study channel assignment in 802.11n WLANs with heterogeneous clients. We first present the network model, interference model, and throughput estimation model to estimate the throughput of each client. We then formulate the channel assignment problem into an optimization problem, with the objective of maximizing overall network throughput. Since the problem is NP-hard, we give a distributed channel assignment algorithm based on the throughput estimation model. We then present another channel assignment algorithm with lower complexity, and aim at minimizing interference experienced by high-rate, 802.11n clients. We have carried out extensive simulations to evaluate the proposed algorithms. Simulation results show that our algorithms can significantly improve the network throughput of 802.11n WLANs, compared with other channel assignment algorithms.

This paper presents a projection neural network with discrete delays and distributed delays (i.e. mixed delays) for solving linear variational inequality (LVI). By the Lyapunov theory and the linear matrix inequality (LMI) approach, the neural network is proved to be globally exponentially convergent to the solution of LVI. Compared with existing neural networks for solving LVI, the proposed one features the ability of solving a class of non-monotone LVI. One numerical example is provided to illustrate the effectiveness and the satisfactory performance of the neural network.

Recent experimental studies have revealed that a large percentage of wireless links are lossy and unreliable for data delivery in wireless sensor networks (WSNs). Such findings raise new challenges for the design of clustering algorithms in WSNs in terms of data reliability and energy efficiency. In this paper, we propose distributed clustering algorithms for lossy WSNs with a mobile collector, where the mobile collector moves close to each cluster head to receive data directly and then uploads collected data to the base station. We first consider constructing one-hop clusters in lossy WSNs where all cluster members are within the direct communication range of their cluster heads. We formulate the problem into an integer program, aiming at maximizing the network lifetime, which is defined as the number of rounds of data collection until the first node dies. We then prove that the problem is NP-hard. After that, we propose a metric-based distributed clustering algorithm to solve the problem. We adopt a metric called selection weight for each sensor node that indicates both link qualities around the node and its capability of being a cluster head. We further extend the algorithm to multi-hop clustering to achieve better scalability. We have found out that the performance of the one-hop clustering algorithm in small WSNs is very close to the optimal results obtained by mathematical tools. We have conducted extensive simulations for large WSNs and the results demonstrate that the proposed clustering algorithms can significantly improve the data reception ratio, reduce the total energy consumption in the network and prolong network lifetime compared to a typical distributed clustering algorithm, HEED, that does not consider lossy links.

Recently, a neutral-type delayed projection neural network (NDPNN) was developed for solving variational inequality problems. This paper addresses the global stability and convergence of the NDPNN and presents new results for it to solve linear variational inequality (LVI). Compared with existing convergence results for neural networks to solve LVI, our results do not require the LVI that is monotone so as to guarantee the NDPNN that can solve a class of non-monotone LVI. All the results are expressed in terms of linear matrix inequalities, which can be easily checked. Simulation examples demonstrate the effectiveness of the obtained results.

In recent years, a projection neural network was proposed for solving linear variational inequality (LVI) problems and related optimization problems, which required the monotonicity of LVI to guarantee its convergence to the optimal solution. In this paper, we present a new result on the global exponential convergence of the projection neural network. Unlike existing convergence results for the projection neural network, our main result does not assume the monotonicity of LVI problems. Therefore, the projection neural network can be further guaranteed to solve a class of non-monotone LVI and non-convex optimization problems. Numerical examples illustrate the effectiveness of the obtained result.

This paper is concerned with the problem of global synchronization for complex networks with hybrid coupling. An innovative approach is proposed to develop delay-dependent criteria for the system, which makes use of many relaxed information to construct Lyapunov functional, and employs an integral equation method and matrix expansion method to handle multitude Kronecker product terms. The method in this paper increases the number of arbitrary matrix, and alleviates the requirements of the positive definiteness of the matrix which should be considered in many existing references. This leads to significant improvement in the performance of the synchronization results. Finally, a chaotic synchronization example is provided to show the effectiveness of the proposed results.

This paper investigates the problem of pinning synchronization for a general complex networks with multiple time-varying coupling delays by using Lyapunov functional method. Firstly, we design a controller to keep the given system synchronized, and provide a simple approximate formula to estimate the detailed pinning synchronization states. Furthermore, by using the same method, an adaptive pinning control scheme is developed to achieve global synchronization, and the adaptive pinning controllers are simpler than some traditional controllers. Moreover, the presented results can also be applied to the system with single time delay, and the coupling-configuration matrices are not necessarily symmetric. Finally, numerical examples are given to show the effectiveness of the proposed methods.

This paper is concerned with an array of neutral-type neural networks with hybrid nonlinear coupling, which is composed of discrete-delay coupling and distributed-delay coupling. The discrete-delay considered in this system is assumed to vary over an interval (0 < h 1 ? ? 1(t) ? h 2), where the lower and the upper bounds are known. Based on Lyapunov---Krasovskii (L---K) functional and Kronecker product technique, by introducing several new free-weighting matrices, two novel criterions are acquired to ensure the global synchronization of the proposed networks, which are proved to be much less conservative than some exiting results. Moreover, the derivative of discrete-delay can take any value. Finally, numerical examples are provided to show the effectiveness of the proposed results.

By linearizing complex networks with multiple coupling delays to some time-delayed subsystems, for the first time, some new criterions are given to ensure the global synchronization of the system with multiple delays. Then, based on the proposed criterions and Lyapunov stability theory, pinning control schemes for this system are developed to achieve global synchronization. The obtained conditions are expressed within the framework of linear matrix inequalities and can be easily checked in practice. Finally, several numerical examples are provided to show the effectiveness of the proposed results.

This paper is concerned with the problem of global synchronization for a general complex networks. Based on a useful inequality and Kronecker product technique, a new criterion is obtained, which has fewer unknown variables and is a significant improvement in the performance. Synchronization criteria are derived by some new mathematical skills and Schur complement. The result is expressed by linear matrix inequalities, which can be easily computed and checked in practice. Finally, numerical examples will be used to show the effectiveness of the obtained result.

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.

This paper is concerned with the synchronization problems for an array of neural networks with hybrid coupling and interval time-varying delay. First, a novel augmented Lyapunov-Krasovskii functional (LKF) method is proposed to develop delay-dependent synchronization criteria for the networks, which makes use of more relaxed conditions by employing the new type of augmented matrices with Kronecker product operation. The proposed method can handle a multitude of Kronecker product operations in the LKF and alleviates the requirements of the positive definiteness of some conditional matrices which are usually considered in the existing methods for complex networks. This leads to a significant improvement in the performance of the synchronization criteria, i.e., less conservative synchronization results can be obtained. Meanwhile, the case of fast time-varying delay can also be handled by the proposed method. Furthermore, based on the derived criteria, a robust synchronization criterion is obtained for the system with uncertainties both in coefficient and coupling matrix terms. Since an expression based on linear matrix inequality is used, the proposed criteria can be easily checked in practice. Finally, numerical examples are provided to show the effectiveness of the proposed method.