Search

A dual neural-network method for the coordination of kinematically redundant robots is proposed in this paper. The performance criteria for single robots provided by Nedungadi and Kazerounian are generalized to a multicriteria form for the coordinated-manipulation system composed of multiple serial manipulators. By optimizing the local joint torques and generalized forces applied on the object/workpiece using a designed weighting matrix, the proposed method achieves the global stability during the coordinated-manipulation process. Moreover, the proposed algorithm has an explicit physical meaning, i.e., both the global kinetic energy of the coordination system and the two-norm of the generalized forces applied on the object are minimized simultaneously. In addition, the physical limits of both joint torques and the generalized forces applied on the object are considered, which makes the original coordination problem become a complicated optimization problem subject to both equality and inequality constraints. Compared with numerical optimization algorithms used in existing literatures, the dual neural-network method has better computational capability to deal with the complicated optimization problem. Finally, illustrative examples are given to show that the proposed method is effective and efficient for the multirobot coordinated-manipulation system. [ABSTRACT FROM AUTHOR]

In this paper, a delayed projection neural network is proposed for solving a class of linear variational inequality problems. The theoretical analysis shows that the proposed neural network is globally exponentially stable under different conditions. By the proposed linear matrix inequality (LMI) method, the monotonicity assumption on the linear variational inequality is no longer necessary. By employing Lagrange multipliers, the proposed method can resolve the constrained quadratic programming problems. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed neural network. [ABSTRACT FROM AUTHOR]

Abstract-A robust adaptive control approach is proposed to solve the consensus problem of multiagent systems. Compared with the previous work, the agent's dynamics includes the uncertainties and external disturbances, which is more practical in real-world applications. Due to the approximation capability of neural networks, the uncertain dynamics is compensated by the adaptive neural network scheme. The effects of the approximation error and external disturbances are counteracted by employing the robustness signal. The proposed algorithm is decentralized because the controller for each agent only utilizes the information of its neighbor agents. By the theoretical analysis, it is proved that the consensus error can be reduced as small as desired. The proposed method is then extended to two cases: Agents form a prescribed formation, and agents have the higher order dynamics. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed method. [ABSTRACT FROM AUTHOR]

Multi-variable generalized predictive control algorithm has obtained great success in process industries. However, it suffers from a high computational cost because the multi-stage optimization approach in the algorithm is time-consuming when constraints of the control system are considered. In this paper, a dual neural network is employed to deal with the multi-stage optimization problem, and bounded constraints on the input and output signals of the control system are taken into account. The dual neural network has many favorable features such as simple structure, rapid execution, and easy implementation. Therefore, the computation efficiency, in comparison with the consecutive executions of numerical algorithms on digital computers, is increased dramatically. In addition, the dual network model can yield the exact optimum values of future control signals while many other neural networks only obtain the approximate optimal solutions. Hence the multi-variable generalized predictive control algorithm based on the dual neural network is suitable for industrial applications with the real-time computation requirement. Simulation examples are given to demonstrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

A recurrent neural network for the optimal control of a group of interconnected dynamic systems is presented in this paper. On the basis of decomposition and coordination strategy for interconnected dynamic systems, the proposed neural network has a two-level hierarchical structure: several local optimization subnetworks at the lower level and one coordination subnetwork at the upper level. A goal-coordination method is used to coordinate the interactions between the subsystems. By nesting the dynamic equations of the subsystems into their corresponding local optimization subnetworks, the number of dimensions of the neural network can be reduced significantly. Furthermore, the subnetworks at both the lower and upper levels can work concurrently. Therefore, the computation efficiency, in comparison with the consecutive executions of numerical algorithms on digital computers, is increased dramatically. The proposed method is extended to the case where the control inputs of the subsystems are bounded. The stability analysis shows that the proposed neural network is asymptotically stable. Finally, an example is presented which demonstrates the satisfactory performance of the neural network. [ABSTRACT FROM AUTHOR]

Abstract  Neural units with higher-order synaptic operations have good computational properties in information processing and control applications. This paper presents neural units with higher-order synaptic operations for visual image processing applications. We use the neural units with higher-order synaptic operations for edge detection and employ the Hough transform to process the edge detection results. The edge detection method based on the neural unit with higher-order synaptic operations has been applied to solve routing problems of mobile robots. Simulation results show that the proposed neural units with higher-order synaptic operations are efficient for image processing and routing applications of mobile robots. [ABSTRACT FROM AUTHOR]

Intervention surgery strongly requires information on the guide-wire position under the monitoring of X-ray video. Hence, the related researches such as guide-wire detecting or tracking have become widespread. However, most of the existing methods require a lot of resources for computing or large data for training since the X-ray videos have internal physicals such as anatomical skeleton contours and organs that are quite similar to a guide-wire. This work presents a practical method that only requires a moderate number of training data for detecting a guide-wire tip in an X-ray video sequence during the percutaneous coronary intervention surgery. The method applies maximally stable extremal regions (MSER) combine with modified multi-filters (region area range filter and stroke width variation filter) for region detection and local binary patterns (LBP) for guide-wire recognition. The motivation for applying MSER and LBP are the robust efficacy and the low requirement of resources. The approach evaluated 20 different sequences of X-ray videos, a total of 1295 frames. 50 selected frames were used as training templates and others to experiment. The method was successfully performed to the detecting guide-wires with p-value < 0.01 compared with conventional MSER methods, 93.7% average detection accuracy, and 21 fps average speed. [ABSTRACT FROM AUTHOR]

An introduction to the journal is presented in which the editor discusses a study on particle swarm optimization with diversive curiosity, article on a proposed novel intelligent identification method, and a discussion on the structure of fault information fusion system.

The containment problem of first-order and second-order integral multi-agent systems with communication noises is investigated in this paper. Motivated by the previous work, a time-varying gain a(t) is introduced to attenuate the noise effect. It is proved that the proposed protocol can solve the mean square containment problem if the following conditions hold: (1) the communication topology graph has a spanning forest whose roots are leaders of the multi-agent system; (2)∫ 0∞a(t)dt=∞ (3) limt→∞a(t)=0. Moreover, if a(t) is uniformly continuous, these conditions are necessary for ensuring the mean square containment. In addition, the proposed protocol is also extended to allow the case where each agent has its own gain ai(t). It is proved that the mean square containment problem can still be solved if all agent-dependent gains are infinitesimals of the same order as time goes to infinity. Finally, two simulation examples are provided to demonstrate the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to describe the spacecraft attitude for global representation without singularities. The nonlinear reduced-order observer is used to estimate the derivative of the spacecraft output, and the CNN is introduced to further improve the control performance through approximating the spacecraft attitude motion. The implementation of the basis functions of the CNN used in the proposed controllers depends only on the desired signals, and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the CNN approximation errors and external disturbances. The adaptive NN controller-II can efficiently avoid the over-estimation problem (i.e., the bound of the CNNs output is much larger than that of the approximated unknown function, and hence, the control input may be very large) existing in the adaptive NN controller-I. Both adaptive output feedback controllers using CNN can guarantee that all signals in the resulting closed-loop system are uniformly ultimately bounded. For performance comparisons, the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed. Simulation studies are presented to show the advantages of the proposed CNN-based output feedback approach over the standard adaptive output feedback approach. [ABSTRACT FROM AUTHOR]

The paper focuses on the study of solving the large-scale traveling salesman problem (TSP) based on neurodynamic programming. From this perspective, two methods, temporal difference learning and approximate Sarsa, are presented in detail. In essence, both of them try to learn an appropriate evaluation function on the basis of a finite amount of experience. To evaluate their performances, some computational experiments on both the Euclidean and asymmetric TSP instances are conducted. In contrast with the large size of the state space, only a few training sets have been used to obtain the initial results. Hence, the results are acceptable and encouraging in comparisons with some classical algorithms, and further study of this kind of methods, as well as applications in combinatorial optimization problems, is worth investigating. [ABSTRACT FROM AUTHOR]

In this paper, a new type of six-degrees of freedom (DOF) flexible parallel mechanism (FPM) is presented. This type of parallel mechanism possesses several favorable properties: 1) its number of DOFs is independent of the number of serial chains which make up the mechanism; 2) it has no kinematical singularities; 3) it is designed to move on rails, and therefore its workspace is much larger than that of a conventional parallel manipulator; and 4) without changing the number of DOFs and the kinematics of the mechanisms, the number of the serial chains can be reconfigured according to the needs of the tasks. These properties make the mechanism very preferable in practice, especially for such tasks as joining huge ship blocks, in which the manipulated objects vary dramatically both in weights and dimensions. Furthermore, the mechanism can be used as either a fully actuated system or an underactuated system. In the fully actuated case, the mechanism has six DOF motion capabilities and manipulation capabilities. However, in the underactuated case, the mechanism still has six DOF motion capabilities, but it has only five DOF manipulation capabilities. In this paper, both the inverse and forward kinematics are studied and expressed in a closed form. The workspace and singularity analysis of the mechanism are also presented. An example is presented to illustrate how to calculate the kinematics of the mechanism in both fully-actuated and underactuated cases. Finally, an application of such a mechanism to manufacturing industry is introduced. [ABSTRACT FROM AUTHOR]

The Rao-Blackwellized particle filter (RBPF) algorithm usually has better performance than the traditional particle filter (PF) by utilizing conditional dependency relationships between parts of the state variables to estimate. By doing so, RBPF could not only improve the estimation precision but also reduce the overall computational complexity. However, the computational burden is still too high for many real-time applications. To improve the efficiency of RBPF, the particle swarm optimization (PSO) is applied to drive all the particles to the regions where their likelihoods are high in the nonlinear area. So only a small number of particles are needed to participate in the required computation. The experimental results demonstrate that this novel algorithm is more efficient than the standard RBPF. [ABSTRACT FROM AUTHOR]