Tiejun Wu

A Framework to Model the Topological Structure of Supply Networks

Topological structure is considered more and more important in managing a supply network or predicting its development. In this paper, a new framework is proposed to model the topological structure of supply networks, where different types of supply networks can be created just by introducing different supplier-customer connecting rules. Generally, the networks created in the framework are much different from the random networks with the same degree sequences. The revealed phenomenon suggests that real-world supply networks may benefit from its intrinsic mechanism on flexibility, efficiency, and robustness to target attacks. Note to Practitioners-The topological structure of supply networks is considered more and more important in managing a supply network or predicting its development. In this paper, we introduce a framework to model and analyze the topological structure of supply networks. This work aims to characterize supply networks by statistical methods and can help researchers better understand the material dynamics on supply networks and further conveniently create their own supply networks by summarizing practical supplier-customer connecting rules or analyzing real-world supply network data. The work should be further expanded in other aspects, such as simulating material dynamics on supply networks, designing optimal structure by introducing proper supplier-customer connecting rules, rearranging local connections to enhance the competi tiveness and further ensure the long-term benefit of a target firm, and so on, all of which are of much interest for governors, investors, and managers and can be studied in the present framework in the future.

Car-following model of vehicular traffic

Car-following model is a microscopic simulation model of vehicular traffic, which describes the one-by-one following process of vehicles on the same lane. As a common traffic phenomenon, following behavior is important in the micro-study of traffic. In consequence, the car-following has been paid great attention. Compared with other traffic-flow models, car-following model embodies the human factors and reflects the real traffic situation in a better way. This paper, which introduces and analyzes the advantages and disadvantages of GHR model, OV model, CA model, and fuzzy-logic model in detail, gives a systematic review of the development and actuality of car-following model. In final, it discusses two types of stability analytic theory: local stability and asymptotic stability, to some extent.The Car-following models is a kind of microscopic simulation model for vehicular traffic, which describe the one-by-one following behaviors of vehicles in the same traffic lane. As a common traffic phenomenon, following behavior is of great importance in the micro-study of intelligent traffic control. Compared with other traffic-flow models, car-following model embodies the human factors and reflects the real traffic situation in a better way. This paper gives a systematic review of the development and actuality of car-following models by introducing and analyzing in detail the advantages and disadvantages of GHR model, OV model, CA model and fuzzy-logic model. In addition, local stability and asymptotic stability of car-following models are discussed in this paper.

An accelerated ant colony algorithm for complex nonlinear system optimization

Ant colony algorithms as a category of evolutionary computational intelligence can deal with complex optimization problems better than traditional optimization techniques. An accelerated ant colony algorithm is proposed in this paper to tackle complex nonlinear system optimization problems by using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates. Global optimal solutions can be obtained more efficiently through self-adjusting the path searching behaviors of the artificial ants. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The simulation results convectively show that, in comparison with traditional optimization approaches and currently used basic ant colony algorithms, the proposed algorithm possess prominent capability in dealing with complex nonlinear system optimization problems with extremely complex solution structures and is applicable in solving complicated nonlinear optimization problems in practice such as network optimization and transportation problems.

Stability Analysis of Large-scale Nonlinear Systems with Hybrid Models

This paper proposes a development method to deal with the stability problems for a kind of complex large-scale systems with hybrid models. These hybrid large-scale systems are composed of considerable interconnected nonlinear subsystems, some of which are described by differential equations and others by Takagi-Sugeno fuzzy models. Novel techniques to cope with the nonlinear interconnection between subsystems are developed. A set of LMI-based conditions are derived to judge the stability of the whole system by checking the stability of the subsystems in parallel, which greatly speeds up the stability analysis process. And the computational complexity is greatly reduced. A numerical example is given to illustrate its effectiveness

Real-Time Path Planning for Mobile Robots

A new on-line real-time approach with obstacle avoidance for mobile robots moving in an uncertain environment has been proposed and implemented. With the integration of global planning and local planning, this path planning approach is based on polar coordinates in which the desirable direction angle is taken into consideration as an optimization index. Detecting unknown obstacles with local feedback information by robot’s sensor system, this approach orients the desirable direction of mobile robot so as to generate local sub-goal in every planning window. As a result, the difference between real direction angle and desirable direction angle of robot motion steers the mobile robot to detour collisions and advance toward the target without stopping to re-plan a path when new sensor data become available. This approach is not only simple and flexible, but also overcomes flaws of global planning and local planning. The effectiveness, feasibility, real-time performance, optimization capability, high precision and perfect stability are demonstrated by means of simulation examples.

Grasp evaluation and contact points planning for polyhedral objects using a ray-shooting algorithm

Grasp evaluation and planning are two fundamental issues in robotic grasping and dexterous manipulation. Most traditional methods for grasp quality evaluation suffer from non-uniformity of the wrench space and a dependence on the scale and choice of the reference frame. To overcome these weaknesses, we present a grasp evaluation method based on disturbance force rejection under the assumption that the normal component of each individual contact force is less than one. The evaluation criterion is solved using an enhanced ray-shooting algorithm in which the geometry of the grasp wrench space is read by the support mapping. This evaluation procedure is very fast due to the efficiency of the ray-shooting algorithm without linearization of the friction cones. Based on a necessary condition for grasp quality improvement, a heuristic searching algorithm for polyhedral object regrasp is also proposed. It starts from an initial force-closure unit grasp configuration and iteratively improves the grasp quality to find the locally optimum contact points. The efficiency and effectiveness of the proposed algorithms are illustrated by a number of numerical examples.

Decentralized adaptive iterative learning control for uncertain interconnected systems with state delay

Base on the uncertain interconnected systems with state delay, a decentralized model reference adaptive iterative learning control is proposed in this paper. The proposed controller of each subsystem only relies on local state variables with no any information exchanges with other subsystems. In order to eliminate the effects of interconnections, state delays and uncertainties, the adaptive parameters are updated along iteration axis. Simulation results demonstrate, utilizing the proposed adaptive controller, the tracking error for each subsystem converges along the iteration axis.

A relative feasibility degree based approach for constrained optimization problems

Based on the ratio of the size of the feasible region of constraints to the size of the feasible region of a constrained optimization problem, we propose a new constraint handling approach to improve the efficiency of heuristic search methods in solving the constrained optimization problems. In the traditional classification of a solution candidate, it is either a feasible or an infeasible solution. To refine this classification, a new concept about the relative feasibility degree of a solution candidate is proposed to represent the amount by which the ‘feasibility’ of the solution candidate exceeds that of another candidate. Relative feasibility degree based selection rules are also proposed to enable evolutionary computation techniques to accelerate the search process of reaching a feasible region. In addition, a relative feasibility degree based differential evolution algorithm is derived to solve constraint optimization problems. The proposed approach is tested with nine benchmark problems. Results indicate that our approach is very competitive compared with four existing state-of-the-art techniques, though still sensitive to the intervals of control parameters of the differential evolution.

Nonlinear model predictive control with immune optimization for voltage security control

A nonlinear model predictive control scheme with immune algorithm is proposed to solve power system voltage security control problems. A nonlinear differential-algebraic-inequality model is used to predict system behavior. A gradational targeting method is developed to decompose global horizon control targets into sub-objectives in receding prediction intervals via Pareto-type weighting functions. A novel immune algorithm is presented, using a multiple gene chain structure of antibodies to represent the solution candidates of the complicated optimization problem. Employing pattern recognition techniques to extract gene patterns of better antibodies, and identifying similar antigen patterns via learning and memorizing to create a better initial guess of solutions in order to accelerate the convergence of the optima searching procedure. System performance comparative results based on the emergency voltage control of a six-bus example power system are reported. The results indicate the promising application potential of the method proposed in this paper.

Delay-dependent robust H/sub /spl infin// control for with parameter uncertainties and time delay

A delay-dependent sufficient condition for the existence of a robust H/sub /spl infin// switched controller with state delay feedback for linear switched systems with parameter uncertainties and time delay was derived and formulated in nonlinear matrix inequalities solvable by an iterative algorithm. Compared with the conventional memoryless state-feedback controller, the proposed controller can achieve better robust control performance since the delayed state is utilized as additional feedback information and the parameters of the proposed controllers are changed synchronously with the dynamical characteristic of the system. This design method was also extended to the case where only delayed state is available for the controller. The example of balancing an inverted pendulum on a cart demonstrates the effectiveness and applicability of the proposed design methods.