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In this paper, a new electrical power management system is proposed for light electric vehicles (LEV). The main power sources are lithium battery and proton exchange membrane fuel cell (PEMFC). According to the gray theory, the gray model is used for power evaluation, which will help the competitive electrical power management system to make a good switching. Then, the powers of Li-battery and PEMFC can compare each other and share its power by switching way which can achieve the objective of power management more efficiently. The parameters of LEV are voltage, current and power. The performance of lithium battery and PEMFC should be tested before designing the LEV power management system. The result shows that the proposed competitive electrical power management system can provide power continuously until the fuel is burnt out, and good power management performance is demonstrated.

This article considers the robust passivity analysis for a class of discrete-time recurrent neural networks (DRNNs) with mixed time-delays and uncertain parameters. The mixed time-delays that consist of both the discrete time-varying and distributed time-delays in a given range are presented, and the uncertain parameters are norm-bounded. The activation functions are assumed to be globally Lipschitz continuous. Based on new bounding technique and appropriate type of Lyapunov functional, a sufficient condition is investigated to guarantee the existence of the desired robust passivity condition for the DRNNs, which can be derived in terms of a family of linear matrix inequality (LMI). Some free-weighting matrices are introduced to reduce the conservatism of the criterion by using the bounding technique. A numerical example is given to illustrate the effectiveness and applicability.

Multitudinous parameters involved have made the direct methanol fuel cell (DMFC) a complex “black-box,” posing challenges and difficulties in its modeling. This paper presents a neural network (NN) model with immune-based particle swarm optimization (IPSO) approach of the DMFC system, which is different from the conventional complex mathematical models. With the actual operation of DMFC taken into consideration, the polarization curves are run under a stable condition as the reference data for training the model. To reduce time cost for the training procedure and maintaining minimum modeling error, the IPSO algorithm is applied to the learning procedure of NN model. By combining the NN and the IPSO, the weight of the transfer function on the node in the hidden layer can be adjusted to minimize modeling error. The simulation results were in agreement with the experimental results, showing that the hybridization of NN model with IPSO approach can effectively demonstrate the polarization behaviors on a DMFC ...

This article has presented a new approach to estimate the output voltage of proton exchange membrane fuel cell (PEMFC) accurately by combining the use of a back-propagation neural network (BPNN) model and the Taguchi method. Using the PEMFC experimental data measured from performance test equipment of PEMFC, the BPNN model could be trained and constructed for obtaining the steady state output voltage of PEMFC. Furthermore, in order to determine the important parameters in BPNN, the Taguchi method is used for parameter optimization, with the goal of reducing the estimation error. The test equipment of PEMFC is accurate enough for acquiring the output voltage of PEMFC, and is quite useful for teaching purpose. However, taking the high cost, complicated operation procedure, and environment safety into consideration, it is necessary to develop a simulation model of PEMFC to benefit teaching and R&D. Therefore, this article will present an approach for constructing a BPNN model with precise accuracy for the ou...

This paper presents techniques for building system configuration, control architecture, and implementation of a vision-based wheeled mobile robot (WMR). The completed WMR has been built with the dead-reckoning method so as to determine the vehicle's velocity and posture by the numerical differentiation/integration over short travelling. The developed proportional-integral-derivative (PID) controllers show good transient performances; that is, the velocity of right and left wheels can track the commands quickly and correctly. Moreover, the path-tracking control laws have also been executed within the digital signal processor (DSP)-based controller in the WMR. The image-recognized system can obtain motion information at 15 frames/s by using the hybrid intelligent system (HIS) model, which is one of the well-known colour detection methods. The better performance a vision system has, the more successful the control laws design. The WMR obtains its posture from the dead-reckoning device together with the vision system. These subsystems are integrated, and the operators of the whole system are completed. This WMR system can be thought of as a platform for testing various tracking control laws and a signal-filtering method. To solve the problem of position/orientation tracking control of the WMR, two kinematical optimal non-linear predictive control laws are developed to manipulate the vehicle to follow the desired trajectories asymptotically. A Kalman filter scheme is used to reduce the bad effect of the imagine nose; thereby the accuracy of pose estimation can be improved. The experimental system is composed of a wireless RS232 modem, a DSP-based controller for the WMR, and a vision system with a host computer. A computation-effective and high-performance DSP-based controller is constructed for executing the developed sophisticated path-tracking laws. Finally, the simulation and experimental results show the feasibility and effectiveness of the proposed control laws.

A sliding mode controller u( t) is designed to achieve the following three objectives simultaneously: eigenvalue placement, H∞ norm constraint, and individual state variance constraint for bi-linear stochastic systems. By using the invariance property of sliding mode control, the matched bi-linear term of the system disappears on the sliding mode. With the aid of the upper bound covariance control theory, the controller u( t) is derived in which the control feedback gain matrix G is synthesized for achieving the above multiple objectives.

In this paper, a new approach to estimate the optimal performance of an unknown proton exchange membrane fuel cell (PEMFC) has been proposed. This proposed approach combines the Taguchi method and the numerical PEMFC model. Simulation results obtained using the Taguchi method help to determine the value of control factors that represent the tested unknown PEMFC. The objective of reducing both fuel consumption and operation cost can be achieved by determining the parameters for the unknown PEMFC. In addition, the optimal operation power for the tested unknown PEMFC can also be predicted. Experimental results on the test equipment show that the proposed approach is effective in optimal performance estimation for the tested unknown PEMFC, thus demonstrating the success achieved by combining the Taguchi method and the numerical PEMFC model.

This paper deals with the problem of robust stability for discrete-time uncertain genetic regulatory networks with both discrete interval and infinite-distributed time-varying delays, where discrete time-varying delay is on the lower and upper bounds and the uncertain parameters are norm-bounded. By employing Lyapunov-Krasovskii functional method combined with the bounding technique, a sufficient condition is established that guarantees the robust stability for discrete-time uncertain genetic regulatory networks with infinite-distributed delays. A particular feature of the criterion is dependent on the lower and upper bounds of the discrete interval time-varying delays, and is therefore less conservative than the delay-independent ones. Simulation examples are to prove the feasibility and applicability for proposed result.

With the widespread popularity of Android devices, the number of Android applications has increased dramatically in recent years. In order to assure the quality of the applications, Android testing has drawn extensive attention. This paper proposes an approach to automate the testing of Android applications based on the Capture and Replay method. Particularly, the user events of Android applications are captured and converted into Robotium test scripts that can be executed to replay the recorded actions of users. The approach also allows inserting assertions when capturing user interactions for verifying the outputs of Android UI components. A supporting tool is implemented to illustrate the usefulness of the proposed approach.

A performance-constrained sliding mode controller will be proposed to satisfy the pole assignment, H∞ norm, and individual variance constraints for linear perturbed MIMO systems. Application of this controller involves a simulation of the linearized perturbed ship steering yaw-motion systems which will be provided for demonstration of the effectiveness of the present methodology. The control law developed in this paper is designed in the framework of a combination of the concept of the sliding mode control with the theory of upper bound covariance control. Copyright © 2000 John Wiley & Sons, Ltd.

Based on the concept of variable structure control, this paper investigates the steady state covariance assignment problem for perturbed stochastic multivariable systems. By using the invariance property of variable structure systems, the matched perturbation of the system disappears on the sliding mode. With the aid of Ito-formula, the controller u(t) is proposed. Combining the sliding phase and hitting phase of the system design, the control feedback gain matrix G is derived to satisfy the steady state covariance assignment.

Based on the concept of variable structure control, this paper investigates the local state covariance assignment problem for stochastic large-scale systems. By using the invariance property of variable structure systems, the interconnection terms with matching condition will disappear on the sliding mode. With the aid of Ito-formula, the hitting controller of each subsystem is derived. Combining the sliding phase and hitting phase of the system design, the local feedback gain matrix Gi for each subsystem is obtained to achieve the local state covariance assignment.

Based on the concept of sliding mode control (SMC), this paper investigates the local-state upper bound covariance control (UBCC) problems with the H/sub /spl infin// norm and variance constraints for stochastic uncertain large-scale systems. By using the invariance property of variable structure systems, the uncertain interconnection terms with a matching condition will disappear on the sliding mode. Combining the sliding phase and hitting phase of the system design, the hitting controller and the gain matrix G, for each subsystem are proposed to achieve the goal.

A performance constrained variable structure controller will be introduced to satisfy the pole assignment, H ∞ norm and individual variance constrains for linearized perturbed ship steering yaw-motion systems in this paper. Combining the variable structure control and upper bound covariance control, a control feedback gain will be first developed to achieve the above system performance constraints for a linear perturbed system. Applying the results of this combined technique, a simulation for the linearized perturbed ship steering yaw-motion systems will then be provided to demonstrate the effectiveness of the present methodology.

The determination of the critical path (CP) in stochastic networks is difficult. It is partly due to the randomness of path durations and partly due to the probability issue of the selection of the critical path in the network. What we are confronted with is not only the complexity among random variables but also the problem of path dependence of the network. Besides, we found that CP is not necessarily the longest (or shortest) path in the network, which was a conventional assumption in use. The Program Evaluation and Review Technique (PERT) and Critical Path Index (CPI) approaches are not able to deal with this problem efficiently. In this study, we give a new definition on the CP in stochastic network and propose a modified label-correcting tracing algorithm (M-LCTA) to solve it. Based on the numerical results, compared with Monte Carlo simulation (MCS), the proposed approach can accurately determine the CP in stochastic networks.

This paper investigates the global delay-dependent robust stability in the mean square for uncertain stochastic neural networks with time-varying delay. The activation functions are assumed to be globally Lipschitz continuous. Based on a linear matrix inequality approach, globally delay-dependent robust stability criterion is derived by introducing some relaxation matrices which, when chosen properly, lead to a less conservative result. Two numerical examples are given to illustrate the effectiveness of the method.

Based on the concept of variable structure control, this paper investigates the upper bound covariance assignment with H(subscript ∞) norm and variance constrained problem for perturbed stochastic systems. Because of the invariance property of variable structure systems, the matched perturbation of the system disappears on the sliding mode. With the aid of Ito-formula, the controller u(t) is proposed. Combining the sliding phase and hitting phase of the system design, the control feedback gain matrix G is derived to achieve upper bound covariance assignment with H(subscript ∞) norm and variance constraints.

In this paper, the problem of static output feedback stabilization is considered. By congruence and state-coordinate transformations, a convex condition for static output feedback stabilization is given in terms of a matrix linear inequality while subject to an equality constraint. To facilitate stabilization design via LMI conditions, the equality constraint is satisfied by imposing a structure constraint on matrix variable. Moreover, a numerical construction algorithm and a sufficient condition for the presented convex condition are given as well. Finally, numerical examples demonstrated the utility of the proposed approach for static output feedback stabilization.

The constrained controller u(t) is proposed to achieve the error state upper bound covariance control with H/sub /spl infin// norm and variance constraints for stochastic model reference systems. Because of the invariance property of sliding mode control systems, the matched reference model input and the plant error term disappear on the sliding mode of the error system. Combining the sliding phase and hitting phase of sliding mode control, the control feedback gain matrix G is derived to achieve the goals.

Based on the concept of sliding mode control, the paper studies the error state covariance assignment problem for stochastic model reference systems. Because of the invariance property of variable structure systems, the matched model reference input and the plant error term disappear on the sliding mode of the error system. With the aid of the Ito-formula, the controller is proposed. Combining the sliding phase and hitting phase of the system design, the feedback gain matrix G is derived to achieve the error state covariance assignment.