Design of PID controller with incomplete derivation based on differential evolution algorithm

To determine the optimal or near optimal parameters of PID controller with incomplete derivation, a novel design method based on differential evolution （DE） algorithm is presented. The controller is called DE-PID controller. To overcome the disadvantages of the integral performance criteria in the frequency domain such as IAE, ISE, and ITSE, a new performance criterion in the time domain is proposed. The optimization procedures employing the DE algorithm to search the optimal or near optimal PID controller parameters of a control system are demonstrated in detail. Three typical control systems are chosen to test and evaluate the adaptation and robustness of the proposed DE-PID controller. The simulation results show that the proposed approach has superior features of easy implementation, stable convergence characteristic, and good computational efficiency. Compared with the ZN, GA, and ASA, the proposed design method is indeed more efficient and robust in improving the step response of a control system.     

Application of Neurocomputing in Adaptive Control of Large-Scale Aerospace Systems

We are engaged in solving two difficult problems in adaptive control of the large-scale time-variant aerospace system. One is parameter identification of time-variant continuous-time state-space modei; the other is how to solve algebraic Riccati equation (ARE) of large order efficiently. In our approach, two neural networks are employed to independently solve both the system identification problem and the ARE associated with the optimal control problem. Thus the identification and the control computation are combined in closed-loop, adaptive, real-time control system . The advantage of this approach is that the neural networks converge to their solutions very quickly and simultaneously.     

Optimal redundancy allocation for reliability systems with imperfect switching

The problem of stochastically allocating redundant com- ponents to increase the system lifetime is an important topic of reliability. An optimal redundancy allocation is proposed, which maximizes the expected lifetime of a reliability system with sub- systems consisting of components in parallel. The constraints are minimizing the total resources and the sizes of subsystems. In this system, each switching is independent with each other and works with probability p. Two optimization problems are studied by an incremental algorithm and dynamic programming technique respectively. The incremental algorithm proposed could obtain an approximate optimal solution, and the dynamic programming method could generate the optimal solution,     

Model algorithm control using neural networks for input delayed nonlinear control system

The performance of the model algorithm control method is partially based on the accuracy of the system’s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to "learn"the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.     

Stochastic framework for reliability enhancement using optimal feeder reconfiguration

Optimal distribution feeder reconfiguration （DFR） is a valuable and costless approach to increase the load balance, reduce the amount of power losses, and improve the voltage of the buses. In this way, this paper aims to investigate the optimal DFR strategy as a proper tool to improve the reliability of the radial distribution networks. The idea of failure rate reduction is employed to see the effect of feeder current reduction on the reliability of the system more accurately. The objects to be investigated are system average interruption frequency index （SAIFI）, system average interruption duration index （SAIDI）, average energy not supplied （AENS） and total active power losses. The problem is then formulated in a stochastic framework based on the point estimate method （PEM） to handle the uncertainty effects. The feasibility and satisfying performance of the proposed method is examined on a standard IEEE test system.     

Integration-centric approach to system readiness assessment based on evidential reasoning

An integration-centric approach is proposed to handle inadequate information in the system readiness level (SRL) assessment using the evidential reasoning (ER) algorithm. Current SRL assessment approaches cannot be applied to handle inadequate information as the input. The ER-based approach is proposed to synthesize inadequate input information and an integration-centric perspective is applied to reduce the computational complexity. Two case studies are performed to validate the efficiency of the proposed approach. And these studies are also performed to study how the inadequate information will affect the assessment result. And the differences caused by the system’s structure. The importance of the system’s structure in the SRL assessment is demonstrated and the contributions made in this study are summarized as conclusions.     

A GENERAL APPROACH BASED ON AUTOCORRELATION TO DETERMINE INPUT VARIABLES OF NEURAL NETWORKS FOR TIME SERIES FORECASTING

Input selection is probably one of the most critical decision issues in neural network designing, because it has a great impact on forecasting performance. Among the many applications of artificial neural networks to finance, time series forecasting is perhaps one of the most challenging issues. Considering the features of neural networks, we propose a general approach called Autocorrelation Criterion (AC) to determine the inputs variables for a neural network. The purpose is to seek optimal lag periods, which are more predictive and less correlated. AC is a data-driven approach in that there is no prior assumption about the models for time series under study. So it has extensive applications and avoids a lengthy experimentation and tinkering in input selection. We apply the approach to the determination of input variables for foreign exchange rate forecasting and conduct comparisons between AC and information-based in-sample model selection criterion. The experiment results show that AC outperforms inf     

Adaptive fuzzy sliding mode control for robotic airship with model uncertainty and external disturbance

An adaptive fuzzy sliding mode control (AFSMC) approach is proposed for a robotic airship.First,the mathematical model of an airship is derived in the form of a nonlinear control system.Second,an AFSMC approach is proposed to design the attitude control system of airship,and the global stability of the closed-loop system is proved by using the Lyapunov stability theorem.Finally,simulation results verify the effectiveness and robustness of the proposed control approach in the presence of model uncertainties and external disturbances.     

Capacitated stochastic coloured Petri net-based approach for computing two-terminal reliability of multi-state network

Classical network reliability problems assume both networks and components have only binary states,fully working or fully failed states.But many actual networks are multi-state,such as communication networks and transportation networks.The nodes and arcs in the networks may be in intermediate states which are not fully working either fully failed.A simulation approach for computing the two-terminal reliability of a multi-state network is described.Two-terminal reliability is defined as the probability that d units of demand can be supplied from the source to sink nodes under the time threshold T.The capacities of arcs may be in a stochastic state following any discrete or continuous distribution.The transmission time of each arc is also not a fixed number but stochastic according to its current capacity and demand.To solve this problem,a capacitated stochastic coloured Petri net is proposed for modelling the system behaviour.Places and transitions respectively stand for the nodes and arcs of a network.Capacitated transition and self-modified token colour with route information are defined to describe the multi-state network.By the simulation,the two-terminal reliability and node importance can be estimated and the optimal route whose reliability is highest can also be given.Finally,two examples of different kinds of multistate networks are given.     

Novel robust control framework for morphing aircraft

This paper develops a robust control methodology for a class of morphing aircraft,which is called innovative control effector(ICE) aircraft.For the ICE morphing aircraft,the distributed arrays of hundreds of shape-change devices are employed to stabilize and maneuver the air vehicle.Because the morphing aircraft have the inherent uncertainty and varying dynamics due to the alteration of their configuration,a desired control performance can not be satisfied with a fixed feedback controller.Therefore,a novel control framework including an adaptive flight control law and an adaptive allocation algorithm is proposed.Firstly,a state feedback adaptive control law is designed to guarantee closed-loop stability and state tracking in the presence of uncertain dynamics caused by the wing shape change due to different flight missions.In the control allocation,many distributed arrays are managed in an optimal way to improve the robustness of the system.The scheme is used to an uncertain morphing aircraft model,and the simulation results demonstrate their performance.     

Kind of deadlock-free scheduling method

Deadlock must be avoided in a manufacturing system. In this paper, an efficient algorithm for finding an optimal deadlock-free schedules in a manufacturing system with very limited buffer is presented. This algorithm is based on the effective genetic algorithm (GA) search method, and a formal Petri net structure is introduced to detect the token player assuring deadlock-free. In order to make the scheduling strategy generated by GA meet the required constraint of deadlock-free, some results of the structure analysis of Petri net are involved as a criterion to select deadlock-free schedule from the population generated by GA. The effectiveness and efficiency of the proposed approach is illustrated by using an example.     

Stationary Analysis and Optimal Control Under Multiple Working Vacation Policy in a GI/M~(a,b)/1 Queue

This paper considers an infinite buffer renewal input queue with multiple working vacation policy wherein customers are served by a single server according to general bulk service(a, b)-rule(1 ≤ a ≤ b). If the number of waiting customers in the system at a service completion epoch(during a normal busy period) is lower than ‘a', then the server starts a vacation. During a vacation if the number of waiting customers reaches the minimum threshold size ‘a', then the server starts serving this batch with a lower rate than that of the normal busy period. After completion of a batch service during working vacation, if the server finds less than ‘a' customers accumulated in the system, then the server takes another vacation, otherwise the server continues to serve the available batch with that lower rate. The maximum allowed size of a batch in service is ‘b'. The authors derive both queue-length and system-length distributions at pre-arrival epoch using both embedded Markov chain approach and the roots method. The arbitrary epoch probabilities are obtained using the classical argument based on renewal theory. Several performance measures like average queue and system-length, mean waiting-time, cost and profit optimization are studied and numerically computed.     

Modeling genetic regulatory networks: a delay discrete dynamical model approach

Modeling genetic regulatory networks is an important research topic in genomic research and computational systems biology.This paper considers the problem of constructing a genetic regulatory network(GRN) using the discrete dynamic system(DDS) model approach.Although considerable research has been devoted to building GRNs,many of the works did not consider the time-delay effect. Here,the authors propose a time-delay DDS model composed of linear difference equations to represent temporal interactions among significantly expressed genes.The authors also introduce interpolation scheme and re-sampling method for equalizing the non-uniformity of sampling time points.Statistical significance plays an active role in obtaining the optimal interaction matrix of GRNs.The constructed genetic network using linear multiple regression matches with the original data very well.Simulation results are given to demonstrate the effectiveness of the proposed method and model.     

Finite-time tracking control for motor servo systems with unknown dead-zones

A finite-time tracking control scheme is proposed in this paper based on the terminal slid- ing mode principle for motor servo systems with unknown nonlinear dead-zone inputs. By using the differential mean value theorem, the dead-zone is represented as a time-varying system and thus the inverse compensation approach is avoided. Then, an indirect terminal sliding mode control （ITSMC） is developed to guarantee the finite-time convergence of the tracking error and to overcome the singu- larity problem in the traditional terminal sliding mode control. In the proposed controller design, the unknown nonlinearity of the system is approximated by a simple sigmoid neural network, and the ap- proximation error is diminished by employing a robust term. Comparative experiments on a turntable servo system are conducted to show the superior performance of the proposed method.     

Stability analysis and design of time-delay uncertain systems using robust reliability method

A robust reliability method for stability analysis and reliability-based stabilization of time-delay dynamic systems with uncertain but bounded parameters is presented by treating the uncertain parameters as interval variables.The performance function used for robust reliability analysis is defined by a delayindependent stability criterion.The design of robust controllers is carried out by solving a reliability-based optimization problem in which the control cost satisfying design requirements is minimized.This kind of treatment makes it possible to achieve a balance between the reliability and control cost in the design of controller when uncertainties must be taken into account.By the method,a robust reliability measure of the degree of stability of a time-delay uncertain system can be provided,and the maximum robustness bounds of uncertain parameters such that the time-delay system to be stable can be obtained.All the procedures are based on the linear matrix inequality approach and therefore can be carried out conveniently.The effectiveness and feasibility of the proposed method are demonstrated with two practical examples.It is shown by numerical simulations and comparison that it is meaningful to take the robust reliability into account in the control design of uncertain systems.     

FLUID-BASED SIMULATION APPROACH FOR HIGH VOLUME CONVEYOR TRANSPORTATION SYSTEMS

High volume conveyor systems in distribution centers have very large footprint and can handlelarge volumes and hold thousands of items.Traditional discrete-event cell-based approach to simulatesuch networks becomes computationally challenging.An alternative approach,in which the traffic isrepresented by segments of fluid flow of different density instead of individual packages,is presentedin this paper to address this challenge.The proposed fluid-based simulation approach is developedusing a Hybrid Petri Nets framework.The underlying model is a combination of an extension of aBatches Petri Nets(BPN)and a Stochastic Petri Nets(SPN).The extensions are in the inclusion ofrandom elements and relaxation of certain structural constraints.Some adaptations are also made to fitthe target system modeling.The approach is presented with an example.     

System reliability of assured accuracy rate for multi-state computer networks from service level agreements viewpoint

From the viewpoint of service level agreements, the transmission accuracy rate is one of critical performance indicators to assess internet quality for system managers and customers. Under the assumption that each arc＇s capacity is deterministic, the quickest path problem is to find a path sending a specific of data such that the transmission time is minimized. However, in many real-life networks such as computer networks, each arc has stochastic capacity, lead time and accuracy rate. Such a network is named a multi-state computer network. Under both assured accuracy rate and time constraints, we extend the quickest path problem to compute the probability that d units of data can be sent through multiple minimal paths simultaneously. Such a probability named system reliability is a performance indicator to provide to managers for understanding the ability of system and improvement. An efficient algorithm is proposed to evaluate the system reliability in terms of the approach of minimal paths.     

American continuous-installment options of barrier type

This paper analyzes and values an American barrier option with continuous payment plan written on a dividend paying asset under the classical Black-Scholes model.The integral representation of the initial premium along with the delta hedge parameter for an American continuous-installment down-and-out call option are obtained by using the decomposition technique.This offers a system of nonlinear integral equations for determining the optimal exercise and stopping boundaries,which can be utilized to approximate the option price and delta hedge parameter.The implementation is based on discretizing the quadrature formula in the system of equations and using the Newton-Raphson method to compute the two optimal boundaries at each time points.Numerical results are provided to illustrate the computational accuracy and the effects on the initial premium and optimal boundaries with respect to barrier.     

Robust trading rule selection and forecasting accuracy

Trading rules performing well on a given data set seldom lead to promising out-of-sample results, a problem which is a consequence of the in-sample data snooping bias. Efforts to justify the selection of trading rules by assessing the out-of-sample performance will not really remedy this predica- ment either, because they are prone to be trapped in what is known as the out-of-sample data-snooping bias. Our approach to curb the data-snooping bias consists of constructing a framework for trading rule selection using a-priori robustness strategies, where robustness is gauged on the basis of time- series bootstrap and multi-objective criteria. This approach focuses thus on building robustness into the process of trading rule selection at an early stage, rather than on an ex-post assessment of trading rule fitness. Intra-day FX market data constitute the empirical basis of the proposed investigations. Trading rules are selected from a wide universe created by evolutionary computation tools. The authors show evidence of the benefit of this approach in terms of indirect forecasting accuracy when investing in FX markets.     

Learning-based force servoing control of a robot with vision in an unknown environment

A learning-based control approach is presented for force servoing of a robot with vision in an unknown environment. Firstly, mapping relationships between image features of the servoing object and the joint angles of the robot are derived and learned by a neural network. Secondly, a learning controller based on the neural network is designed for the robot to trace the object. Thirdly, a discrete time impedance control law is obtained for the force servoing of the robot, the on-line learning algorithms for three neural networks are developed to adjust the impedance parameters of the robot in the unknown environment. Lastly, wiping experiments are carried out by using a 6 DOF industrial robot with a CCD camera and a force/torque sensor in its end effector, and the experimental results confirm the effecti veness of the approach.