Disturbance Observer Based Control for Four Wheel Steering Vehicles With Model Reference

This paper presents a disturbance observer based control strategy for four wheel steering systems in order to improve vehicle handling stability. By combination of feedforward control and feedback control, the front and rear wheel steering angles are controlled simultaneously to follow both the desired sideslip angle and the yaw rate of the reference vehicle model.A nonlinear three degree-of-freedom four wheel steering vehicle model containing lateral, yaw and roll motions is built up, which also takes the dynamic effects of crosswind into consideration.The disturbance observer based control method is provided to cope with ignored nonlinear dynamics and to handle exogenous disturbances. Finally, a simulation experiment is carried out,which shows that the proposed four wheel steering vehicle can guarantee handling stability and present strong robustness against external disturbances.

Reduced Model Based Control of Two Link Flexible Space Robot

Model based control schemes use the inverse dynamics of the robot arm to produce the main torque component necessary for trajectory tracking. For model-based controller one is required to know the model parameters accurately. This is a very difficult task especially if the manipulator is flexible. So a reduced model based controller has been developed, which requires only the information of space robot base velocity and link parameters. The flexible link is modeled as Euler Bernoulli beam. To simplify the analysis we have considered Jacobian of rigid manipulator. Bond graph modeling is used to model the dynamics of the system and to devise the control strategy. The scheme has been verified using simulation for two links flexible space manipulator.

Feedback Scheduling of Model-based Networked Control Systems with Flexible Workload

In this paper,a novel control structure called feedback scheduling of model-based networked control systems is proposed to cope with a flexible network load and resource constraints.The state update time is adjusted according to the real-time network congestion situation.State observer is used under the situation where the state of the controlled plant could not be acquired.The stability criterion of the proposed structure is proved with time-varying state update time.On the basis of the stability of the novel system structure,the compromise between the control performance and the network utilization is realized by using feedback scheduler. Examples are provided to show the advantage of the proposed control structure.

On the model-based networked control for singularly perturbed systems

In this paper, the control of a two-time-scale plant, where the sensor is connected to a linear controller/ actuator via a network is addressed. The slow and fast systems of singularly perturbed systems are used to produce an estimate of the plant state behavior between transmission times, by which one can reduce the usage of the network. The approximate solutions of the whole systems are derived and it is shown that the whole systems via the network control are generally asymptotically stable as long as their slow and fast systems are both stable. These results are also extended to the case of network delay.

The Research of Role Tree-Based Access Control Model

Towards the crossing and coupling permissions in tasks existed widely in many fields and considering the design of role view must rely on the activities of the tasks process,based on Role Based Accessing Control (RBAC) model,this paper put forward a Role Tree-Based Access Control (RTBAC) model. In addition,the model definition and its constraint formal description is also discussed in this paper. RTBAC model is able to realize the dynamic organizing,self-determination and convenience of the design of role view,and guarantee the least role permission when task separating in the mean time.

Model-based predictive controller design for a class of nonlinear networked systems with communication delays and data loss

This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation re- suits demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

A New Approach to Intelligent Model Based Predictive Control Scheme

This paper describes a new approach to intelligent model based predictive control scheme for deriving a complex system. In the control scheme presented, the main problem of the linear model based predictive control theory in dealing with severe nonlinear and time variant systems is thoroughly solved. In fact, this theory could appropriately be improved to a perfect approach for handling all complex systems, provided that they are firstly taken into consideration in line with the outcomes presented. This control scheme is organized based on a multi-fuzzy-based predictive control approach as well as a multi-fuzzy-based predictive model approach, while an intelligent decision mechanism system (IDMS) is used to identify the best fuzzy-based predictive model approach and the corresponding fuzzy-based predictive control approach, at each instant of time. In order to demonstrate the validity of the proposed control scheme, the single linear model based generalized predictive control scheme is used as a benchmark approach. At last, the appropriate tracking performance of the proposed control scheme is easily outperformed in comparison with previous one.

Model-Based Split-Range Algorithm for the Temperature Control of a Batch Reactor

In the manufacturing processes of high value-added products in the pharmaceutical, fine chemical polymer and food industry, insufficient control might produce off-grade products. This can cause significant financial losses, or in the pharmaceutical industry, it can result in an unusable batch. In these industries, batch reactors are commonly used, the control of which is essentially a problem of temperature control. In the industry, an increasing number of heating-cooling systems utilising three different temperature levels can be found, which are advantageous from an economic point of view. However, it makes the control more complicated. This paper presents a split-range designing technique using the model of the controlled system with the aim to design a split-range algorithm more specific to the actual sys- tem. The algorithm described provides high control performance when using it with classical PID-based cascade temperature control of jacketed batch reactors;however, it can be used with or as part of other types of controllers, for ex- ample, model-based temperature controllers. The algorithm can be used in the case of systems where only two as well as where three temperature levels are used for temperature control. Besides the switching between the modes of opera- tion and calculating the value of the manipulated variable, one of the most important functions of the split-range algo- rithm is to keep the sign of the gain of the controlled system unchanged. However, with a more system-specific split-range solution, not only can the sign of the gain be kept unchanged, but the gain can also be constant or less de- pendent on the state of the system. Using this solution, the design of the PID controller becomes simpler and can be implemented in existing systems without serious changes.

Application of control volume based finite element method for solving the black-oil fluid equations

This is the second paper of a series where we introduce a control volume based finite element method （CVFEM） to simulate multiphase flow in porous media. This is a fully conservative method able to deal with unstructured grids which can be used for representing any complexity of reservoir geometry and its geological objects in an accurate and efficient manner. In order to deal with the inherent heterogeneity of the reservoirs, all operations related to discretization are performed at the element level in a manner similar to classical finite element method （FEM）. Moreover, the proposed method can effectively reduce the so-called grid orientation effects. In the first paper of this series, we presented this method and its application for incompressible and immiscible two-phase flow simulation in homogeneous and heterogeneous porous media. In this paper, we evaluate the capability of the method in the solution of highly nonlinear and coupled partial differential equations by simulating hydrocarbon reservoirs using the black-oil model. Furthermore, the effect of grid orientation is investigated by simulating a benchmark waterflooding problem. The numerical results show that the formulation presented here is efficient and accurate for solving the bubble point and three-phase coning problems.

Surrogate-based modeling and dimension reduction techniques for multi-scale mechanics problems

Successful modeling and/or design of engineering systems often requires one to address the impact of multiple ＂design variables＂ on the prescribed outcome.There are often multiple,competing objectives based on which we assess the outcome of optimization.Since accurate,high fidelity models are typically time consuming and computationally expensive,comprehensive evaluations can be conducted only if an efficient framework is available.Furthermore,informed decisions of the model/hardware＇s overall performance rely on an adequate understanding of the global,not local,sensitivity of the individual design variables on the objectives.The surrogate-based approach,which involves approximating the objectives as continuous functions of design variables from limited data,offers a rational framework to reduce the number of important input variables,i.e.,the dimension of a design or modeling space.In this paper,we review the fundamental issues that arise in surrogate-based analysis and optimization,highlighting concepts,methods,techniques,as well as modeling implications for mechanics problems.To aid the discussions of the issues involved,we summarize recent efforts in investigating cryogenic cavitating flows,active flow control based on dielectric barrier discharge concepts,and lithium（Li）-ion batteries.It is also stressed that many multi-scale mechanics problems can naturally benefit from the surrogate approach for ＂scale bridging.＂

Optimal Preview Control for Automatic Carrier Landing System of Carrier-Based Aircraft with Air Wake

Carrier-based aircraft carrier landing is a special kind of tracking control problem and not suitable for classical control methods,which may miss the desired performance or result in overdesign.Therefore,we present an optimal preview control for automatic carrier landing system(ACLS)by using state information of system,as well as future reference information,which can avoid the shortcomings of classical control methods.Since the flight performance of carrier-based aircraft is disturbed by air wake when the aircraft flies near the area of carrier stern,we design a disturbance rejection strategy to ensure that aircraft track the glide path with high precision and robustness.Further,carrier-based aircraft is a complex nonlinear system.However,the nonlinear model of carrier-based aircraft can be linearized at equilibrium landing state and decoupled into the longitudinal model and the lateral model.Therefore,an optimal preview control system is designed.The simulation results of a carrier-based aircraft show that the optimal preview control system can effectively suppress air wake.Tracking accuracy of optimal preview controller is higher than that of the proportional integral differential(PID)control system.

Validation methodology for distribution-based degradation model

Distribution-based degradation models （or graphical approach in some literature） occur in a wide range of applications. However, few of existing methods have taken the validation of the built model into consideration. A validation methodology for distribution-based models is proposed in this paper. Since the model can be expressed as consisting of assumptions of model structures and embedded model parameters, the proposed methodology carries out the validation from these two aspects. By using appropriate statistical techniques, the rationality of degradation distributions, suitability of fitted models and validity of degradation models are validated respectively. A new statistical technique based on control limits is also proposed, which can be implemented in the validation of degradation models＇ validity. The case study on degradation modeling of an actual accelerometer shows that the proposed methodology is an effective solution to the validation problem of distribution-based de qradation models.

Architecture Design for Internet-Based Control Systems

The Internet is playing an important role in information retrieval, and additionally industrial process manipulation. This paper describes an approach to writing requirements specifications for Internet-based control systems, from which architectures can be derived. The requirements specifications developed are described in terms of a functional model, which is then extended to form an information architecture. Distinct from the functional model, the information architecture provides an indication as to the architectural structure of subsequently developed Internet-based control systems. Three general control structures are generated from the analysis of an information architecture. An integrated-distributed architecture is derived as an ideal implementation, in which a control system is linked to the Internet at all levels of a control system hierarchy.

Monte-Carlo based cascade control approach with focus on real overactuated space systems

The present research relies on a cascade control approach through the Monte-Carlo based method in the presence of uncertainties to evaluate the performance of the real overactuated space systems.A number of potential investigations in this area are first considered to prepare an idea with respect to state-of-the-art.The insight proposed here is organized to present attitude cascade control approach including the low thrust in connection with the high thrust to be implemented,while the aforementioned Monte-Carlo based method is carried out to guarantee the approach performance.It is noted that the investigated outcomes are efficient to handle a class of space systems presented via the center of mass and the moments of inertial.And also a number of profiles for the thrust vector and the misalignments as the disturbances all vary in its span of nominal variations.The acquired results are finally analyzed in line with some well-known benchmarks to verify the approach efficiency.The key core of finding in the research is to propose a novel 3-axis control approach to deal with all the mentioned uncertainties of space systems under control,in a synchronous manner,as long as the appropriate models in the low-high thrusts are realized.

Train crossing modeling based on measurements and best numerical approximation in subway tunnels

Communication based train control systems （CBTC） must work even in the worst situation-- train crossing. This paper models the propagation characteristics in one of the most common and piv- otal scenarios--train crossing in subway tunnels which is rarely mentioned in previous publications. Firstly, measurements for train crossing scenario at 2.4 GHz in a real subway line in Madrid have been made. The field measurement is the most reliable way to reveal the propagation characteristics involving shadowing effect and fast fading. Moreover, to precisely describe the fast fading distribu- tion and eliminate the inevitable weak points of traditional fitting way, a best numerical approxima- tion method using Legendre orthogonal polynomials has been proposed. Comparisons show that this method works better and is of greater physical significance. Finally, a complete statistical model is given and all the coefficients can be applied by system designers for the link and system level simu- lations.

Observer-Based Nonlinear Feedback Controls for Heartbeat ECG Tracking Systems

The analysis and design of observed-based nonlinear control of a heartbeat tracking system is investigated in this paper. Two of Zeeman’s heartbeat models are investigated and modified by adding the control input as a pacemaker, thereby creating the control-affine nonlinear system models that capture the general heartbeat behavior of the human heart. The control objective is to force the output of the heartbeat models to track and generate a synthetic electrocardiogram (ECG) signal based on the actual patient reference data, obtained from the William Beaumont Hospitals, Michigan, and the PhysioNet database. The formulations of the proposed heartbeat tracking control systems consist of two phases: analysis and synthesis. In the analysis phase, nonlinear controls based on input-output feedback linearization are considered. This approach simplifies the difficult task of developing nonlinear controls. In the synthesis phase, observer-based controls are employed, where the unmeasured state variables are estimated for practical implementations. These observer-based nonlinear feedback control schemes may be used as a control strategy in electronic pacemakers. In addition, they could be used in a software-based approach to generate a synthetic ECG signal to assess the effectiveness of diagnostic ECG signal processing devices.

Joint optimization of sampling interval and control for condition-based maintenance using availability maximization criterion

Most of the maintenance optimization models in condition-based maintenance(CBM) consider the cost-optimal criterion, but few papers have dealt with availability maximization for maintenance applications. A novel optimal Bayesian control approach is presented for maintenance decision making. The system deterioration evolves as a three-state continuous time hidden semi-Markov process. Considering the optimal maintenance policy, the multivariate Bayesian control scheme based on the hidden semi-Markov model(HSMM) is developed, the objective is to maximize the long-run expected average availability per unit time. The proposed approach can optimize the sampling interval and control limit jointly. A case study using Markov chain Monte Carlo(MCMC)simulation is provided and a comparison with the Bayesian control scheme based on hidden Markov model(HMM), the age-based replacement policy, Hotelling’s T2, multivariate exponentially weihted moving average(MEWMA) and multivariate cumulative sum(MCUSUM) control charts is given, which illustrates the effectiveness of the proposed method.

Experience-Based Access Control in UbiComp: A New Paradigm

Experience is a sociological concept and builds over time. In a broader sense, the human-centered equivalents of experience and trust apply to D2D interaction. Ubiquitous computing (UbiComp) embeds intelligence and computing capabilities in everyday objects to make them effectively communicate, share resources, and perform useful tasks. The safety of resources is a serious problem. As a result, authorization and access control in UbiComp is a significant challenge. Our work presents experience as an outcome of history (HI), reliability (RL), transitivity (TR), and Ubiquity (UB). This experience model is easily adaptable to a variety of self-regulating context-aware access control systems. This paper proposes a framework for Experience-Based Access Control (EX-BAC) with all major services provided by the model. EX-BAC extends attribute-based access control. It uses logical device type and experience as context parameters for policy design. When compared with the state-of-the-art, EX-BAC is efficient with respect to response time.

A Pragmatic Approach for Assessing the Economic Performance of Model Predictive Control Systems and Its Industrial Application

In this article,an approach for economic performance assessment of model predictive control(MPC) system is presented.The method builds on steady-state economic optimization techniques and uses the linear quadratic Gaussian(LQG) benchmark other than conventional minimum variance control(MVC) to estimate the potential of reduction in variance.The LQG control is a more practical performance benchmark compared to MVC for performance assessment since it considers input variance and output variance,and it thus provides a desired basis for determining the theoretical maximum economic benefit potential arising from variability reduction.Combining the LQG benchmark directly with benefit potential of MPC control system,both the economic benefit and the optimal operation condition can be obtained by solving the economic optimization problem.The proposed algorithm is illustrated by simulated example as well as application to economic performance assessment of an industrial model predictive control system.