IT治理的主要内控模型比较研究

随着信息化程度的不断提高和信息化技术的发展,信息技术已经成为推动社会发展的重要基础性资源。信息系统广泛深入地渗透到社会的各个领域,并成为政治、经济、军事、文化乃至社会一切领域的基础。那么对IT治理也就提到了人们的思考日程上。IT治理的研究自从提出就受到了国内外众多学者的关注,而且随着信息技术的发展,企业的IT投入越来越多,对IT的治理的关注程度越来越高。目前,IT治理的主要模型包括ITIL、ISO/IEC17799/27002、Prince2、COBIT。每个模型都有各自的优势,适用于不同的领域。

关于以“风险内控量化评价模型”为基础的内部控制评价方法的研究——基于抽样审计与变权理论

本文结合抽样审计和变权理论的相关思想,设计了一套可以对企业风险控制水平进行总体量化评价的方法。并且就如何将该方法与信息系统进行整合进行了研究。

论准备海外上市公司的内部控制建设

本文主要探讨准备在海外上市的中国企业要加入到国际市场的竞争中,应该如何按照国际上通行的内部控制标准来建立内部控制体系。以COSO报告作为内控标准来分析我国现阶段企业内控管理的基本状况,并进一步就国内企业应如何按照国际上通行的内部控制标准来建立完善而有效的内部控制体系提出了一些应对措施和建议。

施工单位财务内部控制研究

财务内部控制是为了保证单位财务目标的实现,是单位内部控制的核心。我国施工单位以项目管理为核心的经营管理体制已基本形成。受投资规模大、生产周期长、影响因素多、竞争异常激烈等行业特点所限,财务风险贯穿于施工单位工程项目施工全过程。因此,施工单位财务风险管理更为复杂。本文以内部控制、COSO基本框架、系统工程等作为理论基础,系统地分析了施工单位的财务风险点,提出了"以项目成本控制为核心,以项目全过程管控为主线,以全面预算控制为抓手"的施工单位财务内部控制基本框架结构。

BIO-INSPIRED SELF-ADAPTIVE MANUFACTURING SYSTEM CONTROL ARCHITECTURE

Future manufacturing systems need to cope with frequent changes and disturbances, therefore their control architectures require constant adaptability, agility, stability, self-organization, intelligence, and robustness. Bio-inspired manufacturing system can well satisfy these requirements. For this purpose, by referencing the biological organization structure and the mechanism, a bio-inspired manufacturing cell is presented from a novel view, and then a bio-inspired self-adaptive manufacturing model is established based on the ultra-short feedback mechanism of the neuro-endocrine system. A hio-inspired self-adaptive manufacturing system coordinated model is also established based on the neuro-endocrine-immunity system （NEIS）. Finally, an example based on pheromone communication mechanism indicates that the robustness of the whole manufacturing system is improved by bio-inspired technologies.

An Identification Method Based on the Improved NLJ Algorithm and Its Application

The accurate model is the most important and basic condition for the application of advanced process control, but the conventional methods do not provide satisfactory results in the case of unstable processes. To effec-tively control these processes, a novel identification method (Model Parameters and Initial States Identification si-multaneously in closed loop —MPISI) is proposed. The model parameters and initial states of state equation can be simultaneously identified using this method. The results of simulation and application show that this method has the advantageous of disturbance-rejection and robustness. This method proposes a novel way for the optimization and the advanced control of the process systems.

The IMC Structure of Multi-rate Multivariable Predictive Control Systems and An Improved Algorithm

Multirate multivariable predictive control system with the sampling mechanism that adjusts the plant inputs only once but detects the plant outputs several times during a period is examined. The IMC structure of the system is derived, and its robust stability and zero steady state error characteristics are analyzed. A new control algorithm is developed by adding the variation of the outputs to the index performance. The simulation results show that the method is effective and has zeros steady-state error.

Performance enhancement of IMC-PID controller design for stable and unstable second-order time delay processes

An IMC-PID controller was proposed for unstable second-order time delay system which shows the characteristics of inverse response(RHP zero). A plot of Ms versus λ was suggested to calculate the suitable tuning parameter λ, which provides a trade-off between performance and robustness. Six different forms of process models were selected from literature to show the applicability of the present method. Performance of controller was calculated by ITAE and total variation TV and compared with recently published tuning rules. Undesirable overshoot was removed by using a set-point weighting parameter. Robustness was tested by introducing a perturbation into the various model parameters and closed-loop results show that the designed controller is robust in the case of model uncertainty. The proposed method shows an overall better closed-loop response as compared to other recently reported methods.

Hybrid internal model control and proportional control of chaotic dynamical systems

A new chaos control method is proposed to take advantage of chaos or avoid it. The hybrid Internal Model Control and Proportional Control learning scheme are introduced. In order to gain the desired robust performance and ensure the system's stability, Adaptive Momentum Algorithms are also developed. Through properly designing the neural network plant model and neural network controller, the chaotic dynamical systems are controlled while the parameters of the BP neural network are modified. Taking the Lorenz chaotic system as example, the results show that chaotic dynamical systems can be stabilized at the desired orbits by this control strategy.

Robust Control of Time Delay Systems

This study presents the design of both H∞ Loop Shaping Control （HLSC） and Internal Model Control （IMC） strategies for linear time delay systems. For first order time delay system, a systematic approach for weight selection based on the sensitivity function was proposed, then compared to the internal model control strategy. For both methods, the synthesis was based on the Pade approximation. Two cases are considered for time delay： upper or lower than system time constant. Simulation results for the proposed approaches are acceptable ever in presence of disturbances and model mismatches.

Dynamic Optimization of a 2-DOF Pseudo-Equatorial Tracking System

The key word for the design process of the photovoltaic tracking systems is the energetic efficiency. Using the tracking system, the photovoltaic panel follows the sun and increases the collected energy, but the driving motors consume a part of this energy. In these terms, the optimization of the tracking systems became an important challenge in the modem research and technology. In this paper, a strategy for the dynamic optimization of the photovoltaic tracking systems is presented. The main task in optimization is to maximize the energetic gain by increasing the incoming solar radiation and minimizing the energy consumption for tracking. This strategy is possible by developing the virtual prototype of the tracking system, which is a control loop composed by the multi-body mechanical model connected with the dynamic model of the actuators and with the controller model. In this way, it is possible to optimize the tracking mechanism, choose the appropriate actuators, and design the optimal controller.

A modified structure internal model robust control method for the integration of active front steering and direct yaw moment control

Taking into account the nonlinearity of vehicle dynamics and the variations of vehicle parameters,the integrated control strategy for active front steering(AFS)and direct yaw control(DYC)that can maintain the performance and robustness is a key issue to be researched.Currently,the H∞method is widely applied to the integrated control of chassis dynamics,but it always sacrifices the performance in order to enhance the stability.The modified structure internal model robust control(MSIMC)obtained by modifying internal model control(IMC)structure is proposed for the integrated control of AFS and DYC to surmount the conflict between performance and robustness.Double lane change(DLC)simulation is developed to compare the performance and the stability of the MSIMC strategy,the PID controller based on the reference vehicle model and the H∞controller.Simulation results show that the PID controller may oscillate and go into instability in severe driving conditions because of large variations of tire parameters,the H∞controller sacrifices the performance in order to enhance the stability,and only the MSIMC controller can both ensure the robustness and the high performance of the integrated control of AFS and DYC.

Steering control strategy guide by two preview vision cues

Vision cues play an important role in states feedback in motion control.However,the existing driver steering models consider little about vision cues utilized by human drivers during their steering procedure.This paper presents a novel steering control strategy based on two preview points(far point and near point).The far point is used to compensate the steering wheel by predicting the upcoming curvature change with respect to the lane,while the near point as vision feedback,which is used to tune the steering wheel by estimating the errors of vehicle states and lane center.To obtain much smoother lateral acceleration during steering,a forward internal model is established using a second-order yaw dynamics system that captures the influence of yaw angular acceleration caused by steering wheel angle.The input parameter of the second-order system is the vision cues of both the near and far points,and the output parameters are the ideal yaw angle and yaw rate.To calculate suitable the steering wheel angle,an adaptive controller is designed using fuzzy sliding technology,which is used as the input of the vehicle system dynamics.Numerical simulation results show that the proposed method performs better than the existing driver steering models in case of imitating human drivers' behavior,and exhibits excellent adaption to the lane curvature change.