科创中国

当前各国公司治理都实施统一的董事会治理监管,然而公司经营环境复杂,最优董事会是否是内生性的争论成为近年来公司治理关注的焦点.本文以CEO寻租空间、监督成本等构建最优董事会抉择模型,研究发现:治理成本的存在,使得CEO寻租空间对董事会规模和独立董事比例有积极影响,而监督成本对二者有负面影响;从边际治理成本角度,最优董事会与其独立性存在一定程度的替代性.我国1999-2008年上市公司实证研究发现:一定监管条规下,最优规模与独立性的替代假说成立,鉴于公司经营差异性,僵化的监管制度将提升治理成本,造成董事会效率低下.由此,建议监管机构从治理执行效果出发,考虑最优董事会的相机抉择行为,采用柔性化的监管条规,提高公司治理绩效.

The identification of maximum road friction coefficient and optimal slip ratio is crucial to vehicle dynamics and control.However,it is always not easy to identify the maximum road friction coefficient with high robustness and good adaptability to various vehicle operating conditions.The existing investigations on robust identification of maximum road friction coefficient are unsatisfactory.In this paper,an identification approach based on road type recognition is proposed for the robust identification of maximum road friction coefficient and optimal slip ratio.The instantaneous road friction coefficient is estimated through the recursive least square with a forgetting factor method based on the single wheel model,and the estimated road friction coefficient and slip ratio are grouped in a set of samples in a small time interval before the current time,which are updated with time progressing.The current road type is recognized by comparing the samples of the estimated road friction coefficient with the standard road friction coefficient of each typical road,and the minimum statistical error is used as the recognition principle to improve identification robustness.Once the road type is recognized,the maximum road friction coefficient and optimal slip ratio are determined.The numerical simulation tests are conducted on two typical road friction conditions(single-friction and joint-friction)by using CarSim software.The test results show that there is little identification error between the identified maximum road friction coefficient and the pre-set value in CarSim.The proposed identification method has good robustness performance to external disturbances and good adaptability to various vehicle operating conditions and road variations,and the identification results can be used for the adjustment of vehicle active safety control strategies.

董事会定位深刻反映大学的管理理念、组织精神和价值追求.当前我国行业特色大学董事会在性质、功能、服务、规模、特色等方面的定位已经显示出与传统大学管理体制的继承性与异质性,但因现行教育管理体制的制约.仍存在法律地位不明、功能设定狭隘、忽略社会与行业需求,特色管理文化贫乏等问题.明确行业特色大学董事会定位的内容及其实现路径,必须从扩大大学办学自主权,维护董事会合理权力边界,合理确定董事成员规模及组成,拓展产学研合作领域,凝练大学管理文化等方面统筹考虑.

Dual mechanical port machine(DMPM), as a novel electromechanical energy conversion device, has attracted widespread attention. DMPM with spoke type permanent magnet arrangements(STPM-DMPM), which is one of several types of DMPM, has been of interest recently. The unique coupling characteristics of STPM-DMPM are beneficial to improving system performance, but these same characteristics increase the difficulties of control. Now there has been little research about the control of STPM-DMPM, and this has hindered its practical application. Based on a mathematical model of STPM-DMPM, the coupling characteristics and the merits and demerits of such devices are analyzed as applied to a hybrid system. The control strategies for improving the disadvantages and for utilizing the advantage of coupling are researched. In order to weaken the interaction effect of torque outputs in the inner motor and the outer motor that results from coupling in STPM-DMPM, a decoupling control method based on equivalent current control is proposed, and independent torque control for the inner motor and outer motor is achieved. In order to solve address the problem of adequately utilization of coupling, minimizing the overall copper loss of the inner motor and the outer motor of STPM-DMPM is taken as the optimization objective for optimal control, and the purpose of utilizing the coupling adequately and reasonably is achieved. The verification tests of the proposed decoupling control and optimal control strategies are carried out on a prototype STPM-DMPM, and the experimental results show that the interaction effect of torque outputs in the inner motor and the outer motor can be markedly weakened through use of the control method. The overall copper loss of the inner motor and the outer motor can be markedly reduced through use of the optimal control method, while the power output remains unchanged. A breakthrough in the control problem of STPM-DMPM is accomplished by combining the control methods. Good performance in the control of STPM-DMPM will enhance its practicality, particularly as applied to hybrid systems.

The existing research of the acceleration control mainly focuses on an optimization of the velocity trajectory with respect to a criterion formulation that weights acceleration time and fuel consumption. The minimum-fuel acceleration problem in conventional vehicle has been solved by Pontryagin's maximum principle and dynamic programming algorithm, respectively. The acceleration control with minimum energy consumption for battery electric vehicle(EV) has not been reported. In this paper, the permanent magnet synchronous motor(PMSM) is controlled by the field oriented control(FOC) method and the electric drive system for the EV(including the PMSM, the inverter and the battery) is modeled to favor over a detailed consumption map. The analytical algorithm is proposed to analyze the optimal acceleration control and the optimal torque versus speed curve in the acceleration process is obtained. Considering the acceleration time, a penalty function is introduced to realize a fast vehicle speed tracking. The optimal acceleration control is also addressed with dynamic programming(DP). This method can solve the optimal acceleration problem with precise time constraint, but it consumes a large amount of computation time. The EV used in simulation and experiment is a four-wheel hub motor drive electric vehicle. The simulation and experimental results show that the required battery energy has little difference between the acceleration control solved by analytical algorithm and that solved by DP, and is greatly reduced comparing with the constant pedal opening acceleration. The proposed analytical and DP algorithms can minimize the energy consumption in EV's acceleration process and the analytical algorithm is easy to be implemented in real-time control.