http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Bingzhao Gao (검색결과 5 건)
- 무료
- 기관 내 무료
- 유료
-
Clutch Slip Control of Automatic Transmission Using Backstepping Technique
Bingzhao Gao,Hong Chen,Kazushi Sanada 제어로봇시스템학회 2009 제어로봇시스템학회 국제학술대회 논문집 Vol.2009 No.8
To improve the shift quality of vehicle with clutch-to-clutch gear shifts, a nonlinear controller is designed for the clutch slip control during the inertia phase. Backstepping technique is used to deduce the control algorithm. Model uncertainties including steady state error sand unmodelled dynamics are also considered as disturbance inputs and the controller is designed such that the error dynamics is input-to-statestable(ISS). Finally, the designed controller is discretized and tested on a complete power train simulation model.
-
POWER LOSS EVALUATION OF AUTOMATED MANUAL TRANSMISSION WITH GEARSHIFT ASSISTANT MECHANISM
Zhiqiang Sun,Bingzhao Gao,Jiaqi Jin,Kazushi Sanada 한국자동차공학회 2021 International journal of automotive technology Vol.22 No.2
This paper deals with the power loss evaluation in gearshift assistant mechanism applied in automated manual transmission. The originalities include building the numerical models of gearshift assistant mechanism efficiency analysis for cost penalty evaluation and designing gear ratio matching algorithm to match the gear ratios of epicyclic mechanism with desired ones, which is expected to minimize the clutch friction losses by reducing the angular velocity differences between two clutch pads. In the numerical models, gear meshing losses and friction losses are considered the main power sink. By the kinematic relationships and power flow analysis, a set of generalized efficiency calculating formulas of simple epicyclic mechanism in six working modes with respect to teeth numbers are summarized, which are useful computer-aided tools in calculating multiple simple epicyclic mechanisms’ efficiency. The matched gear ratios and the efficiency models are tested on a powertrain simulation model and several simulation results are reported. Finally, this paper summarized the merits and further research targets of the proposed transmission, which is a promising structure to achieve swift and smooth gearshift with low power losses.
-
Zhiqiang Sun,Bingzhao Gao,Jiaqi Jin,Kazushi Sanada 한국자동차공학회 2019 International journal of automotive technology Vol.20 No.5
To eliminate or reduce torque interruption and driveline jerk for traditional automated manual transmission (AMT), AMT with a gearshift assistant mechanism (GAM) is originally proposed in this paper. The GAM consists of a torque complementary motor and an epicyclic mechanism with a synchronizing clutch. During gear upshift, the electrical motor provides complementary torque to primary (output) shaft after synchronizer discharges, then the synchronizing clutch will work to synchronize primary shaft with anticipated gear. The lockup of the synchronizing clutch will ensure the synchronization of primary shaft and anticipated gear. After finishing synchronizing, synchronizer will lock up the anticipated gear and engine recovers torque supply to finish gearshift. Based on the mathematical model of the proposed transmission, its detailed structure, kinematic character and dynamic behavior are discussed. Controllers are designed to achieve presumed gearshift performance, and simulation results show its effectiveness. Problems may be encountered in engineering application and possible application on electrical vehicle (EV) of the proposed transmission are also discussed. Finally, this paper summarized the merits and further research targets of the proposed transmission, which is a promising structure to achieve swift and smooth gearshift.
-
Jinlong Hong,Liang Lu,Bingzhao Gao,Lin Zhang,Hong Chen 한국자동차공학회 2021 International journal of automotive technology Vol.22 No.2
For the torque decoupled hybrid electric vehicles (HEVs) equipped with automated manual transmission (AMT), the motor torque can be used to compensate for traction loss during gear shift. Thus, engine speed regulation can be used to achieve speed synchronization, and the clutch can be removed, which simplifies the overall structure of AMT. A triple-step nonlinear method is proposed in this paper to improve the engine speed tracking accuracy, which utilizes the steady-state, reference trajectory dynamics, and the tracking error information comprehensively. By the expense of the intake manifold pressure tracking performance, the developed controller can guarantee the asymptotic convergence of the engine speed tracking error. After the triple-step controller is deduced, it is verified through co-simulation between AMT HEV plant model in AMESim and controller model in Simulink. The simulation results demonstrate that the proposed controller can exert the advantages of the two control inputs: throttle opening α and spark advance deviation Δθ, and certificate the overall gear shift quality, including shift time and vehicle jerk.
-
Yuxiang Zhang,Xiaoling Liang,Shuzhi Sam Ge,Bingzhao Gao,Tong Heng Lee 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
Guaranteed safety and performance under various circumstances remain technically critical and practically challenging for the wide deployment of autonomous vehicles. For such safety-critical systems, it will certainly be a requirement that safe performance should be ensured even during the reinforcement learning period in the presence of system uncertainty. To address this issue, a Barrier Lyapunov Function-based safe reinforcement learning algorithm (BLF- SRL) is proposed here for the formulated nonlinear system in strict-feedback form. This approach appropriately arranges the Barrier Lyapunov Function item into the optimized backstepping control method to constrain the state-variables in the designed safety region during learning when unknown bounded system uncertainty exists. More specifically, the overall system control is optimized with the optimized backstepping technique under the framework of Actor-Critic, which optimizes the virtual control in every backstepping subsystem. Wherein, the optimal virtual control is decomposed into Barrier Lyapunov Function items; and also with an adaptive item to be learned with deep neural networks, which achieves safe exploration during the learning process. Eventually, the principle of Bellman optimality is satisfied through iteratively updating the independently approximated actor and critic to solve the Hamilton-Jacobi-Bellman equation in adaptive dynamic programming. More notably, the variance of control performance under uncertainty is also reduced with the proposed method. The effectiveness of the proposed method is verified with motion control problems for autonomous vehicles through appropriate comparison simulations.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
