The anterior loop was highly prevalent in Chinese, and the length of the anterior loop was highly variable. Therefore, we recommend that drilling commences from a location approximately 5.5 mm mesially from the mental foramen, when installing implants in the most distal interforaminal area.

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Trajectory tracking and state estimation are significant in the motion planning and intelligent vehicle control. This article focuses on the model predictive control approach for the trajectory tracking of the intelligent vehicles and state estimation of the nonlinear vehicle system. The constraints of the system states are considered when applying the model predictive control method to the practical problem, while 4-degree-of-freedom vehicle model and unscented Kalman filter are proposed to estimate the vehicle states. The estimated states of the vehicle are used to provide model predictive control with real-time control and judge vehicle stability. Furthermore, in order to decrease the cost of solving the nonlinear optimization, the linear time-varying model predictive control is used at each time step. The effectiveness of the proposed vehicle state estimation and model predictive control method is tested by driving simulator. The results of simulations and experiments show that great and robust performance is achieved for trajectory tracking and state estimation in different scenarios.

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With the development of the global automotive industry, environmental pollution and driving safety have been major problems. This paper focuses on a novel integrated cooperative antilock braking system (ABS) controller, which is used for antilock regenerative braking systems (ARBS) and antilock mechanical braking systems (AMBS) for hybrid electric vehicles (HEV) driving on different road surfaces. An intelligent tire system is utilized to detect varying road surfaces to obtain friction information and optimal operation slip ratio. In addition, the HEV eight‐degree‐of‐freedom dynamics model is developed for ABS control, which includes the LuGre tire model. Adaptive backstepping and finite state machine controllers are explored to maintain optimal wheel slip and regenerate energy, based on wheel slip, battery state of charge (SOC), motor capability torque, and vehicle velocity, and to develop the switching rules of regenerative and mechanical braking systems. In order to evaluate the performance of regenerative and mechanical braking systems on harvesting energy and driving safety, the HEV and LuGre dynamic tire models are developed in MATLAB SIMULINK /SimDriveline. The estimation results of different road surfaces and slip controller effects are tested via the experiments and simulations, finding good braking performances and high regenerative efficiency in different maneuvers.

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