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Simplified diagram of automatic mechanical transmission (AMT) with the gearshift assistant mechanism concept. 1: Inner combustion engine; 2: Main clutch; 3: Gear pair connect the engine output shaft and the secondary shaft; 4: Anticipated gear pair; 5: Synchronizer; 6: Present gear pair; 7: Primary shaft (output shaft); 8: Complementary gears; 9: Torque complementary motor; 10: Epicyclic mechanism; 11: Synchronizing clutch; 12: Secondary shaft. As shown in Figure 2, the kinematics of AMT with the gearshift assistant mechanism could be described by two angular velocities, e and p , as:
Source publication
Automatic mechanical transmission (AMT) with a gearshift assistant mechanism is a novel transmission architect concept aiming to improve the torque interruption and driveline jerk of AMT. During the shifting process, the shifting performance deteriorates as the varying road gradient and the friction coefficient worsen the coupling effect between th...
Contexts in source publication
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... a synchronizing clutch will be engaged to synchronize the preshift gear and the transmission output shaft. The synchronizing clutch is a substitute for the synchronizers and the clutch of AMT in the shifting process (as shown in Figure 1). Yet, the structure of the gearshift assistant mechanism inevitably leads to mutual coupling between the motor torque and the clutch friction torque. ...
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... shown in Figure 1, the gearshift assistant mechanism consists of an electric motor, an epicyclic mechanism, and a synchronizing clutch. The motor directly transmits power to the output shaft through a pair of gears, namely the complementary gear. ...
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... As shown in Figure 10, when Thres = 0.001, the calculation finishes at 99 iterations, and the minimum value obtained by the simulation results is about 80.3; when Thres is reduced to 0.0001, the calculation finishes at 377 iterations, and the result is about 57.02. If Thres is set to 0, the simulation will continue, but after 1022 iterations, the result is about 56.79, and the simulation has not finished yet. ...
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... it can be inferred that if the Thres selected is too large, the simulation result is likely to fall into the local minimum; if Thres is selected too small, as the number of iterations continues to increase (taking the standard shown in Figure 4 as more than 300 iterations), the convergence rate of the function value does not change significantly. As shown in Figures 11 and 12, after changing the weight coefficients scl w and m w , the cost function value ( ) V x changes accordingly. In the current model, increasing m w will increase the value of the function because the two variables e scl and e m are calculated in scalar form, and the latter is numerically larger than the former. ...
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... the current model, increasing m w will increase the value of the function because the two variables e scl and e m are calculated in scalar form, and the latter is numerically larger than the former. The minimum values obtained by convergence in Figures 11 and 12 have increased in different degrees compared to that in Figure 10, and so does the number of iterations. In order to explore the effect of different initial points on the simulation results, seven random initial points were defined into the 7 × 6 matrix B: ...
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... the current model, increasing m w will increase the value of the function because the two variables e scl and e m are calculated in scalar form, and the latter is numerically larger than the former. The minimum values obtained by convergence in Figures 11 and 12 have increased in different degrees compared to that in Figure 10, and so does the number of iterations. In order to explore the effect of different initial points on the simulation results, seven random initial points were defined into the 7 × 6 matrix B: ...
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... meaning of matrix B is consistent with the meaning of matrix A in Formula (13). As shown in Figure 13, the simulation was aborted at 349 iterations and the function value converged to about 60.58. Compared with Figure 10, after 99 iterations, the function value of each initial point in Figure 13 is about 89, and the iteration is not terminated; the function value of each initial point in Figure 10 converges to 80.3 and satisfies Thres = 0.001. ...
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... shown in Figure 13, the simulation was aborted at 349 iterations and the function value converged to about 60.58. Compared with Figure 10, after 99 iterations, the function value of each initial point in Figure 13 is about 89, and the iteration is not terminated; the function value of each initial point in Figure 10 converges to 80.3 and satisfies Thres = 0.001. When the function value of each initial point in Figure 13 meets Thres = 0.001, the convergence result is close to the minimum value of 57.02, which is the minimum function value in Figure 10 when its initial points iterated to meet Thres = 0.0001. ...
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... shown in Figure 13, the simulation was aborted at 349 iterations and the function value converged to about 60.58. Compared with Figure 10, after 99 iterations, the function value of each initial point in Figure 13 is about 89, and the iteration is not terminated; the function value of each initial point in Figure 10 converges to 80.3 and satisfies Thres = 0.001. When the function value of each initial point in Figure 13 meets Thres = 0.001, the convergence result is close to the minimum value of 57.02, which is the minimum function value in Figure 10 when its initial points iterated to meet Thres = 0.0001. ...
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... shown in Figure 13, the simulation was aborted at 349 iterations and the function value converged to about 60.58. Compared with Figure 10, after 99 iterations, the function value of each initial point in Figure 13 is about 89, and the iteration is not terminated; the function value of each initial point in Figure 10 converges to 80.3 and satisfies Thres = 0.001. When the function value of each initial point in Figure 13 meets Thres = 0.001, the convergence result is close to the minimum value of 57.02, which is the minimum function value in Figure 10 when its initial points iterated to meet Thres = 0.0001. ...
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... with Figure 10, after 99 iterations, the function value of each initial point in Figure 13 is about 89, and the iteration is not terminated; the function value of each initial point in Figure 10 converges to 80.3 and satisfies Thres = 0.001. When the function value of each initial point in Figure 13 meets Thres = 0.001, the convergence result is close to the minimum value of 57.02, which is the minimum function value in Figure 10 when its initial points iterated to meet Thres = 0.0001. It can be deduced from this that the selection of the initial value points will affect the number of iterations and the accuracy of the result; when the function value change has a greater impact on the system output performance, the value of Thres should be reduced to obtain a more accurate result. ...
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... with Figure 10, after 99 iterations, the function value of each initial point in Figure 13 is about 89, and the iteration is not terminated; the function value of each initial point in Figure 10 converges to 80.3 and satisfies Thres = 0.001. When the function value of each initial point in Figure 13 meets Thres = 0.001, the convergence result is close to the minimum value of 57.02, which is the minimum function value in Figure 10 when its initial points iterated to meet Thres = 0.0001. It can be deduced from this that the selection of the initial value points will affect the number of iterations and the accuracy of the result; when the function value change has a greater impact on the system output performance, the value of Thres should be reduced to obtain a more accurate result. ...
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... reduce the amount of calculation, Thres can be directly set as 0.0001 in this case. As shown in Figure 14, when Thres is unchanged and the weight coefficient scl w is reduced to 0.5, the target tracking ability will be decreased, and as it decreases below 0.5, the target tracking ability will also decrease, but the trend is flat. In the case of applying the same weight coefficients to the initial points taken from matrix B, although more iterations are required to meet the same Thres condition, the results obtain a lower function value and better target tracking ability. ...
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... the case of applying the same weight coefficients to the initial points taken from matrix B, although more iterations are required to meet the same Thres condition, the results obtain a lower function value and better target tracking ability. As shown in Figure 15, the relationship between the target tracking ability of the output shaft angular acceleration p and Thres is similar to that of scl , but when Thres = 0.001, the fluctuation is large, which will deteriorate the riding comfort of the vehicle. Combining with Figure 14, when Thres = 0.0001, the algorithm can get better target tracking ability of two outputs through 377 iterations at the same time. ...
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... shown in Figure 15, the relationship between the target tracking ability of the output shaft angular acceleration p and Thres is similar to that of scl , but when Thres = 0.001, the fluctuation is large, which will deteriorate the riding comfort of the vehicle. Combining with Figure 14, when Thres = 0.0001, the algorithm can get better target tracking ability of two outputs through 377 iterations at the same time. As shown in Figure 15, when the other conditions remain unchanged, increasing the weight coefficient of the error norm of output p up to 0.5 will significantly improve the target tracking ability. ...
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... with Figure 14, when Thres = 0.0001, the algorithm can get better target tracking ability of two outputs through 377 iterations at the same time. As shown in Figure 15, when the other conditions remain unchanged, increasing the weight coefficient of the error norm of output p up to 0.5 will significantly improve the target tracking ability. However, when the weight coefficient is greater than 0.5, the performance will not be significantly improved. ...
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... when the weight coefficient is greater than 0.5, the performance will not be significantly improved. By changing the initial points, the value of the cost function obtained from matrix B is lower, and the improvement of the target tracking ability can be more obvious than in Figure 15. ...
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... surging toque interferes with the synchronization of the clutch pads. As shown in Figure 15, higher weighting parameters or lower termination thresholds are essential for preventing the output torque from overshooting. Hence, the weighting parameters are selected as =0.5 =0.5 scl m w w ， ...
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... Figures 16-18 show the shifting performance of AMT with the gearshift assistant mechanism in different road grades. It is shown that with the perturbing parameters and the interferences well estimated, the upshift of the proposed transmission can still be successful, and the torque filling effect is also acceptable. ...
Citations
... The orientation of a rigid body in a coordinate system can be characterized based on the three Euler angles, which can also be used to translate a point's coordinates from one reference frame to another and to explain the relationship between two reference frames [32][33][34][35]. The Euler angles, which show how a body rotates around the axes of a coordinate system, are represented by the symbols for the roll, pitch, and yaw angles. ...
Unmanned aerial vehicles have a wide range of uses in the military field, non-combat situations, and civil works. Due to their ease of operation, unmanned aerial vehicles (UAVs) are highly sought after by farmers and are considered the best agricultural technologies, since different types of controller algorithms are being integrated into drone systems, making drones the most affordable option for smart agriculture sectors. PID controllers are among the controllers frequently incorporated into drone systems. Although PID controllers are frequently used in drones, they have some limitations, such as sensitivity to noise and measurement errors, which can lead to instability or oscillations in the system. On the other hand, PID controllers provide improved accuracy in drone system responses. When using PID controllers to achieve the best performance in a drone system, it is better to share the advantages of PID controllers with other intelligence controllers. One promising option is the fuzzy PID controller. The aim of this study was to control quadcopter states (rolling, altitude, and airspeed) by leveraging quadcopter technology and adding hybrid fuzzy PID controls into the system. The quadcopter system and its controllers were mathematically modeled using the Simulink/MATLAB platform, and the system was controlled by fuzzy PID controllers. For validation purposes, the fuzzy PID controller was compared with a classically tuned PID controller. For roll, height, and airspeed, the fuzzy PID controller provided an improvement of 41.5%, 11%, and 44%, respectively, over the classically tuned PID controller. Therefore, the fuzzy PID controller best suits the needs of farmers and is compatible with smart agriculture systems.
... However, when manual transmission shifts, the power is interrupted, it is difficult to shift, and the driving performance of the vehicle depends on the driving level. At the same time, complex shift operations are easy to cause driver fatigue and affect road safety [3]. Therefore, automatic transmission technology has become an important research direction, and the rapid development of electronic technology and its wide application in vehicles also provide a good opportunity for the development of automatic transmission [4]. ...
Automatic transmission system is the core part of vehicle transmission processing, which can improve driving safety. In order to improve the shift effect of automotive automatic mechanical transmission and narrow the gap between the vehicle speed and the expected speed, an automotive automatic mechanical transmission system based on torsional damping was designed in the experiment. On the basis of hardware composed of different modules and fuzzy control algorithm, the system realizes the software design of vehicle automatic mechanical transmission system. The experimental results show that when the system is applied in practice, the gear selection time of the vehicle is between 0.2 s-0.3 s, and the gear shift time is between 0.3 s-0.4 s. The gap between the vehicle speed and the expected speed, and between the vehicle speed and the expected speed is small. The practical application effect is good.
Many processes operated in chemical process industries show time-varying and highly nonlinear characteristics. This paper proposes an enhanced nonlinear PID (NPID) controller for the improvement of setpoint tracking or disturbance rejection responses and new tuning formulas for a FOPTD process model. The NPID controller has a structure with a first-order filter in the derivative term to avoid possible Derivative Kick. The parameters of the NPID controller are expressed in terms of the ratio L/τ of the time delay L to the time constant τ in the process by using the dimensionless approach. Repeated optimizations are performed for each value over the ranges of 0.01 to 1 and 1 to 3 of L/τ and over the ranges of 5 to 30 of the filter parameter N to obtain the average of optimal parameter values that minimize the integral of absolute error performance criterion. By using the least-squares method with together the calculated optimal values and the rule formulas, the tuning rules are obtained. A set of simulation works on the five processes are carried out to demonstrate tracking and disturbance performance and robustness against the noise of this approach.
Multispeed transmissions can enhance the dynamics and economic performance of electric vehicles (EVs), but the coordinated control of the drive motor and gear shift mechanism during gear shifting is still a difficult challenge because gear shifting may cause discomfort to the occupants. To improve the swiftness of gear shifting, this paper proposes a coordinated shift control method based on the dynamic tooth alignment (DTA) algorithm for nonsynchronizer automated mechanical transmissions (NSAMTs) of EVs. After the speed difference between the sleeve (SL) and target dog gear is reduced to a certain value by speed synchronization, angle synchronization is adopted to synchronize the SL quickly to the target tooth slot’s angular position predicted by the DTA. A two-speed planetary NSAMT is taken as an example to carry out comparative simulations and bench experiments. Results show that gear shifting duration and maximum jerk are reduced under the shift control with the proposed method, which proves the effectiveness of the proposed coordinated shift control method with DTA.
