Shengxiong Sun

Bio: Shengxiong Sun is an academic researcher from Beijing Institute of Technology. The author has contributed to research in topics: Manual transmission & Torque. The author has an hindex of 3, co-authored 4 publications receiving 17 citations.

Accelerated Adaptive Second Order Super-Twisting Sliding Mode Observer

Abstract: An accelerated second-order super-twisting sliding mode observer with an adaptive gain is proposed for a typical nonlinear system. The key contribution of this algorithm is that the rate of convergence of observation error is accelerated remarkably by introducing “system damping.” Chattering issue is attenuated with a satisfactory performance compared with conventional sliding mode observer. Furthermore, adaptive gain can vary with deviation between the trajectory and the sliding mode switching manifold dynamically so that overshoot can be reduced. The novel observer is proven mathematically to be convergent in a finite time. Finally, an example of nonlinear system is given to verify the performance.

Off-Line Optimization Based Active Control of Torsional Oscillation for Electric Vehicle Drivetrain

Abstract: As there is no clutch or hydraulic torque converter in electric vehicles to buffer and absorb torsional vibrations. Oscillation will occur in electric vehicle drivetrains when drivers tip in/out or are shifting. In order to improve vehicle response to transients, reduce vehicle jerk and reduce wear of drivetrain parts, torque step changes should be avoided. This article mainly focuses on drivetrain oscillations caused by torque interruption for shifting in a Motor-Transmission Integrated System. It takes advantage of the motor responsiveness, an optimal active control method is presented to reduce oscillations by adjusting motor torque output dynamically. A rear-wheel-drive electric vehicle with a two gear automated manual transmission is considered to set up dynamic differential equations based on Newton’s law of motion. By linearization of the affine system, a joint genetic algorithm and linear quadratic regulator method is applied to calculate the real optimal motor torque. In order to improve immediacy of the control system, time consuming optimization process of parameters is completed off-line. The active control system is tested in AMEsim® and limitation of motor external characteristics are considered. The results demonstrate that, compared with the open-loop system, the proposed algorithm can reduce motion oscillation to a satisfied extent when unloading torque for shifting.

Intelligent estimation for electric vehicle mass with unknown uncertainties based on particle filter

Abstract: Vehicle mass is one of the most critical parameters in the vehicle control system, based on the discrete vehicle longitudinal dynamic equation after the forward Euler approximation, non-linear particle filter is introduced to estimate the vehicle mass intelligently, and it gains a competitive advantage that statistical characteristics of noise and uncertainties in the system are not necessary to be known or supposed in advance. As a sort of recursive, Bayesian state estimator, vehicle mass is regarded as a constant state variable to constitute the discrete state-space equation, motor torque is selected as input signal, and the measurable vehicle speed is selected to constitute the observation equation, parameters such as rolling resistance coefficient, air drag coefficient and road slop are considered as high-power noise and uncertainties. The performance of the proposed vehicle mass estimator is tested by several groups of load and the results demonstrate that the output of the particle filter based vehicle mass estimator can converge to the real value and keep steady.

Accelerated adaptive super twisting sliding mode observer-based drive shaft torque estimation for electric vehicle with automated manual transmission

Abstract: The suddenly released torque that accumulated in the elastic drive shaft will bring torsional vibration and jerking feel at the shifting moment. A novel sliding mode observer is proposed to estimate the torque in drive shaft for a motor-transmission integrated powertrain system. Non-linear external characteristics of a driving motor and non-linear drag torque are considered in the electric powertrain system. In order to attenuate the chatting problem, the second-order super twisting sliding mode algorithm with an adaptive gain is adopted. Furthermore, a term `system damping' is introduced to accelerate the estimation error convergence. The proposed estimation algorithm is tested on test rig for typical operating conditions. The results show that the torque in drive shaft can be estimated satisfactorily and the tracking error converges to 0 in a short time.

Sliding Mode Observer with Adaptive Parameter Estimation for Sensorless Control of IPMSM

Abstract: To improve the observation accuracy and robustness of the sensorless control of an interior permanent magnet synchronous motor (IPMSM), a sliding mode observer based on the super twisting algorithm (STA-SMO) with adaptive parameters estimation control is proposed, as parameter mismatches are considered. First, the conventional sliding mode observer (CSMO) is analyzed. The conventional exponential approach law produces a large chattering phenomenon in the back EMF estimation, which causes a large observation error when filtering the chattering through the low-pass filter. Second, a high-order approach law of the super twisting algorithm is introduced to observe the rotor position and speed estimation, which uses the integral function to eliminate the chattering of the sliding mode. Third, an adaptive parameter estimation control (APEC) is presented to enhance the observation accuracy caused by parameter mismatches; the motor parameter adaptive law of the APEC is designed by Lyapunov’s stability law. Finally, the proposed method not only reduces both the chattering and the low-pass filter, but it also enhances accuracy and robustness against parameter mismatches, as discussed through simulations and experiments.

Improved finite-time command filtered backstepping fault-tolerant control for flexible hypersonic vehicle

Abstract: The tracking control of a flexible hypersonic vehicle with system perturbation and actuator fault is investigated in this paper. A control-oriented model is constructed, which is composed of velocity loop and altitude loop. A double-layer fast adaptive-gain super-twisting disturbance observer (ASTDO) is designed to provide finite-time estimation of the lumped terms of external disturbances and actuator faults. The ASTDO has the property of the non-overestimation of adaptive gains and chattering reduction with higher precision. Based on the estimation of ASTDO, a novel finite-time command filtered backstepping (FCFB) scheme is proposed for the high-order altitude loop. The merit lies in the modified compensating signals and new virtual commands to compensate the command filter errors. The remarkable features of the developed algorithm include the finite-time convergence with higher accuracy and the chattering suppression. Finally, comparative simulation results are given to demonstrate the superiority of the proposed method.

Simulation Model and Method for Active Torsional Vibration Control of an HEV

Abstract: Hybrid electric vehicles (HEV) might cause new noise vibration and harshness (NVH) problems, due to their complex powertrain systems. Therefore, in this paper, a new longitudinal dynamic simulation model of a series-parallel hybrid electric bus with an active torsional vibration control module is proposed. First, the schematic diagrams of the simulation model architecture and the active control strategy are given, and the dynamic models of the main components are introduced. Second, taking advantage of the characteristics of hybrid systems, a method of determining the key dynamic parameters by a bench test is proposed. Finally, in a typical bus-driving cycle for Chinese urban conditions, time domain and frequency domain processing methods are used to analyze vehicle body jerk, fluctuation of rotational speed, and torsional angle of the key components. The results show that the active control method can greatly improve the system’s torsional vibration performance when switching modes and at resonance.

Adaptive torsional vibration active control for hybrid electric powertrains during start-up based on model prediction:

Abstract: Torsional vibration occurs when the hybrid vehicle transmission system is influenced by the multiple excitations and the dynamic loads caused by mode switching. Torsional vibrations of transmission...

Attitude trajectory tracking of quadrotor UAV using super-twisting observer-based adaptive controller:

Abstract: The successful implementation of high-level decision algorithm on quadrotor depends on the accurate trajectory tracking performance. In this paper attitude estimation and trajectory tracking contro...