Dual-mode predictive control of a rotor suspension system

Wu, Yue; Ren, Gui-Ping; Zhang, Haitao

doi:10.1007/s11432-019-9896-2

Cited by 9 publications

(3 citation statements)

References 29 publications

(41 reference statements)

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“…The main difference of our approach lies in the fact that the shares of control contributed by humans and machines can be explicitly optimized in the proposed approach. Recent contributions described in [24,25] have also shown the capability of MPC in solving path-following control problems of autonomous driving vehicles as well as other constrained and nonlinear control systems. In [26], an approximate dynamic programming (ADP)-based approach has been proposed to solve the human-machine shared control problem for robot arms.…”

Section: Related Workmentioning

confidence: 99%

Event-triggered shared lateral control for safe-maneuver of intelligent vehicles

Jiang

Zhang

et al. 2021

Sci. China Inf. Sci.

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With the increasing number of cars, road traffic accidents have caused a lot of losses every year and human factors play an important role in many cases. Applying active safety assistance control or shared control techniques in intelligent vehicles is promising to reduce the number of traffic accidents. In this context, the dynamic optimization of the shared control policy and the smooth transitions of control authority between human drivers and intelligent driving systems are critical issues to be solved. Motivated by this, this paper proposes an event-triggered shared control approach for safe-maneuver of intelligent vehicles with online risk assessment. In the proposed approach, a Bayesian regularized artificial neural network (BRANN) is designed to predict vehicle trajectories and build a quantization function to assess the risk level owing to potential collision events. The shared controller dynamically optimizes the shared control policies between the human and the intelligent driving system via solving a model predictive control (MPC) problem. The predicted driving behaviors in the prediction horizon are pre-computed with a finite-horizon model predictor steering the predicted trajectories contributed by human driving. Moreover, smooth transitions back to human driving mode are realized via adding penalties on the shared control of the intelligent driving system. Three simulation scenarios in the PreScan environment, i.e., rear-end collision avoidance, lane-keeping and unskilled driving, are studied to test the effectiveness of the proposed approach. The simulation results, including the comparison with a linear quadratic regulator (LQR)-based shared controller, are reported, which show that the proposed approach can timely evaluate dangerous events and realize safe driving in terms of collision avoidance and lane-keeping. Also, the proposed approach outperforms the LQR-based shared controller in terms of smooth transitions.

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Section: Related Workmentioning

confidence: 99%

Event-triggered shared lateral control for safe-maneuver of intelligent vehicles

Jiang

Zhang

et al. 2021

Sci. China Inf. Sci.

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“…Jiang et al studied the influence of extended structural parameters on the vibration characteristics of spindle bearing systems based on the dynamic model (Jiang and Zheng, 2010). Wu et al proposed a rotor-magnetic bearing system incorporating a predictive control model (Wu et al, 2020). Through theoretical analysis and turning experiments, Wu et al verified that the angle between the direction of UMP and the feed has a certain relationship with the machined surface profile (Wu et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Dynamic analysis of grinding electric spindle bearing-rotor system under eccentric action

Sheng

Sun

et al. 2023

JAMDSM

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To study the influence of eccentricity on the dynamic characteristics of grinding electric spindle, firstly, the nonlinear oil film force (NOFF) expression of sliding bearing is established based on the boundary condition of adhesion pressure. Then, the expression of the unbalanced magnetic pull (UMP) under the eccentricity of the air gap is derived. Finally, the bearing-rotor dynamics equation of the grinding electric spindle considering the NOFF and UMP is established, and the dimensionless processing is carried out. The nonlinear dynamics study of the bearing-rotor system under the influence of geometric eccentricity and mass eccentricity is carried out through the shaft center trajectory and the shaft center vibration spectrum. The research results can provide reference for the optimal design of grinding electric spindle.

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“…For nonlinear systems, that is, AMB systems, the optimization problem is non convex, which is costly in practice. Model predictive control has been proposed to stabilize the AMB system, see, (Wu et al, 2020; Zhao et al, 2015). However, since these MPC techniques have been designed based on LTI models of the AMB system, they cannot deal efficiently with the high nonlinearity of the system.…”

Section: Introductionmentioning

confidence: 99%

Imbalance compensation of active magnetic bearing systems using model predictive control based on linear parameter-varying models

Morsi

Abbas

Ahmed

et al. 2022

Journal of Vibration and Control

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Active magnetic bearing (AMB) is a suspension system to levitate a rotating shaft freely without any physical contact which allows extremely fast rotation speeds. One big control challenge of the AMB systems, which appears during high rotation speeds, is the non-uniform distribution of the rotor weight about its rotating axis. This is usually referred to as the rotor imbalance problem which produces sinusoidal disturbance forces. These disturbances lead to undesirable vibrations and large deviations of the rotor shaft from its desired trajectories. We adopt in this work model predictive control (MPC) to reduce the effect of these sinusoidal disturbances and to achieve a stable levitation of the rotor shaft while tracking a reference trajectory. Owing to the MPC capability of handling constraints in an optimal manner, physical input constraints can be committed. Moreover, state constraints can be imposed to ensure safety of operation. For tractable implementation, we embed the nonlinear dynamics of the system in a linear parameter-varying (LPV) representation. To guarantee stability of the closed-loop system, a terminal cost and a terminal constraint set are included in the MPC optimization problem. For tractable computations of these terminal ingredients, a reduced LPV model is considered. The performance of the proposed LPV-MPC scheme is validated via simulation on the nonlinear model of an experimental setup of an AMB system and it is compared with two other classical controllers commonly used for these AMB systems in practice.

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Dual-mode predictive control of a rotor suspension system

Cited by 9 publications

References 29 publications

Event-triggered shared lateral control for safe-maneuver of intelligent vehicles

Event-triggered shared lateral control for safe-maneuver of intelligent vehicles

Dynamic analysis of grinding electric spindle bearing-rotor system under eccentric action

Imbalance compensation of active magnetic bearing systems using model predictive control based on linear parameter-varying models

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