Vibration suppression in multi-body systems by means of disturbance filter design methods

Copot, Cosmin; Ionescu, Clara-Mihaela; Vanlanduit, Steve; Keyser, Robain De

doi:10.1177/1077546317736190

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…The model predictive control algorithm employed in this work is the generalized input-output formulation (EPSAC) detailed in [7] and with similar application described in [8]. The generic MPC principle is depicted in figure 1, with the following procedure.…”

Section: A Generic Epsac-mpc Approachmentioning

confidence: 99%

Automatic tuning of predictive control in a hydrostatic drive train system in nominal operation

Ionescu

Haemers

Maxim

et al. 2019

2019 23rd International Conference on System Theory, Control and Computing (ICSTCC)

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This paper presents an automatic model based predictive control tuning and operation of a hydrostat drive train system under nominal conditions. An industrial-friendly version of the predictive control strategy is applied whereas the model is simplified as an approximation of first order plus dead time and the control design parameters are automatically tuned. Implementation on a SISO drive train is experimentally achieved with successful performance in closed loop tests. From the identified characteristics of the real life drive train, a simulated MIMO system is extracted and automatic tuning is again performed. A limitation of the present work is that constraints are not yet included, only safety limits.

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Section: A Generic Epsac-mpc Approachmentioning

confidence: 99%

Automatic tuning of predictive control in a hydrostatic drive train system in nominal operation

Ionescu

Haemers

Maxim

et al. 2019

2019 23rd International Conference on System Theory, Control and Computing (ICSTCC)

Self Cite

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“…Based on a simple 2DOF model, many control approaches are investigated for ASSs. Those approaches include a linear quadratic regulator (LQR) (Taghirad and Esmailzadeh, 1998), H ∞ (Gao et al, 2000; Li et al, 2019a, 2019b), sliding mode control (SMC) (Chen et al, 2017), back-stepping (Sun et al, 2013), disturbance filter (Copot et al, 2018) and so on. The SMC is a variable structure control and has been applied in both the linear and nonlinear systems with external disturbance (Ionescu and Muresan, 2015).…”

Section: Introductionmentioning

confidence: 99%

A research of sliding mode control method with disturbance observer combining skyhook model for active suspension systems

Shangguan

Zhao

2019

Journal of Vibration and Control

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Based on a nonlinear two-degree-of-freedom model of active suspension systems, an approach of the sliding mode control with disturbance observer combining skyhook model sliding mode control with disturbance observer combining is proposed for improving the performance of active suspension systems, and the effectiveness of the proposed approach is validated by the active suspension system plant. Two problems of active suspension systems are solved by using the proposed approach when the tire is excited by the step displacement. One problem is that the suspension deflection of active suspension systems, i.e. the difference between the sprung mass displacement and the unsprung mass displacement, using conventional sliding mode control with disturbance observer not converges to zero in finite time, and the phenomenon of the impact of suspension against the limit block is produced. This problem is solved by providing a reference value of the sprung mass displacement in an active suspension system, which is obtained from the skyhook model. The other problem is that disturbances exist in active suspension systems, which are caused by the inaccurate parameters of stiffness and damping. This problem is solved by designing a disturbance observer to estimate the summation of the disturbances. Finally, the performance indexes of the active suspension system with the sliding mode control with disturbance observer combining skyhook model are calculated and compared with those of using the conventional sliding mode control with disturbance observer and the linear quadratic regulator approach.

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“…The biggest advantage of this controller is that it has the ability to eliminate steady-state errors, robustness toward plant uncertainties, and also eliminates good noise. Subsequently, there were a number of researchers offering methods to improve the FOPID controller to improve quality and control accuracy (Copot et al, 2017; De Keyser et al, 2015, 2018; Muresan et al, 2015). Similarly, Aldair and Wang (2010) have published how to apply the optimal algorithm to correct control parameters.…”

Section: Introductionmentioning

confidence: 99%

A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system

Dao

2019

Journal of Vibration and Control

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This article presents a new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for the nonlinear active mount systems. The proposed method, intelligent adapter fractions proportional–integral–derivative controller, is a smart combination of the time delay estimation control and intelligent fractions proportional–integral–derivative with adaptive control parameters following the speed range of engine rotation via the deep neural network with the optimal non-dominated sorting genetic algorithm-III deep learning algorithm. Besides, we proposed optimal fuzzy logic controller with optimal parameters via particle swarm optimization algorithm to control reciprocal compensation to eliminate errors for intelligent adapter fractions proportional–integral–derivative controller. The control objective is to deal with the classical conflict between minimizing engine vibration impacts on the chassis to increase the ride comfort and keeping the dynamic wheel load small to ensure the ride safety. The results of this control method are compared with that of traditional proportional–integral–derivative controller systems, optimal proportional–integral–derivative controller parameter adjustment using genetic algorithms, linear–quadratic regulator control algorithms, and passive drive system mounts. The results are tested in both time and frequency domains to verify the success of the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system. The results show that the proposed optimal fuzzy logic controller–intelligent adapter fractions proportional–integral–derivative control system of the active engine mount system gives very good results in comfort and softness when riding compared with other controllers.

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Vibration suppression in multi-body systems by means of disturbance filter design methods

Cited by 4 publications

References 30 publications

Automatic tuning of predictive control in a hydrostatic drive train system in nominal operation

Automatic tuning of predictive control in a hydrostatic drive train system in nominal operation

A research of sliding mode control method with disturbance observer combining skyhook model for active suspension systems

A new control method based on fuzzy controller, time delay estimation, deep learning, and non-dominated sorting genetic algorithm-III for powertrain mount system

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