State-dependent periodic adaptive disturbance compensation

Ahn, Hyo‐Sung; Chen, YangQuan

doi:10.1049/iet-cta:20060172

Cited by 21 publications

(8 citation statements)

References 24 publications

(24 reference statements)

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“…The speed control and the torque control of the motor system is influenced negatively by those SDDs. [1][2][3][4] Most of the SDDs are position dependent and affect the operation of the main controller. For instance, when a proportional plus integral (PI) controller is used as the main controller without any sub-controller, it turns out that although the desired velocity is constant, the velocity oscillation is presented making the periodic speed ripple.…”

Section: State Dependent Disturbances In Motor Systemmentioning

confidence: 99%

“…Many kinds of control methods that target to reject the effect of state dependent disturbance (SDD) exploit the state dependent characteristic of SDD have been proposed [1][2][3][4]. However, those compensation methods are not entirely satisfactory in practical motor control environment because they did not consider the several types of time delays that are provoked very frequently in practical motor control.…”

Section: Introductionmentioning

confidence: 99%

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State dependent disturbance compensation method for motor control system with unknown time-varying input delay

Jung

Won

2014

2014 Proceedings of the SICE Annual Conference (SICE)

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In a research field of motor control, several types of control methods that target to eliminate the effects of state dependent disturbances in the motor control environment had been suggested. However, the unknown time-varying input time delay has not been considered in that control schemes. In this paper, a state dependent disturbance compensation method using adaptive control scheme in unknown time-varying input time delay existing motor control environment is presented. The effectiveness of the proposed state dependent disturbance compensation method is demonstrated using MATLAB simulation results. The compensation method is also available in the environment which contains additional known output time delay.

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Section: State Dependent Disturbances In Motor Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State dependent disturbance compensation method for motor control system with unknown time-varying input delay

Jung

Won

2014

2014 Proceedings of the SICE Annual Conference (SICE)

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“…This paper is mainly concerned with the general state-dependent periodic disturbance similar to [1], [2], [3], [8], [9], [10], [14]. The disturbance could be any type of nonlinear periodic function depending on a state variable x which usually represents the linear displacement or rotational angle.…”

Section: Disturbancementioning

confidence: 99%

Dual-high-order periodic adaptive learning compensation for state-dependant periodic disturbance

Luo

Chen

Ahn

2008

2008 47th IEEE Conference on Decision and Control

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1) la(x)1 s boo 18a(x)j8xl S ba < 00 ex:> Fdisturbance == L Ai sin(WiX + 'Pi), i=lwhere Ai is the amplitude, Wi is the state-dependent periodic disturbance frequency, and 'Pi is the phase angle. Note that, this general form can well represent state-dependent periodic disturbance in real world, for example, the state-dependent friction in [2], the position-dependent cogging force in permanent magnetic linear motor [8] or permanent magnetic synchronous motor [9] [10], the eccentricity in the wheeled mobile robots [7] and the experiment apparatus [1], and so on.For simplicity, in the sequel, we denote the above statedependent periodic disturbance as a(x). From physical limit consideration, it is reasonable to believe that a(x) is bounded, that is, errors of more than one previous periods are utilized in the adaptive updatingllearning law. Experimental results reported in [10] confirm that UO-PALe does achieve better compensation performance than the first-order PALC scheme.In the present work, we propose a new high-order periodic adaptive learning compensation (HO-PALC) method for statedependent periodic disturbance where the stored information of more than one previous periods is used and, the information includes composite tracking error as well as the estimate of the periodic disturbance. This is called dual HO-PALC (DHO-PALe) scheme which we show offers potential to achieve faster learning convergence. In particular, when the reference signal is also periodically changing, the proposed DHO-PALC can achieve much better convergence performance in terms of both convergence speed and final error bound. Asymptotical stability proof of the proposed DHO-PALC is presented. Extensive lab experimental results are presented to illustrate the effectiveness of the proposed DHO-PALC scheme over the first-order periodic adaptive learning compensation (FO-PALC).The major contributions of this paper include 1) A new dualhigh-order periodic adaptive learning compensation method for state-dependent periodic disturbance and the proof of the asymptotical stability of the system with the DHO-PALC; 2) Experimental study of the DBO-PALC for state-dependent periodic disturbance on a dynamometer position control system; 3) Experimental demonstration of the advantages of the DHO-PALC over the FO-PALC scheme. II. THE GENERAL FORM OF STATE-DEPENDENTPERIODIC DISTURBANCE This paper is mainly concerned with the general state-dependent periodic disturbance similar to [1], [2], [3], [8], [9], [10], [14]. The disturbance could be any type of nonlinear periodic function depending on a state variable x which usually represents the linear displacement or rotational angle. In Fourier series, the general state-dependent periodic disturbance can be expressed by Abstraet-State-periodic disturbances an' frequently found in motiOb control systems. Eump1eS intlUde tOIling in permanent maanetic linear motor, ectentricity in rotary machines aDd etc. This paper considers general form or state-dependent periodic disturbance and proposes a new blgb-order perIod...

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“…Therefore, the classical time-based ILC cannot be used directly. The spatial ILC schemes are studied in [1,16,25], but the control objective in these approaches is different from that in BLC. It also should be noted that BLC is different from the terminal ILC [26] and point-to-point ILC [9].…”

Section: Introductionmentioning

confidence: 99%

Initial states iterative learning for three-dimensional ballistic endpoint control

et al. 2016

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In this paper, an initial states iterative learning control algorithm is proposed for control of the ballistic endpoint displacement in three-dimensional space, where the target is moving and the projectile experiences system uncertainties. The characteristics of the three-dimensional ballistic process are formulated and explored, and the learning algorithm is proposed in the spatial domain. The algorithm consists of two parts. First, the initial speed and angles are iteratively learned to make the projectile attain a fixed position. Second, the shooting time is learned to tune the arrival time of the projectile. Since the dimensions of the solution space are larger than that of the task space, three control manners, including shooting speed, shooting angle and their combination, are researched respectively. Through rigorously analyzed, it is proved that the algorithm is convergent and the multiple initial states can be adjusted simultaneously. Finally, an example of practical cannonball projection is presented to verify the effectiveness of the proposed algorithms.

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State-dependent periodic adaptive disturbance compensation

Cited by 21 publications

References 24 publications

State dependent disturbance compensation method for motor control system with unknown time-varying input delay

State dependent disturbance compensation method for motor control system with unknown time-varying input delay

Dual-high-order periodic adaptive learning compensation for state-dependant periodic disturbance

Initial states iterative learning for three-dimensional ballistic endpoint control

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