Indirect iterative learning control: Application on artificial pancreatic &amp;#x03B2;-cell

Wang, Youqing; Doyle, Francis J.

doi:10.1109/ccdc.2009.5192271

Cited by 10 publications

(5 citation statements)

References 12 publications

(14 reference statements)

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“…On the other hand, for indirect ILC, two essential issues need to be established: what algorithms are used to design the local control, and which parameters of the local controller are updated by ILC. In our previous studies [20, 21], the local control was designed by MPC, and the set-point for MPC was updated by ILC. This novel combination is referred to as learning- type MPC (L-MPC).…”

Section: Control Algorithmsmentioning

confidence: 99%

Quest for the Artificial Pancreas: Combining Technology with Treatment

Harvey¹,

Wang

Grosman³

et al. 2010

IEEE Eng. Med. Biol. Mag.

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Section: Control Algorithmsmentioning

confidence: 99%

Quest for the Artificial Pancreas: Combining Technology with Treatment

Harvey¹,

Wang

Grosman³

et al. 2010

IEEE Eng. Med. Biol. Mag.

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“…Compared with direct ILC, the indirect ILC has some advantages listed as follows: (1) the existing control structure need not change; only an outer loop module is added to update some parameters of the existing controller; (2) storage data based on advanced control method can be easily integrated and implemented in some batch process; (3) in some cases, indirect learning-type control has better robustness than the direct form [6,7]. This is because direct learning-type control must have a feedforward term, which is sensitive to variations in batch direction.…”

Section: Introductionmentioning

confidence: 99%

“…(3) in some cases, indirect learning-type control has better robustness than the direct form [6,7]. This is because direct learning-type control must have a feedforward term, which is sensitive to variations in batch direction.…”

Section: Introductionmentioning

confidence: 99%

Indirect iterative learning control for robot manipulator with non‐Gaussian disturbances

Chen

Gao

Sun

et al. 2013

IET Control Theory & Applications

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In this study, a novel indirect iterative learning control (ILC) strategy is presented for a robotic manipulator that performs repeat operation and is also subjected to non-Gaussian disturbances. The performance index about the entropy of tracking error and the related optimisation method are used to update the local parameters of controller between any two adjacent batches. Moreover, the entropy is employed as it is a unified probabilistic measure of uncertainty quantification regardless whether the random disturbances are Gaussian distribution or not. Thus, an innovative performance index about tracking error entropy that represents the relationship between the entropy of error and controller gains is proposed in order to obtain the controller which can drive the uncertainty of output error as small as possible with the increase of the batch number. Then the non-linear stochastic optimal method is presented so as to obtain updated gains for the next batch. Stability of closed-loop system is analysed. Finally, a comparison between a classic ILC and the proposed approach is given. Moreover, the effectiveness and feasibility of the proposed control schemes is verified by some simulation results of robotic trajectory tracking.

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“…The solid lines denote the real-time information; the dotted lines denote the information in the previous batch; components in the dashed frames comprise the ILC (modified from[7]). …”

mentioning

confidence: 99%