Application of iterative learning control methods for a service robot with multi-body kinematics

Basler, Sandra; Dünow, Peter; Marquardt, Mathias; Daasch, Andreas

doi:10.1109/mmar.2015.7283920

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Bolder et al (2012) successfully implemented the ILC algorithm in an inkjet printer. Baßler et al (2015) described an application of a multi-body service robot combined with the ILC protocol. A dual-stage ILC system has been introduced to improve the performance of a multi-input multi-output (MIMO) unmatched system in joint elasticity robots, and there has been large-scale research on improving the ILC system.…”

Section: Introductionmentioning

confidence: 99%

Disturbance rejection via iterative learning control with a disturbance observer for active magnetic bearing systems

Tang

et al. 2019

Frontiers Inf Technol Electronic Eng

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Although standard iterative learning control (ILC) approaches can achieve perfect tracking for active magnetic bearing (AMB) systems under external disturbances, the disturbances are required to be iteration-invariant. In contrast to existing approaches, we address the tracking control problem of AMB systems under iteration-variant disturbances that are in different channels from the control inputs. A disturbance observer based ILC scheme is proposed that consists of a universal extended state observer (ESO) and a classical ILC law. Using only output feedback, the proposed control approach estimates and attenuates the disturbances in every iteration. The convergence of the closed-loop system is guaranteed by analyzing the contraction behavior of the tracking error. Simulation and comparison studies demonstrate the superior tracking performance of the proposed control approach.

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Section: Introductionmentioning

confidence: 99%

Disturbance rejection via iterative learning control with a disturbance observer for active magnetic bearing systems

Tang

et al. 2019

Frontiers Inf Technol Electronic Eng

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“…Nonetheless, in conventional ILC, a constant initial condition must be met in conventional ILC, that is, the time and state must be reset at the beginning of each iteration. This design approach has been applied for robotic manipulators and the other industrial control [24][25][26][27][28]. Several control systems for the prototypical wing section of Block and Strganac [9] have been designed in the past, but the application of ILC theory for this model has not been attempted.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Control of a Nonlinear Aeroelastic System despite Gust Load

Gao

Zhang

2015

International Journal of Aerospace Engineering

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The development of a control strategy appropriate for the suppression of aeroelastic vibration of a two-dimensional nonlinear wing section based on iterative learning control (ILC) theory is described. Structural stiffness in pitch degree of freedom is represented by nonlinear polynomials. The uncontrolled aeroelastic model exhibits limit cycle oscillations beyond a critical value of the freestream velocity. Using a single trailing-edge control surface as the control input, a ILC law under alignment condition is developed to ensure convergence of state tracking error. A novel Barrier Lyapunov Function (BLF) is incorporated in the proposed Barrier Composite Energy Function (BCEF) approach. Numerical simulation results clearly demonstrate the effectiveness of the control strategy toward suppressing aeroelastic vibration in the presence of parameter uncertainties and triangular, sinusoidal, and graded gust loads.

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“…There have been a lot of efforts to tackle this problem using different control schemes, such as iterative learning control (ILC) (Baßler et al (2015), Xie and Ren (2018)), model predictive control (MPC) ), and sliding mode control (SMC) ). ILC is effective in acquiring desired precision in various applications like robots (Baßler et al (2015)), and PEA trajectory tracking (Xie and Ren (2018)). However, as it takes several iterations (i.e., trials) in ILC approaches to reach the desired precision, they are not suitable for real-time PEA trajectory tracking applications.…”

Section: Introductionmentioning

confidence: 99%

Long short-term memory (LSTM) neural network-based system identication and augmented predictive control of piezoelectric actuators

Patil¹

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Application of iterative learning control methods for a service robot with multi-body kinematics

Cited by 5 publications

References 3 publications

Disturbance rejection via iterative learning control with a disturbance observer for active magnetic bearing systems

Disturbance rejection via iterative learning control with a disturbance observer for active magnetic bearing systems

Iterative Learning Control of a Nonlinear Aeroelastic System despite Gust Load

Long short-term memory (LSTM) neural network-based system identication and augmented predictive control of piezoelectric actuators

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