Dual IM‐based ILC scheme for linear discrete‐time systems with iteration‐varying reference

Zhu, Qiao; Xu, Jian‐Xin

doi:10.1049/iet-cta.2017.0592

Cited by 12 publications

(7 citation statements)

References 40 publications

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“…where p d q d r d ½ T can be calculated by equations (3), (17), and (18). k pÁ and k dÁ are the appropriate proportion and differential gains, respectively, which should be pre-tuned.…”

Section: Control Strategy For Individual Quadrotor Uavmentioning

confidence: 99%

Iterative learning-based formation control for multiple quadrotor unmanned aerial vehicles

Zhao

Jing

Chen

et al. 2020

International Journal of Advanced Robotic Systems

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A double-layer formation control is proposed to solve the repeated tasks for multiple quadrotor unmanned aerial vehicle systems. The first layer aims at achieving a formation target in which the iterative learning control is designed based on relative distance with neighbor unmanned aerial vehicles and absolute distance with virtual leader unmanned aerial vehicle. The formation controller is responsible for keeping the formation shape and generating the desired flying trajectories for each drones. The second layer control aims at achieving a high-precision tracking to desired flying trajectories which are generated from the formation controller. A double closed-loop proportional-derivative strategy is designed to ensure the accuracy of trajectory tracking for each individual drone. Simulations for the circle formation mission of the multiple quadrotor unmanned aerial vehicle system are given to verify the efficiency of the proposed method.

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“…where p d q d r d ½ T can be calculated by equations (3), (17), and (18). k pÁ and k dÁ are the appropriate proportion and differential gains, respectively, which should be pre-tuned.…”

Section: Control Strategy For Individual Quadrotor Uavmentioning

confidence: 99%

Iterative learning-based formation control for multiple quadrotor unmanned aerial vehicles

Zhao

Jing

Chen

et al. 2020

International Journal of Advanced Robotic Systems

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“…On the basis of general HOIM-based ILC schemes, some extended methods are proposed. Xu et al proposed a dual-HOIM-based repetitive ILC scheme, which consists of HOIM-based repetitive control (RC) and HOIM-based ILC [65]. The HOIM-based ILC loop can learn the control input from previous iterations and the HOIM-based RC loop can learn the control input from previous times at the current iteration.…”

Section: Applications and Extensionsmentioning

confidence: 99%

A Survey on High-Order Internal Model Based Iterative Learning Control

Chai

2019

IEEE Access

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Iterative learning control (ILC) has been developed for decades and is mainly used to solve the repetitive control tasks. However, in the actual operation of systems, there are many non-strictly repetitive or iteration-varying factors, such as the iteration-varying reference trajectory, non-repetitive system parameters, iteration-related initial states, iteration-dependent input and output disturbances, etc. In order to solve the non-strictly repetitive problems, the High-Order Internal Model (HOIM)-based ILC is proposed. HOIM can be formulated as a polynomial in the iteration domain, which is auto-regressive. HOIM-based ILC for nonlinear systems is more complex than HOIM-based ILC for linear systems, and when the system parameters change iteratively, the Lyapunov-based analysis method is used instead of traditional contraction mapping method. Not only can HOIM be integrated into the traditional ILC, it can be also combined with other control methods, such as adaptive control, terminal control, repetitive control and so on. In this paper, we review the advances in HOIM-based ILC, systematically sort out the development and main contents of HOIM, summarize its main applications and extensions, and finally put forward some further development directions. INDEX TERMS High-order internal model, iterative learning control, non-repetitiveness.

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“…From Equations (11) and (12), the discrete-state space string system without uncertainties and external disturbances is expressed as…”

Section: Stability Analysismentioning

confidence: 99%

“…The iterative learning control (ILC) approach has been discussed previously using intelligent control methodologies [5][6][7][8][9][10][11][12][13]. ILC is a practical approach to control such systems that execute similar tasks in a repetitive manner [14], such as a robotic arm in industry [15], a chemical reactor production [16], a nonlinear process [17], and position control of nano systems [18].…”

Section: Introductionmentioning

confidence: 99%

H∞ Iterative Learning Boundary Vibration Control of Uncertain Vibrating String with Iteration-Varying Distributed Disturbance and Boundary Disturbance

Eshag

Sun

et al. 2019

Applied Sciences

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By integrating H∞ control into iterative learning boundary control (ILBC) with the method of lines (MOL), this paper suggests a novel scheme to reduce the vibrations of the uncertain vibrating string system in the presence of iteration-varying distributed/boundary disturbances. The dynamics of the string system are defined by two kinds of differential equations, namely: (a) non-homogenous hyperbolic partial differential equation (PDE) and (b) ordinary differential equations (ODEs). Firstly, MOL is employed to attain the string dynamics in the form of a state-space system instead of a PDE system. Secondly, ILBC is developed in a super-vector framework and integrated with the H∞ control for decreasing the perturbations of the uncertain string system in the presence of iteration-varying distributed/boundary disturbances. Along the time, position, and iteration coordinates: (a) the boundary deflections of the string system are controlled; (b) the vibrations along the string are attenuated to zero; and (c) the external disturbances are excluded. Based on the H∞ algebraic approach, performance/stability conditions and global convergence of the closed-loop string system are assured. Conducted simulations illustrate that the suggested scheme is efficient for diminishing the vibrations of certain and uncertain vibrating string system.

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Dual IM‐based ILC scheme for linear discrete‐time systems with iteration‐varying reference

Cited by 12 publications

References 40 publications

Iterative learning-based formation control for multiple quadrotor unmanned aerial vehicles

Iterative learning-based formation control for multiple quadrotor unmanned aerial vehicles

A Survey on High-Order Internal Model Based Iterative Learning Control

H∞ Iterative Learning Boundary Vibration Control of Uncertain Vibrating String with Iteration-Varying Distributed Disturbance and Boundary Disturbance

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