Adaptive iterative learning control for high-order nonlinear systems with random initial state shifts

Li, Guojun; Lu, Tiantian; Han, Yishi; Xu, Zhijiang

doi:10.1016/j.isatra.2022.04.028

Cited by 13 publications

(8 citation statements)

References 24 publications

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“…Iterative learning control (ILC) is also a type of model‐free control method and is particularly effective for systems with repetitive operation. [ 33 ] ILC constructs the current control input based on previous control inputs and output errors, gradually eliminating output bias and achieving complete tracking of the desired output trajectory. FOWTs are influenced by the stochastic nature of offshore wind and waves, and they do not exhibit characteristics of repetitive operation.…”

Section: The Control Strategy Based On Model‐free Adaptive Control In...mentioning

confidence: 99%

Intelligent Control for Floating Offshore Wind Turbines Based on Game Theory with Data‐Driven and Reliability Awareness

Zhang

Yang

2023

Energy Tech

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To address the challenges of complex modeling and control objective conflicts, a game control scheme based on data‐driven and reliability awareness is proposed for floating offshore wind turbines (FOWTs). In this scheme, first, an enhanced model‐free adaptive control method with the integration of iterative learning is proposed, which utilized online data for driving modeling and control command derivation to ensure the functionality of the FOWT and maintain stable power generation. Then, a healthy reliability‐aware model of the pitching system is developed and integrated into the controller of FOWTs. Finally, a trade‐off between the reliability and functionality of FOWTs is intelligently made through bargaining game theory to derive reasonable control commands. Simulation experiments of the proposed control scheme are carried out using FAST under different wind conditions. The results show that, compared with the gain‐scheduled PI controller and model‐free adaptive controller, the output power of the proposed controller is more stable and the pitch motion of the FOWT is significantly reduced; compared with the improved model‐free adaptive controller, the proposed controller improves the reliability of the pitch system.

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Section: The Control Strategy Based On Model‐free Adaptive Control In...mentioning

confidence: 99%

Intelligent Control for Floating Offshore Wind Turbines Based on Game Theory with Data‐Driven and Reliability Awareness

Zhang

Yang

2023

Energy Tech

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“…Li et al [65] proposed a constrained spatial AILC to realize displacement-speed trajectory tracking for an automatic train control system with speed constraints and unknown uncertainties. The AILC [66] ensured that the system achieved complete tracking by rectifying initial shifts. For parameterizable nonlinear systems, Xu et al [67] combined variable structure control and learning control and presented a new nonlinear control scheme, namely, robust AILC, which can also be applied to nonlinear systems with dead zones [68] .…”

Section: Adaptive Iterative Learning Controllers and Rectifying Algor...mentioning

confidence: 99%

“…The desired trajectories are

. We applied control law (11) and parameter update law (12) , where the parameters were assigned according to the methods in [66] . The preset rectified initial state shift interval is

01

.…”

Section: Adaptive Iterative Learning Controllers and Rectifying Algor...mentioning

confidence: 99%

A survey of methods for handling initial state shifts in iterative learning control

Chen,

Lu,

2023

Heliyon

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“…In reference [17], a novel practical ILC updating law is proposed to improve the path-tracking accuracy of nonholonomic mobile robots with a fixed initial value. In reference [18], a different initial state that shifts the rectifying schemes and solves the problem of iterative learning control for high-order nonlinear systems with arbitrary initial state error is described. However, there are few studies that have simultaneously considered the initial shifts and faults.…”

Section: Introductionmentioning

confidence: 99%

Fault-Estimation Design Based on an Iterative Learning Scheme for Interconnected Multi-Flexible Manipulator Systems with Arbitrary Initial Value

Feng,

Chen,

et al. 2023

Actuators

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This paper reports the design of an iterative-learning-scheme-based fault-estimation method for interconnected nonlinear multi-flexible manipulator systems with arbitrary initial value. For state estimation, observers are employed to reconstruct the state. The proposed scheme ensures that each flexible manipulator subsystem’s states can track their desired reference signals within a finite time. In the next step, an iterative learning fault-estimation law is proposed to track the actual fault signal. In contrast to the previous literature, this approach utilizes potential information from previous iterations to enhance the accuracy of the estimation in the current iteration. Based on these efforts, the obstacle caused by the arbitrary initial value is circumvented, and addressing the fault-estimation errors of each flexible manipulator subsystem are uniformly ultimately bounded is successfully achieved. Then, the λ-norm is developed to explore the convergence conditions of the presented methods. Finally, the effectiveness and feasibility of the proposed approach are verified through assessment of simulation results.

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Adaptive iterative learning control for high-order nonlinear systems with random initial state shifts

Cited by 13 publications

References 24 publications

Intelligent Control for Floating Offshore Wind Turbines Based on Game Theory with Data‐Driven and Reliability Awareness

Intelligent Control for Floating Offshore Wind Turbines Based on Game Theory with Data‐Driven and Reliability Awareness

A survey of methods for handling initial state shifts in iterative learning control

Fault-Estimation Design Based on an Iterative Learning Scheme for Interconnected Multi-Flexible Manipulator Systems with Arbitrary Initial Value

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