Robust Iterative Learning Controller Design using the Performance Weighting Function of Feedback Control Systems

Doh, Tae-Yong,Ryoo, Jung Rae,Chang, Dong Eui 제어로봇시스템학회 2014 International Journal of Control, Automation, and Vol.12 No.1

Iterative learning controllers combined with existing feedback controllers have prominent capability of improving tracking performance in repeated tasks. However, the iterative learning controller has been designed without utilizing effective information such as the performance weighting function to design a feedback controller. In this paper, we deal with a robust iterative learning controller design problem for an uncertain feedback control system using its explicit performance information. We first propose a robust convergence condition in the $L_2$-norm sense for an iterative learning control (ILC) scheme. We present a method to design an iterative learning controller using the information on the performance of the existing feedback control system such as performance weighting functions and frequency ranges of desired trajectories. From the obtained results, several design criteria for iterative learning controller are provided. Through analysis on the remaining error, the loop properties before and after learning are compared. We also show that, in the $L_2$-norm sense, the remaining error can be less than the initial error under certain conditions. Finally, to show the validity of the proposed method, simulation studies are performed.

이족보행로봇의 걸음새 제어를 위한 지능형 학습 제어기의 구현

임동철(Dong-Cheol Lim),국태용(Tae-Yong Kuc) 대한전기학회 2010 전기학회논문지 P Vol.59 No.1

This paper presents an intelligent learning controller for repetitive walking motion of biped walking robot. The proposed learning controller consists of an iterative learning controller and a direct learning controller. In the iterative learning controller, the PID feedback controller takes part in stabilizing the learning control system while the feedforward learning controller plays a role in compensating for the nonlinearity of uncertain biped walking robot. In the direct learning controller, the desired learning input for new joint trajectories with different time scales from the learned ones is generated directly based on the previous learned input profiles obtained from the iterative learning process. The effectiveness and tracking performance of the proposed learning controller to biped robotic motion is shown by mathematical analysis and computer simulation with 12 DOF biped walking robot.

Add-on-type Robust Iterative Learning Controller Design Based on the Information of Feedback Control Systems

Tae-Yong Doh,Jung Rae Ryoo 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.5

Iterative learning control (ILC) combined with a feedback control system improves tracking performance by iteratively tuning the feedforward control signal on the basis of the system information, such as control inputs and tracking errors from previous iterations. Although ILC systems have been added to the existing feedback control systems, the learning controllers have been designed without considering valuable information, such as weighting functions used to design a robust feedback controller. This paper proposes a method for the design of an add-on-type robust iterative learning controller for an uncertain feedback control system using its explicit tracking-performance and plant-uncertainty information. The proposed ILC system is composed of two learning controllers, one of which is directly obtained from the inverse of the nominal feedback control system, and the other is a low-pass filter, known as the Q-filter ensuring robustness for the convergence under uncertainty. To design the learning controllers, first, a robust convergence condition in the L2-norm sense is formulated, which is represented as the Q-filter and other known system information. Subsequently, the sufficient conditions to ensure that the remaining error is less than the initial error are derived. From the results, the criteria for simply designing the learning controllers are presented. Finally, important properties of the proposed ILC system, such as convergence rate and robustness, are demonstrated through simulations.

Generalized Minimum Variance Iterative Learning Speed Control of Ultrasonic Motor

Jingzhuo Shi,Wenwen Huang 대한전기학회 2021 Journal of Electrical Engineering & Technology Vol.16 No.5

In order to reduce the infl uence of time-varying disturbance on motion control performance of ultrasonic motor, the speed control strategy of ultrasonic motor is studied in this paper. An iterative learning control strategy including prediction and closed-loop control is proposed by combining iterative learning control with generalized minimum variance self-tuning control. By introducing the previous control information into the objective function and using the design method of the generalized minimum variance control strategy, the generalized minimum variance iterative learning control law is obtained, which has both self-learning and self-adaptive ability. The proposed control strategy is applied to the speed control of ultrasonic motor and validated by simulation and experiment. The results of experiments under diff erent load conditions and diff erent given values show that good control performance can be obtained by adopting the proposed control strategy. The results of intermittent loading experiments indicate that, the ability to adapt to the non-repetitive disturbances such as sudden load mutation is enhanced.

Position Control of Electro Hydraulic Actuator (EHA) using an Iterative Learning Control

D. N. C. Nam(도안녹치남),N. M. Tri(우엔민트리),H. G. Park(박형규),K. K. Ahn(안경관) 유공압건설기계학회 2014 드라이브·컨트롤 Vol.11 No.4

This paper presents the development of a compact position generator to be used for industrial purposes based on a pump controlled Electro-Hydraulic Actuator (EHA), which is closed-loop controlled by an embedded based Iterative PID controller. The controller is designed by combining the PID controller and the iterative learning scheme to perform tracking control for periodically desired references. Control algorithm is implemented on an embedded computer (AD 7011-EVA) which makes the implementation and application in industrial environments easier.

Torque Ripple Minimization of PMSM Using Parameter Optimization Based Iterative Learning Control

Changliang Xia,Weitao Deng,Tingna Shi,Yan Yan 대한전기학회 2016 Journal of Electrical Engineering & Technology Vol.11 No.2

In this paper, a parameter optimization based iterative learning control strategy is presented for permanent magnet synchronous motor control. This paper analyzes the mechanism of iterative learning control suppressing PMSM torque ripple and discusses the impact of controller parameters on steady-state and dynamic performance of the system. Based on the analysis, an optimization problem is constructed, and the expression of the optimal controller parameter is obtained to adjust the controller parameter online. Experimental research is carried out on a 5.2kW PMSM. The results show that the parameter optimization based iterative learning control proposed in this paper achieves lower torque ripple during steady-state operation and short regulating time of dynamic response, thus satisfying the demands for both steady state and dynamic performance of the speed regulating system.

Position control of Electro hydraulic actuator (EHA) Using an iterative learning control

N. M. Tri(우엔민트리),K. K. Ahn(안경관),H.G. Park(박형규) 유공압건설기계학회 2014 유공압건설기계학회 학술대회논문집 Vol.2014 No.9

This paper presents the development of a compact position generator to be used for industrial purposes bases on a pump controlled Electro-Hydraulic Actuator (EHA), which is close-loop controlled by an embedded based Iterative PID controller. The controller is designed by combining the PID controller and the iterative learning scheme to perform tracking response for periodically desired references. Control algorithm is implemented on an embedded computer (AD 7011-EVA) which eases implementation and application in industrial environments.

A Learning Control Strategy for Robot-assisted Bathing via Impedance Sliding Mode Technique With Non-repetitive Tasks

Yuexuan Xu,Xin Guo,Bokai Xuan,Hao Sun,Gaowei Zhang,Jian Li,Xingyu Huo,Zhifeng Gu 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.3

This paper investigates an impedance-based iterative learning sliding mode control scheme for robotassisted bathing, taking into consideration scenarios with unknown model parameters. Initially, the utilization ofimpedance control is not confined to merely adjusting the desired trajectory but is also instrumental in ensuringactive compliance control during the robot-assisted bathing procedure. Furthermore, an iterative learning control(ILC) is devised to estimate the iteration-invariant dynamic parameters, which are intricate and challenging to precisely ascertain in practical applications. To mitigate the effect of divergent initial conditions in ILC, a trajectoryreconstruction method is introduced, thus ensuring the convergence of tracking errors even when starting from random initial states. Moreover, an adaptive sliding mode control mechanism is proposed to counteract non-parametricexternal disturbances and the torque generated through human-machine interaction during the bathing process. Theconvergence of the double closed-loop system in both the time and iterative domains is demonstrated through theapplication of the composite energy function method. Eventually, the efficacy and superiority of the control strategyoutlined in this paper are jointly verified through co-simulation employing MATLAB and ADAMS.

Feedback-Based Iterative Learning Control for MIMO LTI Systems

Tae-Yong Doh,Jung Rae Ryoo 대한전기학회 2008 International Journal of Control, Automation, and Vol.6 No.2

This paper proposes a necessary and sufficient condition of convergence in the L₂-norm sense for a feedback-based iterative learning control (ILC) system including a multi-input multi-output (MIMO) linear time-invariant (LTI) plant. It is shown that the convergence conditions for a nominal plant and an uncertain plant are equal to the nominal performance condition and the robust performance condition in the feedback control theory, respectively. Moreover, no additional effort is required to design an iterative learning controller because the performance weighting matrix is used as an iterative learning controller. By proving that the least upper bound of the L₂-norm of the remaining tracking error is less than that of the initial tracking error, this paper shows that the iterative learning controller combined with the feedback controller is more effective to reduce the tracking error than only the feedback controller. The validity of the proposed method is verified through computer simulations.

Position control of Electro Hydraulic Actuator (EHA) using an Iterative Learning Control

도안녹치남,우엔민트리,박형규,안경관 사단법인 유공압건설기계학회 2014 드라이브·컨트롤 Vol.11 No.4

This paper presents the development of a compact position generator to be used for industrial purposes based on a pump controlled Electro-Hydraulic Actuator (EHA), which is closed-loop controlled by an embedded based Iterative PID controller. The controller is designed by combining the PID controller and the iterative learning scheme to perform tracking control for periodically desired references. Control algorithm is implemented on an embedded computer (AD 7011-EVA) which makes the implementation and application in industrial environments easier.