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Adaptive compensation for time-varying uncertainties in model-based control of lower-limb exoskeleton systems

Science China(Information Sciences) 2021年第11期64卷 237-239页

作者： Guangkui SONG Rui HUANG Hong CHENG Jing QIU Shuai FAN Center for Robotics University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China

Dear editor,Exoskeletons are widely used in human locomotion assistance and strength augmentation to enhance strength and endurance, wherein strategies based on model-based control are extremely sensitive to errors in the dynamic models. Additionally, the unpredictable human-exoskeleton interaction force can unbalance the pilot. Therefore, learning methods were utilized to improve the accuracy of the dynamic model [1, 2]. The modeling of dynamic model was decoupled based on the direction of interest [3]. Nevertheless,

关键词： Reinforcement Learning Interaction Learning Human-Robot Interaction Inaccurate Dynamic Model time-varying uncertainties Compensation Strategy Lower Exoskeleton

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H2 controller design of linear systems with time-varying uncertainties and input quantization

H2 controller design of linear systems with time-varying unc...

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第26届中国控制与决策会议

作者： Bo-Chao Zheng Yuan-Lu Li Ning Sun School of Information and Control Nanjing University of Information Sciences & Technology

The paper considers the robust H2 state feedback control design for a class of linear uncertain systems with input quantization. The uncertainties are assumed to be time-varying, unknown, but bounded. The state feedback controller includes two parts. The first part, u1(t), whose gains updating automatically between the lower bounds and upper bounds of uncertain parameters, is obtained according to linear matrix inequalities conditions. Such design purpose is to guarantee the robust stability and H2 performance. The second part, u2(t) is designed to eliminate the effect of the quantization error. The main feature of this paper is that a switching mechanism is introduced effectively to determine which bound of uncertain parameters is active in the process of control at each time instant. Finally, a numerical example is given to demonstrate the effectiveness of the proposed scheme.

关键词： robust control time-varying uncertainties switching strategy input quantization

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Robust static output feedback based ILC design with finite frequency specifications for batch processes with time-varying uncertainties

Robust static output feedback based ILC design with finite f...

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第三十九届中国控制会议

作者： Shoulin Hao Tao Liu Wojciech Paszke Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University ofTechnology Institute of Advanced Control Technology Dalian University of Technology Institute of Automation Electronic and Electrical Engineering University of Zielona Góra

For industrial batch processes with time-varying uncertainties, a robust output feedback based iterative learning control（ILC） design is proposed by introducing finite frequency specifications, based on only the output measurement. The integral of output tracking error in the time domain is incorporated into the ILC updating law to improve the tracking performance from batch to batch. Based on the generalized Kalman-Yakubovich-Popov lemma, robust stability of the resulting closed-loop ILC system along both time and batch directions is analyzed in the repetitive system framework. The corresponding ILC law can be determined from the established tractable linear matrix inequality conditions. Moreover, it is shown that the obtained stability condition can also guarantee the bounded output tracking error and system input in the presence of time-and/or batch-varying uncertainties. An illustrative example from the literature is adopted to demonstrate the effectiveness of the proposed design.

关键词： Batch process time-varying uncertainties iterative learning control output feedback finite frequency range specification

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Improved PI based indirect-type ILC for batch processes with time-varying uncertainties: A new perspective

Improved PI based indirect-type ILC for batch processes with...

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第37届中国控制会议

作者： Shoulin Hao Tao Liu Wojciech Paszke Krzysztof Ga?kowski Institute of Advanced Control Technology Dalian University of Technology Institute of Control and Computation Engineering University of Zielona Góra

This paper proposes an improved PI based indirect-type iterative learning control for batch processes with timevarying uncertainties from a new perspective. The proposed ILC law can be designed independent of the initial PI tuning which primarily aims at holding the robust stability of the closed-loop system due to that only the setpoint command is adjusted by ILC. The feedforward control used in the recently developed indirect-type ILC methods is replaced by retuning the closed-loop PI controller, while the redundant learning information from the current and previous cycles could be reduced compared with the existing literature. Correspondingly, the control structure is significantly simplified with less design parameters, while achieving the similar convergence performance along the batch direction. A robust ILC updating law along with retuned PI parameters are derived by solving a linear matrix inequality and results in the robust stability of the closed-loop system. An illustrative example from the literature is adopted to demonstrate the effectiveness of the proposed method.

关键词： Batch process time-varying uncertainties PI controller Iterative learning control PI retuning

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