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19th International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Malita, Victor Misgeld, Berno J. E. Leonhardt, Steffen Rhein Westfal TH Aachen Philips Chair Med Informat Technol D-52074 Aachen Germany

ISBN: (纸本)9781479950812

This paper presents a new algorithm for the automatic adaptation of planar motion for an exoskeleton device. The designed control algorithm aims at adapting the motion of the hemiparetic patient "wearing" the exoskeleton to the desired gait based on the patient's active torque. The patient-exoskeleton coupling is modeled as a stand-alone planar nonlinear hybrid two-legged robot model with point feet. The control algorithm consists of a feedback linearization method extended with an optimal controller that assures the tracking of the reference trajectories and disturbance rejection. This control structure is capsuled by an adaptive iterative learning control, which in every motion cycle adapts the reference joint-angle trajectories for the exoskeleton to follow in the next cycle. Motion captured data from healthy subjects was used as reference input in the closed-loop system, because these trajectories guarantee human-like behavior for the robot model with point feet. Simulation results of the iterative learning controller show promising results with respect to cyclic disturbances, as associated with hemiparetic spasticity.

关键词： adaptive control closed loop systems feedback iterative methods learning systems legged locomotion medical robotics optimal control trajectory control adaptive gait trajectory controller adaptive iterative learning control automatic adaptation closed-loop system cyclic disturbances disturbance rejection exoskeleton device feedback linearization method hemiparetic patient hemiparetic spasticity human-like behavior iterative learning controller optimal controller patient active torque patient-exoskeleton coupling planar motion planar nonlinear hybrid two-legged robot model point feet reference joint-angle trajectories reference trajectory tracking Equations Exoskeletons Legged locomotion Mathematical model Robot kinematics Trajectory Biped locomotion Closed loop systems trajectory optimal controllers Disturbance rejection Robot kinematics iterative learning control medical robotics trajectory control learning systems shelling iterative methods Exoskeleton Device adaptive control Optimal control

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adaptive learning control for Finite Interval Tracking Based on Variable Wavelet Neural Network

Adaptive Learning Control for Finite Interval Tracking Based...

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第三十二届中国控制会议

作者： HE Chao LI Junmin Department of Mathematics Xidian University

ISBN: (纸本)9781479900305

This paper is concerned with the nonlinearly parametric adaptive iterative learning control for unknown nonlinear *** a variable wavelet neural network（WNN） to approximate unknown nonlinearities,where the number of basis functions gradually increasing following the iterations,a robust state feedback adaptive control approach is derived in the sense of Lyapunov *** a reasonable assumption on variable WNN,which can relax the limitation of fixed neural networks used in traditional approaches,the adaptive laws for nonlinear parameters in variable WNN are proposed for the first time,which can resist the disturbance deduced from the variation of WNN's *** this proposed controller,it is proved that all signals of closed-loop system are bounded over iteration and time domain,and the tracking error converges to a small neighborhood of the origin in the sense of L norm after a finite number of learning iterations,by choosing appropriate design ***,the approach can be extended to strict feedback ***,an example and some comparisons are given to demonstrate the effectiveness of the method proposed in this paper.

关键词： adaptive iterative learning control nonlinearly parametric system variable wavelet neural network

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Unbalance Vibratory Displacement Compensation for Active Magnetic Bearings

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Chinese Journal of Mechanical Engineering 2013年第1期26卷 95-103页

作者： GAO Hui XU Longxiang ZHU Yili College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics

As the dynamic stiffness of radial magnetic bearings is not big enough, when the rotor spins at high speed, unbalance displacement vibration phenomenon will be produced. The most effective way for reducing the displacement vibration is to enhance the radial magnetic bearing stiffness through increasing the control currents, but the suitable control currents are not easy to be provided, especially, to be provided in real time. To implement real time unbalance displacement vibration compensation, through analyzing active magnetic bearings （AMB） mathematical model, the existence of radial displacement runout is demonstrated. To restrain the runout, a new control scheme-adaptive iterative learning control （A1LC） is proposed in view of rotor frequency periodic uncertainties during the startup process. The previous error signal is added into AILC learning law to enhance the convergence speed, and an impacting factor/3 influenced by the rotor rotating frequency is introduced as learning output coefficient to improve the rotor control effects, As a feed-forward compensation controller, AILC can provide one tmknown and perfect compensatory signal to make the rotor rotate around its geometric axis through power amplifier and radial magnetic bearings. To improve AMB closed-loop control system robust stability, one kind of incomplete differential PID feedback controller is adopted. The correctness of the AILC algorithm is validated by the simulation of AMB mathematical model adding AILC compensation algorithm through MATLAB soft. And the compensation for fixed rotational frequency is implemented in the actual AMB system. The simulation and experiment results show that the compensation scheme based on AILC algorithm as feed-forward compensation and PID algorithm as close-loop control can realize AMB system displacement minimum compensation at one fixed frequency, and improve the stability of the control system. The proposed research provides a new adaptive iterative/earning cont

关键词： active magnetic bearings unbalance displacement compensation mathematical model adaptive iterative learning control

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Discrete-time adaptive ILC for Non-parametric Uncertain Nonlinear Systems with Iteration-Varying Trajectory and Random Initial Condition

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ASIAN JOURNAL OF control 2013年第2期15卷 562-570页

作者： Chi, R. H. Hou, Z. S. Jin, S. T. Wang, D. W. Qingdao Univ Sci & Technol Sch Automat & Elect Engn Qingdao 266042 Peoples R China Beijing Jiaotong Univ Sch Elect & Informat Engn Adv Control Syst Lab Beijing 100044 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Ctr E City EXQUISITUS Singapore 639798 Singapore

This paper presents a new discrete-time adaptive iterative learning control approach (AILC) for a class of time-varying nonlinear systems with nonparametric uncertainties and non-repeatable external disturbances by incorporating a novel iterative estimate scheme. A major distinct feature of the presented approach is that uncertainties can be completely compensated for, using only I/O data. Another distinct feature is that the pointwise convergence is achieved over a finite time interval without requiring the matching condition on initial states and reference trajectory. Rigorous mathematical analysis is developed, and simulation results illustrate the effectiveness of the proposed approach.

关键词： adaptive iterative learning control non-parametric uncertainties nonlinear discrete-time system random initial condition iteration-varying target trajectories

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adaptive Terminal iterative learning For Batch Process with Batch-varying Parameters

Adaptive Terminal Iterative Learning For Batch Process with ...

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32nd Chinese control Conference (CCC)

作者： Wang Jing Wang Yue Wang Wei Wei Huatong Cao Liulin Jin Qibing Beijing Univ Chem Technol Beijing 100029 Peoples R China Hebei Energy Coll Vocat & Technol Tangshan 063044 Hebei Peoples R China

ISBN: (纸本)9781479900305

adaptive terminal iterative learning control algorithm based on process model updated along with batch index is proposed for final quality control, in which process parameters are batch-varying. Firstly, the final quality model in batch process is built based on the online least square support vector machine (online LSSVM), where the moving-window technique is introduced to update the process model along with the batch changing. Then, an adaptive terminal iterative learning control algorithm according to optimal cost function is given. Convergence and stability analysis are derived based on Lyapunov energy function. Finally, the control algorithm is applied to the typical batch polymerization process, and the simulation results show that the proposed approach has well control performance, monotonous convergence and adaptive ability.

关键词： Online least square support vector machine adaptive iterative learning control Batch Process

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A New Dynamical Linearization based adaptive ILC for Nonlinear Discrete-time MIMO Systems

A New Dynamical Linearization based Adaptive ILC for Nonline...

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12th International Conference on control, Automation, Robotics and Vision (ICARCV)

作者： Chi, Ronghu Hou, Zhongsheng Jin, Shangtai Wang, Danwei Qingdao Univ Sci & Technol Sch Automat & Elect Engn Qingdao Peoples R China Beijing Jiatong Univ Sch Elect & Informat Engn Beijing Peoples R China Nayang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781467318723;9781467318716

Most of the available results of adaptive iterative learning control (AILC) hitherto have considered the control systems with known linearly parameterized structures. A dynamical linearization approach is developed for a general nonlinear multiple input multiple output systems. And then a discrete-time adaptive ILC approach is presented to deal with the ILC problems of nonlinear MIMO systems with iteration-varying initial error and reference trajectory. The controller design and analysis is completely data-driven without using any modeling information of the plant, but the measured I/O data only. The almost perfect tracking performance is asymptotically guaranteed by rigirous mathematical analysis.

关键词： adaptive iterative learning control Nonlinear discrete-time System dynamical linearization Random initial condition Iteration-varying target trajectories

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Hybrid adaptive iterative learning control of non-uniform. trajectory tracking for Nonlinear time-delay systems

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26th Chinese control Conference

作者： Li Junmin Li Xinmin Xing Keyi Xidian Univ Sch Sci Xian 710071 Peoples R China

ISBN: (纸本)9787811240559

A novel adaptive iterative learning control approach is proposed for a class of hybrid parametric nonlinear time-delay systems. The approach consisted of a differential -deference type updating law and a learning control law, can deal, with the non-uniform trajectory tracking problem, in which avoids the restricted on the tracking trajectory in the traditional ILC. A sufficient condition of tracking error converging to zero in the means of mean-square on the finite, interval is also given by constructing a novel composite energy function. A simulation example shows the feasibility and efficiency of the approach.

关键词： adaptive iterative learning control non-uniform trajectory tracking hybrid parametric nonlinear time-delay systems composite energy function

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adaptive iterative learning control for nonlinear systems with unknown control gain

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JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND control-TRANSACTIONS OF THE ASME 2004年第4期126卷 916-920页

作者： Chen, HD Jiang, P Univ Bradford Dept Cybernet & Virtual Syst Bradford BD7 1DP W Yorkshire England Tongji Univ Dept Informat & Control Engn Shanghai 200092 Peoples R China

An adaptive iterative learning control approach is proposed for a class of single-input single-output uncertain nonlinear systems with completely unknown control gain. Unlike the ordinary iterative learning controls that require some preconditions on the learning gain to stabilize the dynamic systems, the adaptive iterative learning control achieves the convergence through a learning gain in a Nussbaum-type function for the unknown control gain estimation. This paper shows that all tracking errors along a desired trajectory in a finite time interval can converge into any given precision through repetitive tracking. Simulations are carried out to show the validity of the proposed control method.

关键词： adaptive iterative learning control unknown control gain repetitive tracking

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adaptive iterative learning control of uncertain robotic systems

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IEE PROCEEDINGS-control THEORY AND APPLICATIONS 2000年第2期147卷 217-223页

作者： Choi, JY Lee, JS Pohang Univ Sci & Technol Dept Elect Engn Pohang 790784 South Korea

A distinct feature of the proposed AILC scheme is that uncertain parameters are estimated in the time domain whereas repetitive disturbances are identified and compensated in the iteration domain. The bounds of the parameters are not required to be known a priori, and the learning control gain can be adjusted independently of the parameter adaptation gain. The overall closed-loop stability and uniform error convergence in the iteration domain are established without any acceleration measurements or their estimated values. The proposed AILC scheme is a balanced combination of the conventional adaptive control and the iterative learning control, where the shortcomings of each scheme are complemented. The validity of the scheme is confirmed through a simulation example.

关键词： closed-loop stability uncertain robotic systems Simulation, modelling and identification adaptive control robots adaptive iterative learning control parameter estimation time domain estimation uniform error convergence matrix algebra repetitive disturbances Algebra Robotics control system synthesis learning systems iteration domain identification control system analysis and synthesis methods uncertain systems Self-adjusting control systems

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