Flexible manipulator trajectory learning control with input preshaping method

Objektif kertas kerja ini ialah untuk mengkaji keberkesanan gabungan pengawal pembelajaran berulang cerdik dan teknik pembentuk masukan bagi penjejakan masukan dan pengurangan getaran pada hujung suatu pengolah fleksibel. Model dinamik sistem tersebut diterbitkan menggunakan kaedah unsur terhingga. Pada permulaan, pengawal kadaran–kebezaan (PD) menggunakan sudut dan halaju hub direka bentuk untuk kawalan pergerakan badan tegar sistem. Kemudian, pengawal pembelajaran berulang dengan algoritma genetik dan pengawal suap hadapan berasaskan teknik pembentuk masukan ditambahkan untuk kawalan getaran sistem. Keputusan simulasi dalam domain masa dan frekuensi diberikan. Keberkesanan pengawal yang direka bentuk ini dikaji berasaskan penjejakan masukan dan kadar pengurangan getaran sistem. Keberkesanan pengawal ini untuk sistem pengolah fleksibel berbagai beban juga dikaji. Kata kunci: Pengolah fleksibel, algoritma genetik, kawalan cerdik, kawalan pembelajaran berulang, pembentukan masukan The objective of the work reported in this paper is to investigate the performance of an intelligent hybrid iterative learning control scheme with input shaping for input tracking and end–point vibration suppression of a flexible manipulator. The dynamic model of the system is derived using finite element method. Initially, a collocated proportional–derivative (PD) controller utilizing hub–angle and hub–velocity feedback is developed for control of rigid–body motion of the system. This is then extended to incorporate iterative learning control with genetic algorithm (GA) to optimize the learning parameters and a feedforward controller based on input shaping techniques for control of vibration (flexible motion) of the system. Simulation results of the response of the manipulator with the controllers are presented in time and frequency domains. The performance of hybrid learning control with input shaping scheme is assessed in terms of input tracking and level of vibration reduction. The effectiveness of the control schemes in handling various payloads is also studied. Key words: Flexible manipulator, genetic algorithms, intelligent control, iterative learning control, input shaping

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Hybrid Learning Control With Input Shaping for Input Tracking and Vibration Suppression of a Flexible Manipulator

Volume 1 ◽

10.1115/esda2004-58239 ◽

2004 ◽

Author(s):

M. Z. Md Zain ◽

M. O. Tokhi ◽

Z. Mohamed

Keyword(s):

Iterative Learning Control ◽

Vibration Suppression ◽

Hybrid Learning ◽

Learning Control ◽

Iterative Learning ◽

Body Motion ◽

Flexible Manipulator ◽

Input Shaping ◽

Control Schemes ◽

Frequency Domains

The objective of the work reported in this paper is to investigate the development of hybrid iterative learning control with input shaping for input tracking and end-point vibration suppression of a flexible manipulator. The dynamic model of the system is derived using the finite element method. Initially, a collocated proportional-derivative (PD) controller utilizing hub-angle and hub-velocity feedback is developed for control of rigid-body motion of the system. This is then extended to incorporate iterative learning control and a feedforward controller based on input shaping techniques for control of vibration (flexible motion) of the system. Simulation results of the response of the manipulator with the controllers are presented in the time and frequency domains. The performance of the hybrid learning control with input shaping scheme is assessed in terms of input tracking and level of vibration reduction. The effectives of the control schemes in handling various payloads are also studied.

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Gait generation via unified learning optimal control of Hamiltonian systems

Robotica ◽

10.1017/s0263574712000756 ◽

2013 ◽

Vol 31 (5) ◽

pp. 717-732 ◽

Cited By ~ 8

Author(s):

Satoshi Satoh ◽

Kenji Fujimoto ◽

Sang-Ho Hyon

Keyword(s):

Optimal Control ◽

Hamiltonian Systems ◽

Control Method ◽

Biped Robot ◽

Learning Control ◽

Parameter Tuning ◽

Tuning Parameter ◽

Gait Generation ◽

Optimal Control Method ◽

Trajectory Learning

SUMMARYThis paper proposes a repetitive control type optimal gait generation framework by executing learning control and parameter tuning. We propose a learning optimal control method of Hamiltonian systems unifying iterative learning control (ILC) and iterative feedback tuning (IFT). It allows one to simultaneously obtain an optimal feedforward input and tuning parameter for a plant system, which minimizes a given cost function. In the proposed method, a virtual constraint by a potential energy prevents a biped robot from falling. The strength of the constraint is automatically mitigated by the IFT part of the proposed method, according to the progress of trajectory learning by the ILC part.

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