Coordinate transformation and on-line planning for position/force control of constrained robots

This paper presents an improved artificial bee colony (ABC) technique for solving the dynamic economic emission dispatch (DEED) problem. Ramp rate limits, valve-point loading effects and prohibited operating zones (POZs) have been considered. The proposed technique integrates the grenade explosion method and Cauchy operator in the original ABC algorithm, to avoid random search mechanism. However, the DEED is a multi-objective optimization problem with two conflicting criteria which need to be minimized simultaneously. Thus, it is recommended to provide the best solution for the decision-makers. Shannon’s entropy-based method is used for the first time within the context of the on-line planning of generator outputs to extract the best compromise solution among the Pareto set. The robustness of the proposed technique is verified on six-unit and ten-unit system tests. Results proved that the proposed algorithm gives better optimum solutions in comparison with more than ten metaheuristic techniques.

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An On-line Planning Support System to Evaluate Urban and Regional Planning Scenarios

Planning Support Systems in Practice - Advances in Spatial Science ◽

10.1007/978-3-540-24795-1_18 ◽

2003 ◽

pp. 331-347 ◽

Cited By ~ 4

Author(s):

Christopher Pettit ◽

Tung-Kai Shyy ◽

Robert Stimson

Keyword(s):

Support System ◽

Regional Planning ◽

Line Planning ◽

Urban And Regional Planning ◽

Planning Support ◽

Planning Support System ◽

On Line

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Intelligent Adaptive Active Force Control of a Robotic Arm with Embedded Iterative Learning Algorithms

Jurnal Teknologi ◽

10.11113/jt.v35.594 ◽

2012 ◽

Cited By ~ 3

Author(s):

Musa Mailah ◽

Miaw Yong Ong

Keyword(s):

Force Control ◽

Learning Algorithms ◽

Iterative Learning ◽

Operating Conditions ◽

Robotic Arm ◽

Active Force ◽

Inertia Matrix ◽

Active Force Control ◽

Controller Gain ◽

On Line

Kawalan jitu dan lasak bagi satu sistem lengan robot atau pengolah adalah amat penting terutama sekali jika sistem mengalami pelbagai bentuk bebanan dan keadaan pengendalian. Kertas kerja ini memaparkan satu kaedah baru dan lasak untuk mengawal lengan robot menggunakan teknik pembelajaran secara berlelaran yang dimuatkan dalam strategi kawalan daya aktif. Sebanyak dua algoritma pembelajaran utama digunakan dalam kajian – yang pertama digunakan untuk menala gandaan pengawal secara automatik manakala yang satu lagi pula untuk menganggarkan matriks inersia pengolah. Kedua-dua parameter ini dihasilkan secara adaptif dan dalam talian ketika robot sedang menjalankan tugas menjejak trajektori dalam persekitaran tindakan daya gangguan. Dalam kajian ini, pengetahuan awal tentang kedua–dua nilai gandaan pengawal dan anggaran matriks inersia tidak wujud. Dengan demikian, suatu skema kawalan yang jitu dan lasak terhasil. Keberkesanan kaedah yang dicadangkan dapat ditentusahkan melalui hasil kajian yang diperoleh dan dibentangkan dalam kertas kerja ini. Kata kunci: Adaptif; kawalan daya aktif; pembelajaran berlelaran; matriks inersia; gandaan pengawal The robust and accurate control of a robotic arm or manipulator are of prime importance especially if the system is subjected to varying forms of loading and operating conditions. The paper highlights a novel and robust method to control a robotic arm using an iterative learning technique embedded in an active force control strategy. Two main iterative learning algorithms are utilized in the study – the first is used to automatically tune the controller gains while the second to estimate the inertia matrix of the manipulator. These parameters are adaptively computed on-line while the robot is executing a trajectory tracking task and subject to some forms of external disturbances. No priori knowledge of both the controller gains and the estimated inertia matrix are ever assumed in the study. In this way, an adaptive and robust control scheme is derived. The effectiveness of the method is verified and can be seen from the results of the work presented in this paper. Keywords: Adaptive; active force control; iterative learning; inertia matrix; controller gain

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On-line Planning on Active SLAM-Based Robot Olfaction for Gas Distribution Mapping

2018 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS) ◽

10.1109/agers.2018.8554199 ◽

2018 ◽

Author(s):

Duddy Soegiarto ◽

Bambang Riyanto Trilaksono ◽

Widyawardana Adiprawita ◽

Egi Muhammad Idris ◽

Yayan Prima Nugraha

Keyword(s):

Gas Distribution ◽

Line Planning ◽

Distribution Mapping ◽

On Line ◽

Gas Distribution Mapping

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A Force-Control Model for Edge-Deburring with Filamentary Brush

Journal of Manufacturing Science and Engineering ◽

10.1115/1.1373650 ◽

1999 ◽

Vol 123 (3) ◽

pp. 528-532 ◽

Cited By ~ 7

Author(s):

Lienjing Chen ◽

Robert J. Stango ◽

Vikram Cariapa

Keyword(s):

Force Control ◽

Material Removal ◽

Complete Removal ◽

Force Sensor ◽

Control Model ◽

Master Curves ◽

Machining Performance ◽

Unknown Variable ◽

Machining Force ◽

On Line

In this paper a force-control model is developed for edge deburring with filamentary brushes. The model is based upon experimentally obtained “master curves,” that is, material removal data that corresponds to the actual machining performance of the brush/workpart system during the incremental burr removal process. This information is used in conjunction with the on-line brush machining force to compute the brush feed rate that ensures complete removal of the edge burr. Computer simulated results are reported for the removal of an edge burr having unknown variable height. The results indicate that the present force-control model can provide a straight forward approach for computing brush feed rates that lead to complete removal of edge burrs, and suggests that implementation can be carried out using a force sensor and a simple control strategy.

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