Force control is one of the most important and fundamental tasks of robot manipulators. In order to realize precise contact tasks with an nuknown environment, robot controllers have to adapt themselves to the unknown environment. Recently, fuzzy reasoning and/or neural networks are expected to play an important role in force control of robot manipulator. Human knowledge can be reflected in control rules by using fuzzy control method, and learning/adaptation ability can be obtained by applying neural networks to robot controllers. In this paper, an effective control policy for robot contact tasks with an unknown environment is proposed using fuzzy-neuro techniques. In this control policy, a neural network is applied to classify the unknown environment based on its dynamic response of the environment and then select the suitable fuzzy neural force controller. The selected fuzzy neural force controller is able to realize the desired contact force precisely using its on-line adaptation ability. Furthermore, the fuzzy selection of the controller is introduced for force control with the environment whose suitable fuzzy neural force controller is not prepared. The effectiveness of the proposed method is evaluated by experiment with a 2DOF planar robot manipulator.
|Number of pages||7|
|Journal||Nihon Kikai Gakkai Ronbunshu, C Hen/Transactions of the Japan Society of Mechanical Engineers, Part C|
|Publication status||Published - Jan 1 1999|
All Science Journal Classification (ASJC) codes
- Mechanics of Materials
- Mechanical Engineering
- Industrial and Manufacturing Engineering