Research On Adaptive Force/position Control For Environmental Constrained Reconfigurable Manipulators

Reconfigurable manipulator consists of robot modules which incorporate power electronics, computing systems, sensors and actuators. These modules can be quickly separated and assembled into the desirable manipulator that is suitable for different tasks with external constraints. Compared with the conventional manipulators with the fixed configuration, the reconfigurable manipulator can be adapted to diverse task requirements and take advantage of low cost, easy maintenance, convenient modification, portability and durability against system malfunctions. In many fields, such as industrial production,national defense, aviation and space exploration, disaster relief and other fields, it has wide application prospect. In these complex and changeable environment, it is inevitable that the end-effector of the robot arm is in contact with the outside environment, it is asked to be more flexible and intelligent, and the simple position control can not meet the needs, also need to control the contact force. Therefore, it is necessary to study the hybrid force/position coordinated control of the constrained reconfigurable manipulator.In this paper, the kinematic equations of the reconfigurable manipulator are given by using the exponential product formula firstly, and the dynamic model of the free space of the system is given by using the Newton-Euler equations. Jacobian matrix is used to analyze the relationship between manipulator constraint and the joint force/torque, in order to give the reconfigurable manipulator dynamics model under the condition of constrained environment. Secondly, according to the constraint conditions, the reduced-order dynamic model of a reconfigurable manipulator is formulated,sliding mode force/position control for constrained reconfigurable manipulator based on adaptive neural network is studied.Then, considering the reconfigurable manipulator,combining decentralized control with centralized control scheme, hybrid force/position control for constrained reconfigurable manipulators based on adaptive neural network is designed to control the contact force and to prevent the excessive contact force of the manipulator. Finally, the effectiveness and feasibility of the designed controller are verified by numerical simulation of the two degree of freedom for constrained reconfigurable manipulator.