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Under-Actuated Robot Manipulator Positioning Control Using Artificial Neural Network Inversion Technique

DOI: 10.1155/2012/927905

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Abstract:

This paper is devoted to solve the positioning control problem of underactuated robot manipulator. Artificial Neural Networks Inversion technique was used where a network represents the forward dynamics of the system trained to learn the position of the passive joint over the working space of a 2R underactuated robot. The obtained weights from the learning process were fixed, and the network was inverted to represent the inverse dynamics of the system and then used in the estimation phase to estimate the position of the passive joint for a new set of data the network was not previously trained for. Data used in this research are recorded experimentally from sensors fixed on the robot joints in order to overcome whichever uncertainties presence in the real world such as ill-defined linkage parameters, links flexibility, and backlashes in gear trains. Results were verified experimentally to show the success of the proposed control strategy. 1. Introduction Underactuated robot manipulator possesses fewer actuators than degrees of freedom (DOF). Complex internal dynamics, nonholonomic behavior, and lack of feedback linearizability are often exhibited in such systems, making that class of robots a challenging one for synthesis of control schemes. Due to their advantages over fully actuated robots, this type of manipulators has gained the interest of several researchers [1–16]. Saving in weight and cost is an advantage, where low-cost automation and space robots require this feature. Another advantage is that underactuated robots can easily overcome actuator failure due to unexpected accident. Such fault-tolerant control is highly desirable for robots in remote or hazardous environments [1, 2]. The difficulty of the control problem for underactuated mechanisms is obviously due to the reduced dimension of the input space. In particular it has been shown that this system is highly nonlinear and it is impossible to stabilize asymptotically with a smooth feedback [3]. Sordalen et al. [4] have designed an joint robot controlled by just two motors using nonholonomic gears. Other researchers have tried controlling an underactuated robot in a gravity field, such as the Acrobot [5–7]. The control of a high-bar robot was investigated by Takashima [8] while Saito et al. [9] have investigated the control of a brachiation robot. Neglecting joint friction which is not easy to achieve in real world as it involves high manufacturing cost, Luca et al. [10, 11] have studied the control of two-link manipulator moving in a horizontal plane with a single actuator at the first

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