A Parallel Levenberg-Marquardt Algorithm for Recursive Neural Network in a Robot Control System

2. Related Work

The main task of manipulator control is to solve the robot in the work space in the movement of the location, trajectory and attitude, operation sequence and time control and other issues. The most important and most basic is the Invesrekinematics and Invesredynamcis problems and the corresponding control strategies (Oscar, 2002). If we achieve effective control, we need to solve two problems, one is how to achieve the stability of the closed-loop error system, making the trajectory tracking error as soon as possible close to 0; the other is how to suppress interference, as far as possible to reduce the interference signal on the tracking accuracy. For the above problems, the general method is modeled by mathematical methods, and through the linear servo control theory to design the controller, you can get a solution. However, due to the particularity of the robot (uncertainty factors and excessive interference signal), it is difficult to obtain accurate mathematical model, there is no precise mathematical model, it cannot guarantee the quality of control. Until recently, with the increasingly sophisticated neural network control technology, the control problem of the robot seems to have a new opportunity.