Sangho Jin

Fuzzy Control for Robot Manipulators with Artificial Rubber Muscles

When controlling a robot manipulator by aplying the well-known computed torque control law, we usually need the mathematically accurate model. However, it is not necessarily easy to make a rigorous model, because the dynamic modeling of the robot manipulator essentially include some uncertainties such as system pa- rameters, disturbance inputs and nonlinear elements. Thus we can be faced with the problem of designing a robust controller that achieves a stable control, even through we use inaccurate modeling information. Some different approaches to the problem of robust control design for uncertain systems have beem proposed for a case when the bounds on the uncertainties are known (Hui and Ẓak, 1992); two major approaches to the deterministic control of uncertain systems are the deterministic control using Lyapunov functions (Corless and Leitmann, 1981; Coreless, 1989) and the variable structure control (or sliding mode control) methods (Utkins, 1977; De- Carlo et al. The approached without using the bound information on the uncertainties can also be found in Chen (1990) or Imura et al. (1991).

Controls of servomotors for carry hospital robots

Two methods for controlling servomotors with pulse encoders are presented for improving the movement of a carry hospital robot (CHR) along a desired line. Some simulation studies have been executed for the design of a PI controller or optimal regulator for the control of the DC servomotor. By applying the simulation results, we actually design a PI controller in an analogue circuit and experimentally compare the results of the PI control with those of a PLL (Phase Locked Loop) control. It is then clarified that the latter approach is effective in improving the accuracy of the CHR.

An Iterative Learning Fuzzy Control for Multi-Link Manipulators

Abstract An iterative learning fuzzy controller is described for controlling the trajectory of a multi-link robot manipulator. The learning controller used here is called a multiple fuzzy controller, in which several elemental fuzzy controllers are processed in arallel and the degree of the usage of each inferred consequent is determined by using a linear neural network. Two learning algorithms are considered in the framework of the specialized learning architecture: one is based on minimizing the squared sum of the trajectory error for each link and the other is based on directly minimizing the squared trajectory error for each link. The effectiveness of the proposed control method is illustrated by making some simulations for a two-link manipulator.