Application of a General Learning Algorithm to the Control of Robotic Manipulators

Abstract

In this paper, we discuss the use of a general learning algo rithm for the dynamic control of robot manipulators. Unlike some other learning control schemes, learning is based solely on observations of the input-output relationship of the system being controlled and is independent of control objectives. Information learned previously can be applied to new control objectives as long as similar regions of the system state space are involved. The control scheme requires no a priori knowl edge of the robot dynamics and is easy to apply to a particu lar control problem or to modify to accommodate changes in the physical system. The control scheme is computationally efficient and well suited to fixed-point implementation. The learning controller is evaluated in a series of computer simu lations involving a two-axis-articulated robot arm during simulated repetitive and nonrepetitive movements. We inves tigate the effects of varying learning algorithm parameters as well as control system performance in the presence of obser vation noise and changing manipulator payloads. The learn ing control system presented promises to provide good dy namic performance in complex situations at a reasonable cost as measured in terms of both hardware and software devel opment.

Tracking control of a planar five-link bipedal walking system with point contact, considering self-impact joint constraint by adaptive neural network method

“…There have been some developments in the use of neural networks for the control purposes, Miller et al (1987); Miyamoto et al (1988); Ozaki et al (1991); Saad et al (1994). In general, neural network control design has two steps.…”

Automatic Vision-Based Optical Fiber Alignment Using Multirate Technique

“…The CMAC has been successfully applied to function approximation [14] and robot control problems [15]. The basic structure of the CMAC algorithm is shown in Fig.…”

Survey of Intelligent Control Algorithms for Humanoid Robots

“…Recently, some researchers have begun considering the use of neural networks for control of humanoid walking (Doerschuk et al (1998);Miller (1994);Miller (1987); Kun et al (1999); Wang and Gruver (1992) Salatian et al 1992aSalatian et al ,1992bSalatian et al ,1997 studied off-line and on-line reinforcement techniques for adapting the gait designed for horizontal surfaces to be executed on sloping surfaces. They considered humanoid robot SD-2 with 8 DOFs and two force sensors on both feet.…”

“…More recently, Miller et al 1994Miller et al ,1987Miller et al ,1999 has developed a hierarchical controller that combines simple gait oscillators, classical feedback control techniques and neural network learning, and does not require detailed equations of the dynamics of walking. The emphasis is on the real-time control studies using an experimental ten-axis biped robot with foot force sensors.…”