In this paper, we propose a new learning control scheme for various walk motion control of biped robot with same learning-base by neural network. We show that learning control algorithm based on the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multi layer back propagation neural network identification is simulated to obtain a dynamic model of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The biped robots have been received increased attention due to several properties such as its human like mobility and the high-order dynamic equation. These properties enable the biped robots to perform the dangerous works instead of human beings. Thus, the stable walking control of the biped robots is a fundamentally hot issue and has been studied by many researchers. However, legged locomotion, it is difficult to control the biped robots. Besides, unlike the robot manipulator, the biped robot has an uncontrollable degree of freedom playing a dominant role for the stability of their locomotion in the biped robot dynamics. From the simulation and experiments the reliability of iterative learning control was illustrated.

In general, articulated robot control technology is limited to the design of robot arm control systems considering each joint of the robot joint as a simple servomechanism. This method describes the varying dynamics of a manipulator inadequately because it neglects the motion and configuration of the whole arm mechanism. The changes of the parameters in the controlled system are significant enough to render conventional feedback control strategies ineffective. This basic control system enables a manipulator to perform simple positioning tasks such as in the pock and place operation. However, joint controllers are severely limited in precise tracking of fast trajectories and sustaining desirable dynamic performance for variations of payload and parameter uncertainties. In many servo control applications the linear control scheme proposes unsatisfactory, therefore, a need for nonlinear techniques that increasing. for Forging process automation.

In this paper, we propose a new technique to the design and real-time control of an adaptive controller for robotic manipulator based on digital signal processors. The Texas Instruments DSPs(TMS320C80) chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for dual-arm robotic manipulators. In the proposed scheme, adaptation laws are derived from model reference adaptive control principle based on the improved Lyapunov second method. The proposed adaptive controller consists of an adaptive feed-forward and feedback controller and time-varying auxiliary controller elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for a dual arm robot manipulator with eight joints. joint space and cartesian space.

Since the oil shock of 1970's there was a strong upward tendency for the use of the high viscosity and poorer quality fuels. Therefore the misfiring engine occurs due to the decrease of quantity injected for lean burn and emission control in Compression Ignition Common Rail Direct Injection diesel engine. In this study, it is designed and used the test bed which is installed with fuel injector controller. In addition to equipped engine using CRDI by controlling the injection timing with mapping modulator, it has tested and analyzed the engine performance and combustion characteristics, as it is varied that they are the operating parameters: fuel injected quantity, engine speed and injection timing.

In the paper, we propose an stable walking algorithm of biped robot on the ground and working motion stabilization algorithm against external disturbances. We propose obstacle hurdling, incline walking, and going-up stairs algorithm by using infrared sensors and F/T sensors. Also, posture stabilization algorithm against external forces is designed using F/T sensors. Infrared sensors are used to detect the obstacles in he working environment and F/T sensors are used to obtain the ZMP of biped robot. The experimental results show that the biped robot performs obstacle avoidance, obstacle hurdling, walking on the inclined plane by using the proposed walking moton stabilization algorithm.

This study proposes a non-contact inspective technology based robot vision system for Faulty Inspection of welding States and Parts Shape. The maine focus is real time implementation of the machining parts' automatic inspection by the robotic moving. For this purpose, the automatic test instrument inspects the precision components designator the vision system. pattern Recognition Technologies and Precision Components for vision inspection technology and precision machining of precision parts including the status and appearance distinguish between good and bad. To perform a realization of a real-time automation integration system for the precision parts of manufacturing process, it is designed a robot vision system for the integrated system controller and verified the reliability through experiments. The main contents of this paper, the robot vision technology for noncontact inspection of precision components and machinery parts is useful technology for FA.