This paper deals with speed control of DC servo motor using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm. Conventionally a PID controller has been used in the industrial control. But a PID controller should produce suitable parameters for each system. Also, variables of the PID controller should be changed according to environments, disturbances and loads. In this paper described by a experiment that contained a method using a PID controller with a gain tuning based on a Back-Propagation(BP) Learning Algorithm, we developed speed characteristics of a DC servo motor on variable loads. The parameters of the controller are determined by neural network performed on on-line system after training the neural network on off-line system.

An automatic train operation(ATO) system executes the operation of constant speed travelling and fixed point parking by using microprocessors instead of driver's manual operation. This paper describes the mathematical model for the train considering unknown disturbances which consist of start resistance, travelling resistance, slope resistance, curve resistance, and so on. The speed controller of ATO system is designed by considering the disturbances. The simulation is executed to verify the speed control and fixed point parking performance and to compare its performance with that of a PID-type ATO control system under disturbances. Simulation results show that the control performance of gain scheduled control scheme for ATO system is better than that of the conventional PID controller.

A two-degree-of-freedom quarter-car model is used as the basis for LQ and the proposed controller design for a semi-active suspension. The LQ controller results in the best rms performance trade-offs(as defined by performance index) between ride, handling and packaging requirements. In LQ controller, however, the conflict between road holding and ride comfort remains. The adaptive semi-active suspension control based on the road frequency are introduced in this paper. With this method, the trade-off between road holding and ride comfort can be relaxed. The road frequency is estimated by wavelet transform if rattle space signal. The simulation results show that the proposed controller is superior to the conventional LQ controller.

In this paper, an observer-based adaptive controller is proposed to control the longitudinal motion of vehicles. The standard gradient method will be used to estimate the vehicle parameters, mass, time constant, etc. The nonlinear model between the driving force and the vehicle acceleration will be chosen to design the state observer for the vehicle velocity and acceleration. It will be shown that the proposed observer is exponentially stable, and that the adaptive controller proposed on this paper is stable. It will be proved that the errors of the relative distance, velocity and acceleration converge to zero asymptotically fast, and that the overall system is also asymptotically stable. The simulation results are presented to investigate the effectiveness of the proposed method.

In this research a Simulink/RTW-baed realtime control system was developed for an induction motor vector control. On the Simulink window, the control system is designed in the form of block diagrams, program codes are produced automatically with the RTW(Real Time Workshop), then an DSP c compiler compiles the program codes. With this automatic program producing method rapid prototyping is realized with the least time-consuming manual programming procedures. To show effectiveness of the proposed system designing scheme a DSP-based induction motor vector controller was constructed and implemented.

A lot of robot trajectory tracking methods are proposed to enhance the tracking error, but irregular tracking errors are always accompanied and very hard to reduce it. Up to now, these irregular tracking errors are reduced by introducing more complicated control algorithms. But, it is intuitively obvious to reduce only the big errors selectively in the irregular ones for the better performance instead of using more complicated control algorithms. By the characteristics of the robot, big tracking errors of the end-effector are generated mostly due to the fast moving of joint. So, in this paper, we introduce a new method which reduce the big tracking errors by clippings the joint velocity with the constraint of given path. Using this method, desired trajectory tracking is obtained within the far reduced error bound. Also, this method is successfully applied to generate the path-constrained error reducing trajectories for 2-axis SCARA type robot.

In the video retrieval system, visual clues of still images and motion information of video are employed as feature vectors. We generate the temporal textures to express the motion information whose properties are simple expression, easy to compute. We make those temporal textures of wavelet coefficients to express motion information, M components. Then, temporal texture feature vectors are extracted using spatial texture feature vectors, i.e. spatial gray-level dependence. Also, motion amount and motion centroid are computed from temporal textures. Motion trajectories provide the most important information for expressing the motion property. In our modeling system, we can extract the main motion trajectory from the temporal textures.