In this paper a digital-control algorithm of a power system suing a high-speed PWM for a MRI system is proposed. The MRI system requires an elaborate ladder-shaped current source. And the load of current source is the inductance with resistance. For the inductive load a voltage output of the power system has hi호 frequency components. Therefore this system requires high-speed PWM above 80KHz, A high speed PWM control algorithm which satisfies those conditions is designed. Finally the performance of proposed control algorithm is shown by simulation.

The reference model of an MRC (model reference control) provides the desired trajectory a plant should follow and thus the design of a reference model has a significant effect on control performance. In most control systems control input to a plant has some bounds and it is preferable to make use of as large control inputs as possible within the range of no saturation. In this paper a new approach of selecting the reference model is proposed for bounded control inputs. Design variables of the reference model are determined in such a way that maximizes the performance index within the range of no saturation. Moreover this variable reference model is regularly updated during control. This scheme is verified by application to the servo motor position control system in various simulations. The responses of the MRC with a variable reference model show better tracking performance than that with a fixed reference mode. Moreover by adjusting the update interval of the reference model the control performance can be further improved.

Whereas the linear PID controller is widely used for control of industrial servo systems a high precision positioning system is not easy to control only with the PID controller due to uncertain nonlinear dynamics such as friction backlash etc. As a viable means to overcome the difficulty a learning control scheme is proposed in this paper that is simple and straightforward to implement. The proposed learning controller takes full advantage of current feedback capability of the inner-loop of the control system in that electrical motor dynamics as the well as mechanical part of X-Y positioning system is included in the learning control scheme, The experimental results are given to demonstrate its feasibility and effectiveness in terms of convergence precision of tracking and robustness in comparison with the conventional control method.

This paper proposes a new sliding mode controller combined with a multi-layer neural network using the error back propagation learning algorithm,, The network acts as a compensator of the conventional sliding mode controller to improve the control performance when initial assumptions of uncertainty bounds of system parameters are violated. The proposed controller can reduce th steady state error of conventional sliding mode controller with the boundary layer technique Computer simulation results show that the proposed method is effective to control dynamic systems with unexpectably large uncertainties.

In this paper we give some geometric condition for a stochastic nonlinear system and we propose a control method for a stochastic nonlinear system using neural networks. Since a competitive learning neural networks has been developed based on the stochastcic approximation method it is regarded as a stochastic recursive filter algorithm. In addition we provide a filtering and control condition for a stochastic nonlinear system called the perfect filtering condition in a viewpoint of stochastic geometry. The stochastic nonlinear system satisfying the perfect filtering condition is decoupled with a deterministic part and purely semi martingale part. Hence the above system can be controlled by conventional control laws and various intelligent control laws. Computer simulation shows that the stochastic nonlinear system satisfying the perfect filtering condition is controllable and the proposed neural controller is more efficient than the conventional LQG controller and the canonical LQ-Neural controller.

In this paper we suggest an optimal design method of Fuzzy-Neural Networks(FNN) model for complex and nonlinear systems. The FNNs use the simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rule. And we use a HCM(Hard C-Means) Clustering Algorithm to find initial parameters of the membership function. The parameters such as parameters of membership functions learning rates and momentum weighted value is proposed to achieve a sound balance between approximation and generalization abilities of the model. According to selection and adjustment of a weighting factor of an aggregate objective function which depends on the number of data and a certain degree of nonlinearity (distribution of I/O data we show that it is available and effective to design and optimal FNN model structure with a mutual balance and dependency between approximation and generalization abilities. This methodology sheds light on the role and impact of different parameters of the model on its performance (especially the mapping and predicting capabilities of the rule based computing). To evaluate the performance of the proposed model we use the time series data for gas furnace the data of sewage treatment process and traffic route choice process.

The measurement of electric current using optical techniques provides a number of important practical advantages including effective isolation from high line potentials and freedom from the saturation effects observed in conventional current transformers. In this work the optical current sensor which uses thin YIG(Yittrium Ion Garnet) film as a Faraday element has been developed. We have characterized this optical current sensor and implemented it to the OOCR(Opto-electronic OverCurrent Realy) Performance of the OOCR shows the possibility of replacing the present overcurrent relays with OOCR.

This paper presents an attitude determination algorithm for the local level strapdown inertial navigation where the body the earth and the transport rate can be calculated separately using a DCM computation scheme, Also presented is a velocity determination algorithm taking into account the attitude variation caused by he change of the navigation frame. The proposed algorithm are implemented using three samples of the velocity and the angle within the integration interval.

In this paper a new two-arm cooperative assembly(or insertion) algorithm is proposed. As a force-guided control method for the cooperative assembly the adaptive accommodation controller is adopted since it does not require any complicated contact state analysis nor depends of the geometrical complexity of the assembly parts. Also the RMRC(resolved motion rate control) method using a relative jacobian is used to solve inverse kinematics for two manipulators. By using the relative jacobian the two cooperative redundant manipulators can be formed as a new single redundant manipulator. Two arms can perform a variety of insertion tasks by using a relative motion between their end effectors. A force/torque sensing model using an approximated penetration depth calculation a, is developed and used to compute a contact force/torque in the graphic assembly simulation . By using the adaptive accommodation controller and the force/torque sensing model both planar and a spatial cooperative assembly tasks have been successfully executed in the graphic simulation. Finally through a cooperative assembly task experiment using a humanoid robot CENTAUR which inserts a spatially bent pin into a hole its feasibility and applicability of the proposed algorithm verified.

A coordination method for a macro/micro robot with separate controllers is proposed and evaluated. The macro/micro robot system generally has independent controllers for the macro and the micro robot respectively. A controller for the coordination of the macro and the micro robot has been designed based on the stable independent controller of each system. The method and trajectory generation method is also proposed to track the moving desired position rapidly. The control method and trajectory generation method is also proposed to track the moving desired position rapidly. The control strategy has been implemented to the macro/micron robot system to evaluate the performance. The experimental results show that the proposed method for maintaining the micro robot within its workspace has uniform performance over the various range of the bandwidth and the proposed trajectory generator is shown to be efficient.

This paper presents an effective approach to collision-free motion planning of a robot in the work-space including time-varying obstacles. The free arc is defined as a set composed of the configuration points of the robot satisfying collision-free motion constraint at each sampling time. We represent this free arc with respect to the new coordinate frame centered at the goal configuration and there for the collision-free path satisfying motion constraint is obtained by connecting the configuration points of the free arc at each sampling mined from the sequence of free arcs the optimality is determined by the performance index. Therefore the complicated collision-free motion planning problem of a robot is transformed to a simplified SUB_Optimal Collision Avoidance Problem(SOCAP). We analyze the completeness of the proposed approach and show that it is partly guaranteed using the backward motion. Computational complexity of our approach is analyzed theoretically and practical computation time is compared with that of the other method. Simulation results for two cally and practical computation time is compared with that of the other method. Simulation results for two SCARA robot manipulators are presented to verify the efficacy of the proposed method.

This study describes a pressure tracking control of hydroforming process which is used for precision forming of sheet metals. The hydroforming operation is performed in the high-pressure chamber strictly controlled by pressure control valve and by the upward motion of a punch moving at a constant speed, The pressure tracking control is very difficult to design and often does not guarantee satisfactory performances be-cause of the punch motion and the nonlinearities and uncertainties of the hydraulic components. To account for these nonlinearities and uncertainties of the process and iterative learning controller is proposed using Cerebellar Model Arithmetic Computer (CMAC). The experimental results show that the proposed learning control is superior to any fixed gain controller in the sense that it enables the system to do the same work more effectively as the number of operation increases. In addition reardless of the uncertainties and nonlinearities of the form-ing process dynamics it can be effectively applied with little a priori knowledge abuot the process.