In this paper, we propose a method to apply the Input Shaping Technique (IST) to multi-input multi-output (MIMO) systems. In MIMO systems, there is a high possibility of multi-mode residual vibration. The IST filter designed for this multi mode may need a longer time to suppress the residual vibration. Previous works prove that we can shorten the time lag by using negative sequence. This negative sequence, however, causes another problem - it requires excessive control input. In this paper, we provide a remedy to reduce the size of control input by limiting the reference input by limiting the reference input and its derivative. The result of simulations and experiments on a 2 link flexible arm confirmed the effectiveness of the proposed method.

In this paper, a discrete-time variable structure control method using recursively defined switching function and a decoupled variable structure disturbance compensator is used to achieve high performance circular motion control of a CNC machining center. The discrete-time variable structure control with the decoupled disturbance compensator method developed in this paper uses a recursive switching function defined as the sum of the current tracking error vector and the previous value of the switching function multiplied by a positive constant less than one. This recursive switching function provides much improved performance compared to the method that uses a switching function defined only as a linear combination of the current tracking error. Enhancements in tracking performance are demonstrated in the circular motion control using a CNC milling machine.

In this paper, a new genetic algorithm is proposed for solving multimodal function optimization problems that are not easily solved by conventional genetic algorithm(GA)s. This algorithm finds one of local optima first and another optima at the next iteration. By repeating this process, we can locate all the local solutions instead of one local solution as in conventional GAs. To avoid converging to the same optimum again, we devise a new genetic operator, called a Mendel operator which simulates the Mendels genetic law. The proposed algorithm remembers the optima obtained so far, compels individuals to move away from them, and finds a new optimum.

A nonlinear adaptive flight control system is proposed using a backstepping controller with neural network controller. The backstepping controller is used to stabilize all state variables simultaneously without the two-timescale assumption that separates the fast dynamics, involving the angular rates of the aircraft, from the slow dynamics which includes angle of attack, sideslip angle, and bank angle. It is assumed that the aerodynamic coefficients include uncertainty, and an adaptive controller based on neural networks is used to compensate for the effect of the aerodynamic modeling error. It is shown by the Lyapunov stability theorem that the tracking errors and the weights of neural networks exponentially converge to a compact set. Finally, nonlinear six-degree-of-freedom simulation results for an F-16 aircraft model are presented to demonstrate the effectiveness of the proposed control law.

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). An immune system is the living bodys self-protection and self-maintenance system. these features can be applied to decision making of the optimal swarm behavior in a dynamically changing environment. For applying immune system to DARS, a robot is regarded as a B-cell, each environmental condition as an antigen, a behavior strategy as an antibody, and control parameter as a T-cell, respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robots using communication (immune network). Finally, much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of the optimal swarm strategy. Adaptation ability of the robot is enhanced by adding T-cell model as a control parameter in dynamic environments.

To realize a visual servo control of slender manipulators, two problems to be solved are analysed. The stability problem on so-called noncolocation control and the infinite order problem of the real Jacobian matrix caused by the elastic deformation are discussed. By considering the dynamic relations between rigid and elastic modes, a Jacobian operator is derived and the physical meaning is also explained. Then, for practical control, a simple control scheme using an approximate Jacobian is proposed and its stable conditions are proven by means of the $L_$2$ stability theory. The scheme is structurally similar to the conventional PD control laws, but external sensors(e. g. visual sensor) are used for positioning and internal sensors for damping. A good performance is obtained via control experiments of a slender two link manipulator.

This paper presents an efficient algorithm for including the kinematic calibration data into the motion controller to improve the positioning accuracy of the manipulators. Rather than spending several iterations for finding the inverse solution of the calibrated kinematics, our approach requires only the nominal inverse solution and the calibrated forward kinematics for providing a better position command promptly. Thus, real-time application is guaranteed whenever the manipulators nominal inverse solution can be expressed in a closed form. Experimental results show that the line tracking performances can be remarkably improved by employing our algorithm.

The interactive bicycle simulator presented in this paper consists of a Stewart platform manipulator, magneto-rheological steering and braking devices, and a visual simulator. To provide a rider with reality, these devices should be controlled in real-time and motions of the devices and the visual should be also synchronized. If any of the devices and the visual gets unsynchronized due to significant blocking of control signals, the reality of the simulator is no longer secured. This paper presents communication protocols that minimize the blocked time of the control processes to guarantee the synchronization. The protocols are designed based on IPC (InterProcess Communications) of QNX, TCP/IP, and serial communication. The performance of the designed communication protocols is evaluated with the implemented bicycle simulator, and found satisfactory.

In this paper, we present a vision system which measures the cross-section of a hot wire-rod in the steel plant. We developed a mobile vision system capable of accurate measurement, which is strong to vibration and jolt when moving. Our system uses green laser light sources and CCD cameras as a sensor, where laser sheet beams form a cross-section contour on the surface of the hot wire-rod and the reflected light from the wire-rode is imaged on the CCD cameras. We use four lasers and four cameras to obtain the image with the complete cross-section contour without an occlusion region. We also perform camera calibrations to obtain each cameras physical parameters by using a single calibration pattern sheet. In our measuring algorithm, distorted four-camera images are corrected by using the camera calibration information and added to generate an image with the complete cross-section contour of the wire-rod. Then, from this image, the cross-section contour of the wire-rod is extracted by preprocessing and segmentation, and its height, width and area are measured.

To operate a process plant safely and economically, process monitoring is very important. Process monitoring is the task to identify the state of the system from sensor data. Process monitoring includes data acquisition, regulatory control, data reconciliation, fault detection, etc. This research focuses on the data recon-ciliation using scale-space filtering and fault detection using functional-link associative neural networks. Scale-space filtering is a multi-resolution signal analysis method. Scale-space filtering can extract highest frequency factors(noise) effectively. But scale-space filtering has too large calculation costs and end effect problems. This research reduces the calculation cost of scale-space filtering by applying the minimum limit to the gaussian kernel. And the end-effect that occurs at the end of the signal of the scale-space filtering is overcome by using extrapolation related with the clustering change detection method. Nonlinear principal component analysis methods using neural network have been reviewed and the separately expanded functional-link associative neural network is proposed for chemical process monitoring. The separately expanded functional-link associative neural network has better learning capabilities, generalization abilities and short learning time than the exiting-neural networks. Separately expanded functional-link associative neural network can express a statistical model similar to real process by expanding the input data separately. Combining the proposed methods-modified scale-space filtering and fault detection method using the separately expanded functional-link associative neural network-a process monitoring system is proposed in this research. the usefulness of the proposed method is proven by its application a boiler water supply unit.

This paper presents a technique for estimating the attitude of a model helicopter at near hovering using a combination of inertial and non-inertial sensors such as gyroscope and potentiometer. To estimate the attitude of helicopter a simplified indirect Kalman filter based on sensor modeling is derived and the characteristics of sensors are studied, which are used in determining the optimal Kalman gain. To verify the effectiveness of the proposed algorithm simulation results are presented with real flight data. Our approach avoids a complex dynamic modeling of helicopter and allows for an elegant combination of various sensor data with different measurement frequencies. We also describe the method of implementation of the algorithm in the model helicopter.

In this paper we propose an angular rate and acceleration matching method for initial transfer alignment in aircraft. The conventional angular rate and acceleration matching method performs compensation for the lever arm effects between the master and slave INS before initial alignment. However, the conventional method does not take the flexure angular acceleration into account and thus is not effective when the flexure angular acceleration is large. We propose a new angular rate and acceleration matching method to cope with the flexure acceleration between the master and slave INS and compare the results with those of the conventional method by simulation. The simulation results show that the proposed matching method is better than the conventional matching method in case of large flexure acceleration.

In this paper a model following flight control system design using the discrete time quasi-sliding mode control method is described. The quasi-sliding mode is represented as the sliding mode band, not as the sliding surface. The quasi-sliding mode control is composed of the equivalent control for the nominal system without uncertainties and disturbances and the additive control compensating the uncertainties and disturbances. The linearized plant on the equilibrium point is used in designing a flight control system and the stability conditions are proposed for the model uncertainties. Pseudo-state feedback control which uses the model variables for the unmeasured states is proposed. The proposed method is applied to the design of the roll attitude and pitch load factor control of a bank-to-turn missile. The performance is verified through the nonlinear six degrees of freedom flight simulation.