In this paper, an adaptive robust nonlinear predictive controller is developed for the continuous time nonlinear systems whose control objective is composed of the system output and its desired value. The basic control law is derived from the continuous time prediction model and its feedback dynamcis shows another from if input and output linearization. In order to cope with the parameter uncertainty, robust control is incorporated into the basic control law and the asymptotic convergence of tracking error to a certain bounded region is guaranteed. For stability and performance improvement within the bounded region, an adaptive control is introduced. Simulation tests for the motion control of an underwater wall-ranging robot confirm the performance improvement and the robustness of this controller.

Sliding mode control guarantees robustness in the presence of modeling uncertainties and external disturbances. However, this can be obtained at the cost of high control activity that may lead to chattering As one way to alleviate this problem a boundary layer around sliding surface is typically used. In this case the selection of controller gain, control ban width and boundary layer thickness is a crucial problem for the trade-off between tracking error and chattering. The parameter tuning is usually done by trail-and-error in practice causing significant effort and time. An auto tuning method based on fuzzy rules is proposed in the paper in this method tracking error and chattering are monitored by performance indices and the controller tunes the design parameters intelligently in order to compromise both indices. To demonstrate the efficiency of the propose method a mass-spring translation system and a roboic control system are simulated and tested It is shown that the proposed algorithm is effective to facilitae the parameter tuning for sliding mode controllers.

When robot manipulatros as mathematically modeled. uncetainties may not be avoided. The uncertain factors come from imperfect knowledge of system parameters, payload change. friction, external disturbance and etc. In this work, we proposed a class of robust hybrid control of manipulatosrs. We propose a class of expanded robust hybrid control with the separated bound function and the simulation results are provided to show the effectiveness of the algorithm.

This paper proposes a self-organizing fuzzy modeling(SOFUM)which an create a new hyperplane shaped cluster and adjust parameters of the fuzzy model in repetition. The suggested algorithm SOFUM is composed of four steps: coarse tuning. fine tuning cluster creation and optimization of learning rates. In the coarse tuning fuzzy C-regression model(FCRM) clustering and weighted recursive least squared (WRLS) algorithm are used and in the fine tuning gradient descent algorithm is used to adjust parameters of the fuzzy model precisely. In the cluster creation, a new hyperplane shaped cluster is created by applying multiple regression to input/output data with relatively large fuzzy entropy based on parameter tunings of fuzzy model. And learning rates are optimized by utilizing meiosis-genetic algorithm in the optimization of learning rates To check the effectiveness of the suggested algorithm two examples are examined and the performance of the identified fuzzy model is demonstrated via computer simulation.

Configuration-dependent nonlinear coefficient matrices in the dynamic equation of robot manipulator impose computa- tional burden in real-time implementation of tracking control based on the inverse dynamics controller. However, parallel manipulators such as Stewart platform have relatively small workspace compared to serial manipulators. Based on the characteristics of small motion range. nonlinear coefficient matrices can be approxiamted to constant ones. The modeling errors caused by such approximation are compensated for by H-infinity controller that treats the modeling errors disturbance. The proposed inverse dynamics controller with approximate dynamics combined with H-infinity control shows good tracking performance even for fast tracking control in which computation of full inverse dynamics is not easy to implement.

This paper presents a new camera calibration algorithm for ill-conditioned cases in which the camera plane is nearly parallel to a set of non-coplanar calibration boards. for the ill-conditioned case, most of existing calibration approaches such as Tsais radial-alignment-constraint method cannot be applied. Recently, for the ill-conditioned coplanar calibration Lee&Lee[16] proposed an iterative algorithm based on the least square method. The non-coplanar calibration algorithm presented in this paper is an iterative two-stage procedure with extends the previous coplanar calibration algorithm. Through the first stage, camera, position and orientation parameters as well as one radial distortion factor are determined optimally for a given data of the scale factor and the focal length. In the second stage, the scale factor and the focal length are locally optimized. This process is repeated until any improvement cannot be expected any more Computational results are provided to show the performance of the algorithm developed.

The ladder diagram (LD) for programmable logic controllers (PLCs) ar responsible for much important roles in advance industrial automation. As automated systems become more complex the design procedures of the system become more difficult as well. Hence. the design automation issues based on discrete event models(DEMs) are receiving more attention. One of the popular ways of tackling these problems is employing Petri nets. In this paper, we use the modified automation Petri net(MAPN) to model the manufacturing system and the modified token passing logic (MTPL) method conversion (ECC) algorithm based on the MAPN and the MTPL Finally, an example of the manufacturing system is provided to illustrate the proposed ECC algorithm.

error behaviour can be considered as a linear combination of low amplitude random noise and abrupt jumps. The reason of jump appearance can be explained by the semi-shading effects(buildings, trees), jamming, high dynamic of vehicle and so on. This study describes the stand-alone GPS error jump smoothing algorithm which is developed based on the scalar adaptive filter. The algorithm consists of the coarse jump smoothing and the fine jump smoothing. On the coarse smoothing step, GPS velocities or position differences are used as the measurement for the scalar adaptive filter. The purpose of adaptive filter is to smooth the jump errors. The coarse positions are detennined by the integration of smoothed velocities. On the fine smoothing step, the differences between GPS positions and the coarse positions are smoothed by another scalar adaptive filter. The reason of fine smoothing is based on the facts that smoothing accuracy depends on the variance ofusefuJ signa\. The coarse smoothing which deal with the difference of positions provides the rough error removing. So the coarse smoothed velocities can have much more low amplitude than the raw ones. The fine smoothing procedure provides high quality of filtering process. Simulation results show the efficiency of proposed scheme.

In this paper, a horizontal LOS(line of sight) rate estimator for conventional sea skimming ASM(anti-ship missile) is proposed. A LOS rate dynamics model for a 2-axis gimbal system and the homing geometry is derived. A new LOS rate estimator is proposed by applying the Kalman filter theory to the LOS rate dynamics model. The proposed filter estimates LOS rates by taking roll motions into account. Simulation results show that the proposed filter produces smaller estimation errors than a conventional method.

In this paper, a robust recursive control approach for nonlinear system, which is based on Lyapunov stability, is proposed. The proposed method can apply to extended systems including cascaded systems and the stability is guaranteed in the sense of Lyapunov. The recursive design procedure so called “robust recursive control approach” is used to find a stabilizing robust controller and simultaneously estimate the uncertainty parameters. First, a nonlinear model with uncertainties whose bounds are unknown is derived. Then, unknown bounds of uncertainties are estimated. By using these estimates, the stabilizing robust controller is updated at each step. This approach is applied to the pitch autopilot design of a nonlinear missile system and simulation results indicate good performance.

It is well-known that positioning accuracy can be improved by the use of carrier phase of GPS up to the centimeter level. In order to obtain good accuracy, we need to know integer ambiguity in the carrier phase accurately. In this paper, we propose a fast ambiguity determination method(FADM) which combines Kalman filtering and the search method, and show the improvement of the positioning performance by static and kinematic simulation compared with known methods such as Kalman filtering, LSAST (Least Squares Ambiguity Search Technique), ARCE(Ambiguity Resolution with Constratint Equation), LLL(Lenstra, Lenstra, and Lovasz) algorithms.

This paper presents firstly the method of Euler angle matching designing the transfer alignment using the attitude matching. In this method, the observation directly uses Euler angle difference between MINS and SINS so it needs to describe the rotation vector error to the Euler angle error. The rotation vector error related to the Euler angle error is derive from the direction cosine matrix error equation. The feasibility of the Kalman filter designed for the transfer alignment by Euler angle matching is analyzed by the alignment error results with respect to the roll angle the pitch angle, and the yaw angle matching.