This paper presents a high-precision magnetically levitated (maglev) stage to meet demanding motion specifications in the next-generation precision manufacturing and nanotechnology. Characterization of dynamic behaviors of such a motion stage is a crucial task. In this paper, we address the issues related to the stochastic modeling of the stage including transfer function identification, and noise/disturbance analysis and prediction. Provided are test results on precision dynamics, such as fine settling, effect of optical table oscillation, and position ripple. To deal with the dynamic coupling in the platen, we designed and implemented a multivariable linear quadratic regulator, and performed time-optimal control. We demonstrated how the performance of the current maglev stage can be improved with these analyses and experimental results. The maglev stage operates with positioning noise of 5 nm rms in $\chi$ and y, acceleration capabilities in excess of 2g(20 $m/s^2$), and closed-loop crossover frequency of 100 Hz.

The parametric approaches to sliding mode design are newly proposed for the class of multivariable systems. Our approach is based on an explicit formula for representing all the slid-ing modes using the Lyapunov matrices of full order. By manipulating Lyapunov matrices, the sliding modes which satisfy the design criteria such as the quadratic performance optimization and robust stability to parametric uncertainty, etc., can be easily obtained. The proposed ap-proach enables us to adopt a variety of Lyapunov- (or Riccati-) based approaches to the sliding mode design. Applications to the quadratic performance optimization problem, uncertain systems, systems with uncertain state delay, and the pole-clustering problem are discussed.

It is well known that one can easily design a high-gain adaptive output feedback control for a class of nonlinear systems which satisfy a certain condition called output feedback exponential passivity (OFEP). The designed high-gain adaptive controller has simple structure and high robustness with regard to bounded disturbances and unknown order of the controlled system. However, from the viewpoint of practical application, it is important to consider a robust control scheme for controlled systems for which some of the assumptions of output feedback stabilization are not valid. In this paper, we design a robust high-gain adaptive output feedback control for the OFEP nonlinear systems with uncertain nonlinearities and/or disturbances. The effectiveness of the proposed method is shown by numerical simulations.

In this paper, the problem of designing an adaptive observer for a class of nonlinear systems with linear unknown parameters is studied. The nonlinear system to be considered consists of two blocks, only one of which has measurable states. Assuming the minimum-phase property of the error dynamics obtained after a change of coordinates and imposing some conditions on the functions multiplied by unknown parameters, an adaptive observer is constructed using an existing observer design method.

This paper proposes a simple, robust, nonlinear controller based on Lyapunov stability for tracking the reference welding path and velocity of a two-wheeled welding mobile robot (WMR). The system has three degrees of freedom including two wheels and one torch slider. Torch slider motion is used for faster tracking because the welding speed is very slow. Control law is obtained from the Lyapunov control function to ensure the asymptotical stability of the system. The controller has three free parameters for adjusting the performance of the controlled system. A simple way of measuring the errors using two potentiometers is introduced. The effectiveness of the proposed controller is shown through simulation results.

A modified command shaping control to reduce residual vibrations at a target position and to limit the sway angle of the payload during traveling for container crane systems is investigated. When the maneuvering time is minimized, a large transient amplitude and steady state oscillations may occur inherently. Since a large swing of the payload during the transfer is dangerous, the control objective is to transfer a payload to the desired place as quickly as possible while limiting the swing angle of the payload during the transfer. The conventional shapers have been enhanced by adding one more constraint to limit intermediate sway angles of the payload. The developed method is shown to be more effective than other conventional shapers for prevention of an excessive transient sway. Computer simulation results are provided.

In this paper, a novel estimation technique for a robust adaptive control scheme is presented for a class of uncertain nonlinear systems with a general set of uncertainty. For a class of introduced more extended semi-strict feedback forms which generalize the systems studied in recent years, a novel estimation technique is proposed to estimate the states of the fully nonlinear unmodeled dynamics without stringent conditions. With the introduction of powerful functions, the estimation error can be tuned to a desired small region around the origin via the estimator parameters. In addition, with some effective functions, a modified adaptive backstepping for dynamic uncertainties is presented to drive the output to an arbitrarily small region around the origin by an appropriate choice of the design parameters. With our proposed schemes, we can remove or relax the assumptions of the existing results.

This paper presents an effective trajectory planning algorithm for industrial robot manipulators. Given the end-effector trajectory in Cartesian space, together with the relevant constraints and task specifications, the proposed method is capable of planning the optimum end-effector trajectory. The proposed trajectory planning algorithm considers the joint acceleration limit, end-effector velocity limits, and trajectory allowance. A feedforward compensator is also incorporated in the proposed algorithm to counteract the delay in joint dynamics. The algorithm is carefully designed so that it can be directly adopted with the existing industrial manipulators. The proposed algorithm can be easily programmed for various tasks given the specifications and constraints. A three-dimensional test trajectory was planned with the proposed algorithm and tested with the Performer MK3s industrial manipulator. The results verified effective manipulator performance within the constraints.

This paper establishes some integral equalities formulated by zeros located in the convergence region of a Laplace transformable function. Using the definition of the Laplace transform, it shows that Laplace transformable functions have to satisfy the integral equalities in the time-domain, which can be applied to the understanding of the fundamental limitations on the control system represented by the transfer function. In the unity-feedback control scheme, another integral equality is derived on the output response of the system with open-loop poles located in the convergence region of the output function. From these integral equalities, two sufficient conditions related to undershoot and overshoot phenomena in the step response, respectively, are investigated.

The outdoor antenna servo system is subject to has significant torque disturbances from wind pressures and gusts on the antenna structures, as well as bearing and aerodynamic frictions. This control system should provide a sharp directivity in spite of the environmental disturbances and internal uncertainties. Therefore, the implementation of a real-time controller is necessary for the precise generation of the reference signal and robust tracking performance. In this paper, the discrete-time controller for the quick tracking of a target communication satellite is designed by applying the sampled-data $H_{\infty}$ control theory along with the reference signal generated by an improved conventional step-tracking algorithm. The sampled-data $H_{\infty}$controller demonstrates superior robustness for the longer sampling period when compared with a simple PID controller.

Accuracy in maneuvering target tracking using multiple models relies upon the suit-ability of each target motion model to be used. To construct multiple models, the interacting multiple model (IMM) algorithm and the adaptive IMM (AIMM) algorithm require predefined sub-models and predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers. To solve these problems, this paper proposes the GA-based IMM method as an intelligent tracking method for a maneuvering target. In the proposed method, the acceleration input is regarded as an additive process noise, a sub-model is represented as a fuzzy system to compute the time-varying variance of the overall process noise, and, to optimize the employed fuzzy system, the genetic algorithm (GA) is utilized. The simulation results show that the proposed method has a better tracking performance than the AIMM algorithm.

This paper introduces an identification method for nonlinear models in the form of rule-based Fuzzy-Neural Networks (FNN). In this study, the development of the rule-based fuzzy neural networks focuses on the technologies of Computational Intelligence (CI), namely fuzzy sets, neural networks, and genetic algorithms. The FNN modeling and identification environment realizes parameter identification through synergistic usage of clustering techniques, genetic optimization and a complex search method. We use a HCM (Hard C-Means) clustering algorithm to determine initial apexes of the membership functions of the information granules used in this fuzzy model. The parameters such as apexes of membership functions, learning rates, and momentum coefficients are then adjusted using the identification algorithm of a GA hybrid scheme. The proposed GA hybrid scheme effectively combines the GA with the improved com-plex method to guarantee both global optimization and local convergence. An aggregate objective function (performance index) with a weighting factor is introduced to achieve a sound balance between approximation and generalization of the model. According to the selection and adjustment of the weighting factor of this objective function, we reveal how to design a model having sound approximation and generalization abilities. The proposed model is experimented with using several time series data (gas furnace, sewage treatment process, and NOx emission process data from gas turbine power plants).

This paper proposes a two dimensional behavior analysis method for fish in a water tank based on the ARX method and the Kalman filter algorithm using image processing technology. In modeling the behavior of fish, the input is denoted as the environmental change and uses M-sequence. The output is expressed by the partnership between fish. The behavior model of individual fish is identified by the ARX method. It is then estimated by the Kalman filter algorithm. Finally, the fish behavior is analyzed by FFT. To prove the effectiveness of the pro-posed algorithm, it is applied to two tilapias in a water tank with dimensions of 100cm$\times$100cm$\times$50cm. The effectiveness of the proposed method is demonstrated through ARX identification, estimation of Kalman filter, and FFT analysis.

The problem of tracking moving objects in a video stream is discussed in this pa-per. We discussed the popular technique of optical flow for moving object detection. Optical flow finds the velocity vectors at each pixel in the entire video scene. However, optical flow based methods require complex computations and are sensitive to noise. In this paper, we proposed a new method based on the Hough transform and on voting accumulation for improving the accuracy and reducing the computation time. Further, we applied the Boo-lean based edge detector for edge detection. Edge detection and segmentation are used to extract the moving objects in the image sequences and reduce the computation time of the CHT. The Boolean based edge detector provides accurate and very thin edges. The difference of the two edge maps with thin edges gives better localization of moving objects. The simulation results show that the proposed method improves the accuracy of finding the optical flow vectors and more accurately extracts moving objects' information. The process of edge detection and segmentation accurately find the location and areas of the real moving objects, and hence extracting moving information is very easy and accurate. The Combinatorial Hough Transform and voting accumulation based optical flow measures optical flow vectors accurately. The direction of moving objects is also accurately measured.

This paper proposes a new numerical method to check the stability of a general class of time delay systems. The proposed method checks whether there are characteristic roots whose real values are nonnegative through two steps. Firstly, rectangular bounds of characteristic roots whose real values are nonnegative are computed. Secondly, the existence of roots inside the bounds are checked using the numerical computation of argument principles. An adaptive discretization is proposed for the numerical computation of argument principles.

A major research issue associated with service robots is the creation of an environment recognition system for mobile robot navigation that is robust and efficient on various environment situations. In recent years, intelligent autonomous mobile robots have received much attention as the types of service robots for serving people and industrial robots for replacing human. To help people, robots must be able to sense and recognize three dimensional space where they live or work. In this paper, we propose a three dimensional environmental modeling method based on an edge enhancement technique using a planar fitting method and a neural network technique called "Growing Neural Gas Network." Input data pre-processing provides probabilistic density to the input data of the neural network, and the neural network generates a graphical structure that reflects the topology of the input space. Using these methods, robot's surroundings are autonomously clustered into isolated objects and modeled as polygon patches with the user-selected resolution. Through a series of simulations and experiments, the proposed method is tested to recognize the environments surrounding the robot. From the experimental results, the usefulness and robustness of the proposed method are investigated and discussed in detail.in detail.

Reinforcement learning is considered as an important tool for robotic learning in unknown/uncertain environments. In this paper, we propose an evaluation function expressed in a vector form to realize multi-dimensional reinforcement learning. The novel feature of the proposed method is that learning one behavior induces parallel learning of other behaviors though the objectives of each behavior are different. In brief, all behaviors watch other behaviors from a critical point of view. Therefore, in the proposed method, there is cross-criticism and parallel learning that make the multi-dimensional learning process more efficient. By ap-plying the proposed learning method, we carried out multi-dimensional evaluation (reward) and multi-dimensional learning simultaneously in one trial. A special neural network (Q-net), in which the weights and the output are represented by vectors, is proposed to realize a critic net-work for Q-learning. The proposed learning method is applied for behavior planning of mobile robots.

In this study, we introduce the main ideas on the control and strategy used by the robot soccer team of the Universidad de Buenos hires, UBA-Sot. The basis of our approach is to obtain a cooperative behavior, which emerges from homogeneous sets of individual behaviors. Except for the goalkeeper, the behavior set of each robot contains a small number of individual behaviors. Basically, the individual behaviors have the same core: to move from the initial to-ward the target coordinates. However, these individual behaviors differ because each one has a different precondition associated with it. Each precondition is the combination of a number of elementary ones. The aim of our approach is to answer the following questions: How can the robot compute the preconditions in time\ulcorner How are the control actions defined, which allow the robot to move from the initial toward the final coordinates\ulcorner The way we cope with these issues is, on the one hand, to use ball and robot predictors and, on the other hand, to use very fast planning. Our proposal is to use planning in such a way that the behavior obtained is closer to a reactive than a deliberative one. Simulations and experiments on real robots, based on this approach, have so far given encouraging results.

An overview of our approach to autonomous navigation is presented showing how motion information can be integrated into existing navigation schemes. Particular attention is given to our short range motion estimation scheme which utilises a number of unique assumptions regarding the nature of the visual environment allowing a direct fusion of visual and range information. Graduated non-convexity is used to solve the resulting non-convex minimisation problem. Experimental results show the advantages of our fusion technique.