This paper describes our experimental study in hovering control of an unmanned helicopter with a 3D vision system instead of GPS (Global Position System). The aim of this study is to investigate methods to enable the normal operation of an autonomous helicopter when GPS does not function properly as is often experienced in cluttered environment. A 3D vision system together with a convex colored landmark is used for estimating the position of the vehicle. The estimated position is then fused with the acceleration from IMU (Inertial Measurement Unit) by Kalman filter for velocity estimation. The sensing strategy is validated by field experiments. The helicopter is stably controlled to hover within a 0.4 meter radius circle around the reference point. The hovering performance is equivalent to that when GPS is used. The good hovering performance indicates a possible application of vision system as an alternative of GPS in the case when GPS has trouble to provide reliable measurement.

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This study aims at establishing a robust and effective intelligent control method for nonlinear and complicated systems. In the method, a neural network for integrated control acquires suitable switching and integration of several controllers for a different local purpose by calculating the fitness function based on the system objective using the genetic algorithm. The proposed method is applied to an equilibrium point transfer and stabilization control of a double pendulum that possesses four equilibrium points. In order to verify the effectiveness of the proposed method, computational simulations and experiments using a real apparatus were carried out. As a result, it was demonstrated that the integrated intelligent controllers can transfer and stabilize the double pendulum from an arbitrary equilibrium point to a desired unstable equilibrium point without touching the cart position limit.

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The most common commercial pitching machines for baseball are the "arm" type and the "two rollers" type. These machines tend to have certain limitations. In particular, it is very difficult to simultaneously change both ball speed and direction. In addition, some types of pitches, such as the curveball or screwball, are not easily achieved. In this study, we will explain the hardware and software design of a new "intelligent" pitching machine which can pitch repeatedly with selectable speed, direction and ball rotation. The machine has three rollers and the motion of each is independently controlled by a hierarchical neural network. If the ball speed, direction and rotation are given as input data to this network, signals for controlling the three rollers are produced as output data. The results of a throw experiment with the machine that we developed are shown, which has the ability to pitch assorted breaking balls with a wide range of speeds, from 19.4 to 44.4 m/s. The machine has a speed error of less than about 3%, and a distance error of about 0.15m (twice the length of a ball's diameter).

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This paper proposes a nonverbal interface system using bio-potential signals, such as EOG and EMG, measured by a brain-computer interface and investigates its possibility of application to control of a hands-free manipulation system. A simple gesture recognition algorithm is presented to estimate the user's thinking from the EOG and EMG signals. To evaluate the feasibility and the characteristics of the interface system for hands-free manipulation, moving control experiments in 3D virtual space are carried out and the effectiveness of the proposed interface system is confirmed.

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To enhance ride comfort in the superconducting magnetically levitated transport (Maglev) system, vibrations were reduced by controlling the secondary suspension between the car body and bogie. To reduce vibrations at the relatively high characteristic frequencies of the primary suspension, attention has been directed toward control using damping forces output by a linear generator system integrated into a bogie for on-board power. Because this control can apply damping directly to the primary suspension, it is considered optimal in reducing high-frequency vibrations. Using a Maglev model focusing on vertical motions, this work describes the effectiveness of reducing vibrations using damping force control of the linear generator system for primary suspension and linear quadratic (LQ) control in the actuators for secondary suspension.

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An analytical approach is developed for determining the spacecraft attitude response to disturbances caused by the stick-slip motion of a large extended structure with a tension control mechanism. In this method, attitude dynamics with a flexible appendage is formulated as a general constrained mode model, and coupling with the stick-slip motion is formulated as the transition of equilibrium states of modal parameters. Numerical calculations are presented for the flexible solar array on the Advanced Earth Observing Satellite. This satellite experienced unexpected attitude oscillations due to the stick-slip motion while operating in orbit.

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This paper presents a novel architecture of six-degree-of-freedom (6-dof) parallel manipulator. The parallel manipulator consists of a moving platform, a fixed base, and six supporting constant-length limbs. Since the structure of parallel manipulator is a double equilateral triangle, it is called a slide equilateral triangle parallel manipulator (SETPM). The inverse and forward kinematics problems are described in closed forms and the velocity equation of the new parallel manipulator is given. Three kinds of singularities are presented. In determining the workspace of the manipulator, four types of kinematic constraints have been taken into account. The parallel manipulator has compact configuration and no singularities existed in the workspace. A prototype parallel manipulator was built base on the kinematics and analysis results.

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This is a study on the vibration characteristics of the translation and the inclination motions-coupled systems, which is the dynamical model of a train, a vehicle, and so on. It mainly investigates the nonlinear vibration of such a system which is subject to the base periodic displacement. The study observes the occurrence of the auto-parametric resonance at the resonance point of the translational motion, when the ratio between the natural frequencies of the inclinational motion and the translational motion is close to one-to-two. Moreover, the occurrence of an almost periodic motion is observed when this ratio is slightly different from one-to-two. The main discussion is based on the occurrence regions of the auto-parametric resonance and the almost periodic motion. This paper clarifies that the occurrence region of the auto-parametric resonance widely exists from about the ratio two-to-three to about the ratio one-to-three. It also notes on the influence of unbalance of the center of mass which produces slight linear coupling terms between the translational motion and the inclinational motion. The results of this paper indicate that the auto-parametric resonance is not the special phenomenon and it easily occurs. These theoretical results are confirmed by the experiments.

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This paper discussed the shaft-torsion and blade-bending coupling vibrations of a rotor system, in which the blades were grouped with lacing wires. Massless tension springs were used for modeling the lacing wires. An energy principle in conjunction with the assumed modes method was employed to yield the discrete equations of motion. The natural frequencies and the mode shapes of the system were solved for five- and six-blade cases as examples. Numerical results showed how the natural frequencies varied with the wire stiffness, connecting position, and the rotational speed. The diagrams of the coupling mode shapes and FRF's were drawn. From the results, it was found that lacing wire did not affect the SB (shaft-blades) coupling modes, but the BB (inter-blades) modes were indeed affected by the lacing wire. At moderate range of wire stiffness, the repeated BB modes split into more distinct modes. As expected, increasing the wire stiffness or connecting near outer edge would strengthen the system structure and increasing the natural frequencies of BB modes.

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Vibration signals of tool wear are proved to be non-stationary ones. They usually carry the dynamic information of tool wear and are very useful for tool wear condition recognition. The wavelet analysis is especially suitable for non-stationary signal processing and the artificial neural network is a very good tool for signal identification. In this paper, a new efficient tool wear monitoring method based on the wavelet packet transform and the artificial neural network was presented. The cutting vibration signals in different milling wear conditions are decomposed and reconstructed by using the wavelet packet transform and feature extraction with conditions information is obtained by using proper and scientific methods to extract from signals. Wavelet pretreatment distills feature information from original signal as input vectors of decision net in order to reduce input data dimension, optimize the net construction, compute complexity and decrease the decision the errors. The theoretical background of wavelet packet transform and grey relational degree analysis optimization radial based function network (RBFN) was given. The effect of cutting parameters on energy parameters was taken into account while extracting energy parameters of tool wear characteristic vector, this made the extracted parameters of characteristic vector be more sensitive to tool wear and the sensitivity to cutting parameters be the minimum. The recognition method for tool wear condition was studied through artificial neural network and wavelet packet analysis, and relevant RBFN was established. The results showed that RBFN can be successfully used to recognize and analyze tool wear conditions.

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