Penalty Kick of a Humanoid Robot by a Neural-Network-Based Active Embedded Vision System

[[abstract]]This paper realizes the humanoid robotic system to execute the penalty kick (PK) of the soccer game. The proposed system includes the following three subsystems: a humanoid robot (HR) with 22 degree-of-freedom, a soccer with different colors, and a soccer gate. In the beginning, the HR scans the soccer field to find the gate and the soccer, which are randomly distributed in a specific region in the front of the gate. If a command for the PK of the soccer with specific color is assigned, the HR will be navigated by an active embedded vision system (AEVS). After the HR reaches a planned position and posture, the PK of the HR will be executed. Two key important techniques are developed and integrated into the corresponding task. One is the modeling using multilayer neural network (MNN) for different view angles, the other is the visual navigation strategy for the PK of the HR. In addition, the error sensitivities in the pan and tilt directions of these four visible regions are analyzed and compared. The proposed strategy of the visual navigation includes the following two parts: (i) the switched visible regions are designed to navigate the HR to the planned position, and (ii) the posture revision of the HR in the neighborhood of the soccer in order to execute the PK. Finally, a sequence of experiments for the PK of the HR confirm the effectiveness and efficiency of the propose methodology.[[conferencetype]]國際[[conferencelocation]]Taipei, Taiwa

Fuzzy sliding-mode under-actuated control for autonomous dynamic balance of an electrical bicycle

[[abstract]]The purpose of this paper is to stabilize the running motion of an electrical bicycle. In order to do so, two strategies are employed in this paper. One is to control the bikepsilas center of gravity (CG), and the other is to control the angle of the bikepsilas steering handle. In addition, the proposed system produces three outputs that will affect the dynamic balance of an electrical bicycle: the bikepsilas pendulum angle, lean angle, and steering angle. Based on the data of input-output, two scaling factors are employed to normalize the sliding surface and its derivative. According to the concept of if-then rule, an appropriate rule table for the ith subsystem is obtained. Then the output scaling factor based on Lyapunov stability is determined. The proposed control method used to generate the handle torque and pendulum torque is called fuzzy sliding-mode under-actuated control (FSMUAC). The purpose of using the FSMUAC is the huge uncertainties of a bicycle system often caused by different ground conditions and gusts of wind; merely ordinary proportional-derivative-integral (PID) control method or other linear control methods usually do not show good robust performance in such situations.[[incitationindex]]SCI[[incitationindex]]EI[[booktype]]紙

Designs of Servomechanism and Dynamic Sensing System for Autonomous Dynamic Balance of Humanoid Robot

[[abstract]]The main theme of this paper is focused on the designs of servomechanism and dynamic sensing system for the autonomous dynamic balance of humanoid robot. The servomechanism design includes the following four parts: (i) the lag mechanism with 7 degree-of-freedom (DOF), (ii) the hand mechanism with 4 DOF, (iii) the head and body mechanism with 2 DOF, and (iv) the driving circuit of servomotor. Most important subjects are the improvement of servo stiffness and the passive dynamic walking and running using springs. In addition, the dynamic sensing system includes the following four parts: (i) three axes accelerometer transmission module, (ii) the force detecting module, (iii) the gyro detecting module, and (iv) the decoder module of servomotor. Based on the above designs, the architecture of the autonomous dynamic balance of humanoid robot is achieved.[[sponsorship]]Chinese Automatic Control Society (CACS)[[conferencetype]]國際[[conferencedate]]20081121~20081123[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]臺南, 臺

Fuzzy mixed H2-H-infinity optimization-based decentralized modelreference control and application to piezo-driven XY table systems

[[notice]]本書目待補正[[incitationindex]]SC

Fuzzy-neural-based control for nonlinear time-varying delay systems

[[abstract]]In this paper, a partially known nonlinear dynamic system with time-varying delays of the input and state is approximated by fuzzy-based linear subsystems described by a state-space model with average delay. To shape the response of the closed-loop system, a set of fuzzy reference models is established. Similarly, the same fuzzy sets of the system rule are employed to design a fuzzy neural-based control. The proposed control contains a radial-basis function neural network to learn the uncertainties caused by the approximation error of the fuzzy model (e.g., time-varying delays and parameter variations) and the interactions resulting from the other subsystems. As the norm of the switching surface is inside of a defined set, the learning law starts; in this situation, the proposed method is an adaptive control possessing an extra compensation of uncertainties. As it is outside of the other set, which is smaller than the aforementioned set, the learning law stops; under this circumstance, the proposed method becomes a robust control without the compensation of uncertainties. A transition between robust control and adaptive control is also assigned to smooth the possible discontinuity of the control input. No assumption about the upper bound of the time-varying delays for the state and the input is required. However, two time-average delays are needed to simplify the controller design: 1) the stabilized conditions for every transformed delay-free subsystem must be satisfied; and 2) the learning uncertainties must be relatively bounded. The stability of the overall system is verified by Lyapunov stability theory. Simulations as compared with a linear transformed state feedback with integration control are also arranged to consolidate the usefulness of the proposed control.[[notice]]補正完畢[[incitationindex]]SC

Discrete sliding-mode tracking control of high-displacement piezoelectric actuator systems

[[abstract]]We consider the modeling and control of a high-displacement piezoelectric actuator system (HDPAS). An HDPAS includes a multilayer LVPZT bender actuator (MLBA) and a low-pass filter. The MLBA is subject to hysteresis, bending modes, measurement noise, and external disturbance. Because the spillover associated with a reduced-order model of the MLBA has been known to have the potential to cause the instability of the closed-loop system, a low-pass filter is applied to reduce its effect. To obtain an acceptable model for the HDPAS, a band-limited input and its corresponding output are fed into a recursive least-squares parameter estimation scheme. The resulting model is then used for a controller design, which includes three features. First, a dead-beat to its filtered switching surface is achieved. Second, the H norm of the sensitivity function between the filtered switching surface and the output disturbance is simultaneously minimized to attenuate the effect of output disturbance. Third, a switching control based on Lyapunov redesign is used to further improve the tracking accuracy. To demonstrate the effectiveness of the proposed control, the experimental results of the HDPAS by using the proposed control are compared with those by a proportional integral differential (PID) control.[[incitationindex]]SCI[[booktype]]紙