Seong-Ik Han

Recurrent fuzzy neural network backstepping control for the prescribed output tracking performance of nonlinear dynamic systems.

This paper proposes a backstepping control system that uses a tracking error constraint and recurrent fuzzy neural networks (RFNNs) to achieve a prescribed tracking performance for a strict-feedback nonlinear dynamic system. A new constraint variable was defined to generate the virtual control that forces the tracking error to fall within prescribed boundaries. An adaptive RFNN was also used to obtain the required improvement on the approximation performances in order to avoid calculating the explosive number of terms generated by the recursive steps of traditional backstepping control. The boundedness and convergence of the closed-loop system was confirmed based on the Lyapunov stability theory. The prescribed performance of the proposed control scheme was validated by using it to control the prescribed error of a nonlinear system and a robot manipulator.

Attitude and Direction Control of the Unicycle Robot Using Fuzzy-Sliding Mode Control

This paper proposes an attitude and direction control of a single wheel balanced robot. A unicycle robot is controlled by two independent control laws: the mobile inverted pendulum control method for pitch axis and the reaction wheel pendulum control method for roll axis. It is assumed that both roll dynamics and pitch dynamics are decoupled. Therefore the roll and pitch dynamics are obtained independently considering the interaction as disturbances to each other. Each control law is implemented by a controller separately. The unicycle robot has two DC motors to drive the disk for roll and to drive the wheel for pitch. Since there is no force to change the yaw direction, the present paper proposes a method for changing the yaw direction. The angle data are obtained by a fusion of a gyro sensor and an accelerometer. Experimental results show the performance of the controller and verify the effectiveness of the proposed control algorithm.

Improvement of Shape Recognition Performance of Sendzimir Mill Control Systems Using Echo State Neural Networks

High rigidity twenty-high Sendzimir mills (ZRMs) are widely used for rolling stainless steels, silicon sheets, etc. A ZRM uses a small diameter work roll to produce massive rolling forces. Since a work roll with a small diameter can be bent easily, strips often have complex shapes with mixed quarter and deep edge waves in the shape of plates. In order to solve this problem, fuzzy neural network controls are generally used for shape recognition in ZRM control systems. Among various neural network types, the multi-layer perceptron (MLP) is typically used in current ZRMs. However, an MLP causes the loss of a large amount of shape recognition data. To improve the shape recognition performance of ZRM control systems, echo state networks (ESNs) are proposed to be used. Through simulation results, it is found that shape recognition performance could be improved using the proposed ESN method.

Thixo forging process of wrought aluminum alloy fabricated by rotational helical shape stirrer

The manufacture of rheology materials from wrought and casting aluminum alloys using controlling solid fraction and crystal grain is demonstrated in this paper. The equipment to form the rheology material was designed so that shear force and applied pressure could be carefully and simultaneously applied using a mechanical stirrer. The problems caused by using this method with the thixo forging process were studied by investigating the mechanical properties of a sample that had a controlled solid fraction of 45–50 %.

Dynamic Speed Control of a Unicycle Robot

This paper presents a new control algorithm for dynamic control of a unicycle robot. The unicycle robot motion consists of a pitch that is controlled by an in-wheel motor and a roll that is controlled by a reaction wheel pendulum. The unicycle robot doesn`t have any actuator for a yaw axis control, which makes the derivation of the dynamics relatively simple. The Euler-Lagrange equation is applied to derive the dynamic equations of the unicycle robot to implement the dynamic speed control of the unicycle robot. To achieve the real time speed control of the unicycle robot, the sliding mode control and LQ regulator are utilized to guarantee the stability while maintaining the desired speed tracking performance. In the roll controller, the sigmoid-function based sliding mode controller has been adopted to minimize the chattering by the switching function. The LQR controller has been implemented for the pitch control to drive the unicycle robot to follow the desired velocity trajectory in real time using the state variables of pitch angle, angular velocity, angle and angular velocity of the wheel. The control performance of the two control systems form a single dynamic model has been demonstrated by the real experiments.

Integrated navigation and control for a HAUV system using adaptive super-twisting sliding mode control and indirect Kalman filtering estimation

In this article, an integrated control and strap down inertial navigation system (SIDNS) is designed using the super-twisting algorithm (STA) and the extended indirect Kalman filter (EKF) for the hovering autonomous underwater vehicle system (HAUV). The 6-DOF nonlinear hydraulic dynamics of the HAUV are separated into the known and uncertainty terms to simplify controller design of the proposed STA system. An adaptive law is constructed to compensate uncertainty and the EKF algorithm is used to estimate the navigation information measured from INS-DVL (Doppler velocity log) and other supplementary sensors. Simulation for the integrated STA and SIDNS system shows the efficacy of the proposed method.