Gains of a roll force AGC (Automatic Gain Controller) have been calculated at the first locked-on-time by FSU (Finishing-mill Set-Up model) in hot strip mills and usually these values are not adjusted during the operating time. Consequently, this conventional scheme cannot cope with the continuous variation of system parameters and circumstance, though the gains can be changed manually with the aid of experts to prevent a serious situation such as inferior mass production. Hence, partially uncontrolled variation still remains on delivery thickness. This paper discusses an effective online algorithm which can adjust the gains of the existing control system by considering the effect of time varying variables. This algorithm improves the performance of the system without additional cost and guarantees the stability of the conventional system. Specifically, this paper reveals the major factors that cause the variation of strip and explores the relationship between AGC gains and the effects of those factors through the analysis of thickness signal which occupy different frequency bands. The proposed tuning algorithm is based on the above relationship and realized through ANFIS (Adaptive-Neuro-based Fuzzy Interface System) which is a very useful method because its fuzzy logics reflect the experiences of professionals about the uncertainty and the nonlinearity of the system. The effectiveness of the algorithm is shown by several simulations which are carried out by using the field data of POSCO corporation (South Korea).

The goal of this study was to design a practical fuzzy controller of the cycle-to-cycle control for multi-joint movements of swing phase of functional electrical stimulation (FES) induced gait. First, we designed three fuzzy controllers (a fixed fuzzy controller, a fuzzy controller with parameter adjustment based on the gradient descent method, and a fuzzy controller with parameter adjustment based on a fuzzy model) and two PID controllers (a fixed PID and an adaptive PID controllers) for controlling two-joint (knee and ankle) movements. Control capabilities of the designed controllers were tested in automatic generation of stimulation burst duration and in compensation of muscle fatigue through computer simulations using a musculo-skeletal model. The fuzzy controllers showed better responses than the PID controllers in the both control capabilities. The parameter adjustment based on the fuzzy model was shown to be effective when oscillating response was caused due to the inter-subject variability. Based on these results, we designed the fuzzy controller with the parameter adjustment realized using the fuzzy model for controlling three-joint (hip, knee, and ankle) movements. The controlled gait pattern obtained by computer simulation was not significantly different from the normal gait pattern and it could be qualitatively accepted in clinical FES gait control. The fuzzy controller designed for the cycle-to-cycle control for multi-joint movements during the swing phase of the FES gait was expected to be examined clinically.

In this paper, a modified adaptive fuzzy sliding mode controller for a certain class of uncertain nonlinear systems is presented. We incorporate the fuzzy sliding mode control technique with a modified adaptive fuzzy control technique to design a modified adaptive fuzzy sliding mode controller so that the proposed controller is robust against the unmodeled dynamics and the approximation errors. Firstly, we establish a fuzzy model to describe the dynamic characteristics of the given uncertain nonlinear system. Then, based on the fuzzy model, a fuzzy sliding mode controller is designed. By considering both the information of tracking error and modeling error, the modified adaptive laws for tuning the adjustable parameters of the fuzzy model are derived based on the Lyapunov synthesis approach. Since the modified adaptive laws contain both the tracking error and the modeling error, it implies that the fuzzy model parameters would continuously converge until both the tracking error and modeling error converges to zero. An inverted pendulum control system is simulated to demonstrate the control performance by using the proposed method.

This study proposes a novel adaptive fuzzy control methodology to remove disadvantages of traditional fuzzy approximation based control. Meanwhile, the highly uncertain robot manipulator is taken as an application with either guaranteed robust tracking performances or asymptotic stability in a global sense. First, the design concept, namely, feedforward fuzzy approximation based control, is introduced for a simple uncertain system. Here the desired commands are utilized as the inputs of the Takagi-Sugeno (T-S) fuzzy system to closely compensate the unknown feedforward term required during steady state. Different to traditional works, the assumption on bounded fuzzy approximation error is not needed, while this scheme allows easier implementation architecture. Next, the concept is extended to controlling manipulators and achieves global robust tracking performances. Note that a linear matrix inequality (LMI) technique is applied and provides an easier gain design. Finally, numerical simulations are carried out on a two-link robot to illustrate the expected performances.