Time delay observer (TDO), thanks to the time delay control (TDC) concept, requires little knowledge of a plant model, and hence is easy to design, robust to parameter variation and computationally efficient, yet can reconstruct states rather reliable for nonlinear plant. In this paper, we propose an improved version of TDO that solves two problems inherent in TDO as follows: TDO displays large reconstruction errors due to low-frequency uncertainty and has some restrictions on selecting its gains. By introducing a low pass filter and a state associated with it, we obtain an enhanced time delay observer (ETDO). This observer turns out to have smaller reconstruction errors than those of TDO and not to have any restriction on selecting its gains, thereby solving the problems. Through performance comparison by transfer function and simulation, we validate the analysis results of two observers (TDO and ETDO) and evaluate the performances. Finally, through experiments on BLDC motor system, the analysis results are clearly conformed.

The inverted pendulum is a typical example of unstable systems and has been used for verification of designed control systems. It is also very popular in control education in laboratories, serving as a good example to show the utility of the state space approach to the controller design. This paper shows two kinds of experiment using inverted pendulum: one is the stabilization of a single spherical inverted pendulum by a plane manipulator using visual feedback, and the other is the state transfer control of a double pendulum. In the former experiment, the feedback stabilization using a CCD camera has major importance as an example of controller implementation with non-contact measurement. The latter involves the standard stabilizing regulation method and nonlinear control techniques. The details of the experimental systems, the control algorithms and the experimental results will be given.

The problems of mixed $H_2/H_{\infty}$ filtering design fer continuous and discrete time linear systems with time delay are investigated. The main purpose is to design a stable mixed $H_2/H_{\infty}$ filter which minimizes the H$_2$Performance measure satisfying a prescribed H$_{\infty}$ norm bound on the closed loop system in continuous-time case and discrete-time case, respectively. The sufficient conditions of existence of filter, the mixed $H_2/H_{\infty}$ filter design method, and the upper bound of performance measure are proposed by LMI(linear matrix inequality) techniques in terms of all finding variables. Also, we present optimization problems in order to get the optimal mixed $H_2/H_{\infty}$ filter in continuous and discrete time case, respectively.

In this paper, Haar wavelet-based neural network is described for the identification and control of discrete-time nonlinear dynamical systems. Wavelets are suited to depict functions with local nonlinearities and fast variations because of their intrinsic properties of finite support and self-similarity. Due to the orthonormal properties of Haar wavelet functions, wavelet neural networks result in a greatly simplified training problem. This wavelet-based scheme performs adaptively both the identification of nonlinear functions and the control of the overall system, while the multilayer neural network is applied to the control system just after its sufficient learning of the unknown functions. Simulation shows that the wavelet network can be a good alternative to a multilayer neural network with backpropagation.

This paper investigates the robust nonlinear H$_{\infty}$ filter with FIR(Finite Impulse Response) structure for nonlinear discrete time-varying uncertain systems represented by the state-space model having parameter uncertainty. Firstly, when there is no parameter uncertainty in the system, the discrete-time nominal nonlinear H$_{\infty}$ FIR filter is derived by using the equivalence relationship between the FIR filter and the recursive filter, which corresponds to the standard nonlinear H$_{\infty}$ filter. Secondly, when the system has the parameter uncertainty, the robust nonlinear H$_{\infty}$ FIR filter is proposed for the discrete-time nonlinear uncertain systems.

High-frequency electric resistance welding (HERW) technique is one of the most productive manufacturing method currently available for pipe and tube production because of its high welding speed. In this process, a heat input is controlled by skilled operators observing color and shape of bead but such a manual control can not provide reliability and stability required for manufacturing pipes of high grade quality because of a variety of bead shapes and noisy environment. In this paper, in an effort to provide reliable quality inspection, we propose a neural network-based method for classification of bead shape. The proposed method utilizes the structure of Kohonen network and is designed to learn the skill of the expert operators and to provide a good solution to classify bead shapes according to their welding conditions. This proposed method is implemented on the real pipe manufacturing process, and a series of experiments are performed to show its effectiveness.

The mathematical solutions of the stability convergence are important problems in system control. In this paper such problems are analyzed and resolved for system control using multilayer neural networks. We describe an algorithm to control an unknown nonlinear system with a disturbance, using a multilayer neural network. We include a disturbance among the modeling error, and the weight update rules of multilayer neural network are derived to satisfy Lyapunov stability. The overall control system is based upon the feedback linearization method. The weights of the neural network used to approximate a nonlinear function are updated by rules derived in this paper . The proposed control algorithm is verified through computer simulation. That is as the weights of neural network are updated at every sampling time, we show that the output error become finite within a relatively short time.

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used fur on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

In this paper, a posture control for nonholonomic mobile robots is proposed with an empirical basis. In order to obtain fast and consecutive motions in realistic applications, the motion requirements of a mobile robot are defined. Under the assumption of a velocity controller designed with the selection guidance of control parameters, the algorithm of posture control is presented and experimentally demonstrated for practicality and effectiveness.

This paper introduces a high-performance speed control system based on artificial neural networks(ANN) to estimate unknown parameters of a DC servo motor. The goal of this research is to keep the rotor speed of the DC servo motor to follow an arbitrary selected trajectory. In detail, the aim is to obtain accurate trajectory control of the speed, specially when the motor and load parameters are unknown. By using an artificial neural network, we can acquire unknown nonlinear dynamics of the motor and the load. A trained neural network identifier combined with a reference model can be used to achieve the trajectory control. The performance of the identification and the control algorithm are evaluated through the simulation and experiment of nonlinear dynamics of the motor and the load using a typical DC servo motor model.

This paper presents a recoil and counter recoil motion measurement system using linear variable differential transformers (LVDT). The output of the LVDT is obtained from the differential voltage of the secondary transformers. Since a transducer core is attached to the motion body, the output is directly proportional to the movement length of the core. Displacement, velocity and acceleration are measured from the LVDT. With a comparison between the measurement result and the reference value obtained by the highly accurate Vernier calipers, it is proved that the measurement system with the LVDT is applicable to the test of the moving part of the mechanism with better accuracy.