The promise of improved productivity and quality has lead to numerous research investigations in machining process monitoring and control. Recent studies have demonstrated that careful attention must be paid to the regulation of multiple process modules within a single operation such that each module performs its function properly and adverse interactions between modules do not occur. This had lead to the development of supervisory control; particularly to the development of methodologies to systemati-cally construct and implement these controllers. However, no research study has investigated the effect of the production environ-ment on the design of supervisor controllers. In this paper, the design of supervisory controllers for various production environ-ment is studied. The design approach given in Landers and Ulsoy(1998) is applied to construct two supervisory machining control-lers that are experimentally implemented in a face milling operation. Comparisons with an experimental implementation without process control illustrate the benefits of utilizing process controllers that are coordinated properly. The results also show that the given design approach may be used to construct supervisory controllers for different types of production environments.

We studied the use of time-related information, with and without prediction, to support human operators performing moni-toring and control tasks in the process. Based on monitoring and control techniques used for Project Management we developed a display design for the process industries. A simulated power plant was used to test the hypothesis that availability of predictions along with information on past trends can improve the performances of the human operator handling faults. Several designs of dis-plays were tested in the experiment in which human operators had to detect and handle two types of faults(local and systems wide) in the simulated electricity generation process. Analysis of the results revealed that temporal data, with and without prediction, signifi-cantly reduced response time. Our results encourage the integration of temporal information and prediction in displays used for the control processes to enhance the capabilities of the human operators. Based on the analysis we proposed some guidelines for the de-signer of the human interface of a process control system.

In this paper, adaptive algorithms on the sliding model control for robust tracking control of robust manipulators are presented. The presented algorithms use adaption laws for tuning both the sliding mode gain and the thickness of the boundary layer to reject a disconitnuous control input, and to improve the tracking performance. It is shown that the robustness of the developed adaptive algorithms are guaranteed by the sliding mode control law and that the algorithms are globally convergent in the presence of disturbances and modeling uncertainties. Computer simulations are performed for a two-link manipulator, and the results show good properties of the proposed adaptive algorithms under large mainpulator parameter uncertainties and disturbances.

In this study, we propose a two degree of freedom robust output tracking control method for a class of nonlinear system. We consider hyperbolically nonminimum phase single-input single-output uncertain nonlinear systems. We also consider the case that the nominal input-state equation is differentially flat. Nominal stable state trajectory is obtained in the flat output space via the flat output. Nominal feedforward control input is also computed from the nominal state trajectory. Due to the nature of the method, the generated flat output trajectory and control input are noncausal. Robust feedback control is designed to stabilize the systems around the nominal trajectory. A numerical example is given is given to demonstrate that robust tracking is achieved.

In this paper, we consider the guaranteed cost filtering design method for time-varying delay system with parameter uncertainties by LMI(Linear Matrix Inequality) approach. The objective is to design a stable guaranteed cost filter which minimizes the guaranteed cost fo the closed loop systems in filtering error dynamics. The sufficient conditions for the existence of filter, the guaranteed cost filter design method, and th guaranteed cost upper bound are proposed by LMI technique in terms of all finding variables. Finally, we give an example to check the validity of the proposed method.

In this paper, a parameter estimator is developed for the plant model whose structure is represented by the Takagi-Sugeno model. The essential idea behind the on-line estimation is the comparison of the measured stated with the state of an estimation model whose structure is the same as that of the parameterized model. Based on the parameter estimation scheme, and indirect Model Reference Adaptive Fuzzy control(MRAFC) scheme is proposed to provide asymptotic tracking of a reference signal for the systems with uncertain for slowly time-varying parameters. The developed control law and adaptive law guarantee the boundedness of all signals in the closed-loop systems. In addition, the plant state tracks the state of the reference model asymptotically with time for any bounded reference input signal.

In this paper, we propose the design procedure of advance Polynomial Neural Networks(PNN) architecture for optimal model identification of complex and nonlinear system. The proposed PNN architecture is presented as the generic and advanced type. The essence of the design procedure dwells on the Group Method of Data Handling(GMDH). PNN is a flexible neural architecture whose structure is developed through learning. In particular, the number of layers of the PNN is not fixed in advance but is generated in a dynamic way. In this sense, PNN is a self-organizing network. With the aid of three representative numerical examples, compari-sons show that the proposed advanced PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and generalization capabilities of model is evaluated and also discussed.

How to promote students interest is very important in undergraduate engineering education. One of the techniques for achieving this is select appropriate projects and to integrated them with regular courses. In this paper, a voice recognition system for autonomous robots is proposed as a project to educate students about microprocessors efficiently. The proposed system consists of a microprocessor and a voice recognition processor that can recognize a limited unmber of voice patterns. The commands of autono-mous robots are classified and are organized such that one voice recognition processor can distinguish robot commands under each directory. Thus. the proposed system can distinguish more voice commands than one voice recognition processor can. A voice com-mand systems for three autonomous robots is implemented with a microprocessor Inter 80CI196KC and a voice recognition processor HM2007. The advantages in integrating this system with regular courses are also described.

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that he selected solution become the global optimal solution by optimizing the Akaikes information criterion expressing the quality of the inference rules. The trajectory tracking simulation and experiment of the polishing robot show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding mode controller provides reliable tracking performance during the polishing process.

This paper describes the design of a two-axis force sensor for robots finger. In detects the x-direction force Fx and y-direction force Fy simultaneously. In order to safely grasp an unknown object using the robots fingers, they should detect the force or gripping direction and the force of gravity direction, and perform the force control using the forces detected. Therefore, the robots hand should be made by the robots finger with tow-axis force sensor that can detect the x-direction force and y-direction force si-multaneously. Thus, in this paper, the two-axis force sensor for robots finger is designed using several parallel-plate beams. The equations to calculate the strain of the beams according to the force in order to design the sensing element of the force sensor are derived and these equations are used to design the aize of two-axis force sensor sensing element. The reliability of the derive equa-tions is verified buy performing a finite element analysis of the sensing element. The strain obtained through this process is compared to that obtained through the theory analysis and a characteristics test of the fabricated sensor. It reveals that the rated strains calculated from the derive equations make a good agreement with the results from the Finite Element Method analysis and from the character-istic test.