In large-scale industrial plants, the process control system has multiple system servers to provide seamless services to plant operators irrespective of any system server's failure. In this paper, we present an autonomic connection scheme between the system server and the Human-Machine Interface application (HMI) without additional configuration. The proposed scheme is based on the concept of autonomic computing, supporting the fault-tolerant and/or load-balancing features. Finally, the mathematical analysis shows that the proposed scheme can provide high availability of services to users.

Since most process systems have nonlinearities, it is necessary to consider the design of schemes to deal with such systems. In this paper, a new design scheme of PID controllers is proposed. This scheme is designed based on the internal model control (IMC) which is a kind of the model driven controllers. The internal model consists of the design-oriented model and the full model. The full model is designed by using the neural network. The primary PID control system is firstly constructed for the augmented system which is composed of the controlled object and the internal model, and this control system is designed by the pole-assignment method. Furthermore, the secondary PID controller is designed in order to remove the steady state error. Finally, the effectiveness of the newly proposed control scheme is numerically evaluated on a simulation example.

This paper describes a design scheme of generalized minimum variance controllers (GMVC) using a group method of data handling (GMDH) network for nonlinear systems. Concretely, the predictive value of the output required in the GMVC is obtained by using the GMDH which is a kind of multilayered networks. Since the predictive value of the output in GMVC is calculated by a nonlinear model which is generated by the GMDH network, one can expect to obtain the better control performance than that by the conventional scheme. The behavior of the newly proposed control scheme is evaluated on numerical examples.

PID control schemes based on the classical control theory, have been widely used for various real control systems. However, in practice, since it is considerably difficult to determine the PID parameters suitably, lots of researches have been reported with respect to tuning schemes of PID parameters. Furthermore, several self-tuning and auto-tuning techniques in the PID control have been reported for systems with unknown or slowly time-varying parameters. On the other hand, so-called a generalized predictive control (GPC) scheme has been reported as a useful self-tuning control technique for unknown and/or time variant delay systems. In this paper, a new self-tuning predictive PID control algorithm based on a GPC criterion is proposed.

PID control schemes have been widely used in most of process control systems. Most of these processes are often treated as first-order systems with dead times. And also, in many cases, PID parameters are usually tuned based on the process parameters, i. e. , the time constant, the dead time and the process gain. However, since these process parameters can not be obtained exactly, it is well known that it is difficult to find the suitable PID parameters in practice. In this paper, we propose a genetic tuning scheme of PID parameters for first-order systems with large dead times. The authors have already proposed a tuning method of PID parameters using a genetic algorithm (GA), which was based on the relationship between PID control and generalized minimum variance control(GMVC) laws. In practice, for large dead time systems, first-order low pass pre-filters are often used. The proposed method is an extended version of the previously proposed method mentioned above to the system with a pre-filter due to the large dead time, i. e. , a tuning method of both PID parameters and the pre-filter using a GA. The proposed control scheme is numerically evaluated on some simulation examples.

Cerebellar Model Articulation Controller (CMAC) has been proposed as one of artificial neural networks. This paper describes a design scheme of intelligent control system consists of some CMACs. Each of CMACs is trained for the specified reference signal. A new CMAC is generated for unspecified reference signals, and the CMAC whose reference signal is nearest for the new reference signal, is eliminated. Therefore, since the reference signals are removed from the input signals of the CMAC, the proposed intelligent controller can be designed with fairly small memories.

Lots of self-tuning control schemes have been proposed for tuning the parameters of control systems. Among them, pole-assignment schemes have been widely used for tuning the parameters of control systems with unknown time delays. They are usually classified into two methods, the implicit and the explicit methods according to how to identify the parameters. The latter has an advantage to design a control scheme by taking account of the stability margin and control performance. However, it involves a considerably computational burden to solve a Diophantine equation. A simple scheme is proposed in this paper, which can construct a multivariable self-tuning pole-assignment control system, while taking account of the stability margin and control performance without solving a Diophantine equation.