In industrial control processes, control performance influences the quality of products and utilization efficiency of energy; hence, the controller is necessarily designed according to user-desired control performance. Ideal control performance requires fast response for transient state and maintaining user-specified control performance for steady state. Hence, an algorithm to tune controller parameters to match the requirements for transient state and steady state is proposed. Considering the partial learning ability of the cerebellar model articulation controller (CMAC) neural network, it is utilized as a “tuner” of controller parameters in this study, since then the controller parameters can be tuned in both transient and steady states. Moreover, the fictitious reference iterative tuning (FRIT) algorithm is combined with CMAC in order to avoid problems, which may be caused by system modeling error and by using only a set of closed-loop data, the desired controller can be calculated in an off-line manner. In addition, the controller selected is a proportional-integral-derivative (PID) controller. Finally, the effectiveness of the proposed method is numerically verified by using some simulation and experimental examples.

This paper introduces a new application of adaptive control theory to power control in a code division multiple access (CDMA) cellular system operating over mobile fading radio channels. Conventional feedback power control algorithms allow the base station to send a power command to either raise or lower each user's transmission power according to a bang-bang-like control policy. In this paper, we present an adaptive minimum-variance power control methodology which can be shown to improve power control performance consistently against a random nature of the near-far effect, shadowing and fast varying fading. Two adaptive implementations are considered: direct and indirect control. In the indirect adaptive control, a minimum-variance controller is combined with a constrained estimation algorithm to ensure the stability of a link gain model. In the direct adaptive control, the controller parameters are obtained directly from a standard estimation algorithm. Our simulations have shown that the proposed adaptive minimum-variance power control schemes provide much smaller error variance than the conventional fixed-step bang-bang control scheme and consequently the reverse channel capacity of the CDMA system can be significantly increased.

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 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.