In this paper, the integration of dynamic plant-wide optimization and distributed generalized predictive control (DGPC) is presented for serially connected processes. On the top layer, chance-constrained programming (CCP) is employed in the plant-wide optimization with economic and model uncertainties, in which the constraints containing stochastic parameters are guaranteed to be satisfied at a high level of probability. The deterministic equivalents are derived for linear and nonlinear individual chance constraints, and an algorithm is developed to search for the solution to the joint probability constrained problem. On the lower layer, the distributed GPC method based on neighborhood optimization with one-step delay communication is developed for on-line control of the whole system. Simulation studies for furnace temperature set-points optimization problem of the walking-beam-type reheating furnace are illustrated to verify the effectiveness and practicality of the proposed scheme.

This paper discusses design of Generalized Predictive Control (GPC) scheme. GPC is designed in two cases; the first is a dual-rate (DR) system, where the sampling interval of a plant output is an integer multiple of the holding interval of a control input, and the second is a fast-rate single-rate (FR-SR) system, where both the holding and sampling intervals are equal to the holding interval of the DR system. Furthermore, the relation between them is investigated, and this study gives the conditions that FR-SR and DR GPC become equivalent. To this end, a future reference trajectory of DR GPC is rewritten, and a future predictive output of the FR-SR GPC is rearranged.

This work presents an application of the previously proposed Support Vector Machines Based Generalized Predictive Control (SVM-Based GPC) method [1] to the problem of controlling chaotic dynamics with small parameter perturbations. The Generalized Predictive Control (GPC) method, which is included in the class of Model Predictive Control, necessitates an accurate model of the plant that plays very crucial role in the control loop. On the other hand, chaotic systems exhibit very complex behavior peculiar to them and thus it is considerably difficult task to get their accurate model in the whole phase space. In this work, the Support Vector Machines (SVMs) regression algorithm is used to obtain an acceptable model of a chaotic system to be controlled. SVM-Based GPC exploits some advantages of the SVM approach and utilizes the obtained model in the GPC structure. Simulation results on several chaotic systems indicate that the SVM-Based GPC scheme provides an excellent performance with respect to local stabilization of the target (an originally unstable equilibrium point). Furthermore, it somewhat performs targeting, the task of steering the chaotic system towards the target by applying relatively small parameter perturbations. It considerably reduces the waiting time until the system, starting from random initial conditions, enters the local control region, a small neighborhood of the chosen target. Moreover, SVM-Based GPC maintains its performance in the case that the measured output is corrupted by an additive Gaussian noise.

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.