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The purpose of this paper is to present the linear filtered positional generalized predictive controller (GPC) synthesis using both a positional process model and cost function to ensure stability and offset-free behavior (reference tracking and disturbance rejection), which involves selecting an integral polynomial weighting filter for the setpoint and output of the process, thereby extending the applicability of the predictive controllers to different reference shapes and step disturbances for handling chemical processes. Additionally, robustness aspects are incorporated into the control design of the weighting polynomials, an implementation which involves the filter tuning parameters using a multi-objective optimization based on genetic algorithm. Numerical simulations are conducted featuring two nonlinear chemical processes models (CSTR and boiler level) to assess the efficiency, stability and robustness of different reference shapes and load disturbance rejection.

This paper describes a new approach that has been applied since 2011 with the students of the Feedback Systems course to increase not only the control knowledge and approval rate but also to improve their hands-on practical skills and to prepare them for the industry. Alongside the theoretical lessons and laboratory activities, the students complete their activities with the design and implementation of a physical homemade plant where they make case studies in modeling, calibration, sensor and actuator electronic circuits, simulation, PID tuning and real-time control code under a step by step collection schedule. As result of this laboratory activity, significant progress is observed in the quality, motivation and learning (students feels so good in understanding control topics). The forth experiment of the final practical laboratory task is discussed and it is based on a PID tuning when applied to the fan-and plate, process to be built. Student evaluation is based on a questionnaire to obtain a useful measurement linked to the gain of the final performance.

The purpose of this paper is to present an alternative synthesis for the Generalized Predictive Controller (GPC) applied to a multiple-input multiple-output (MIMO) model, using both a positional plant model and cost function to deal with reference tracking, disturbance rejection and model-plant mismatch (MPM) with minimum control energy and closed-loop robustness. This new control design called Multivariable Filtered Positional GPC (MFP-GPC) uses an integral polynomial weighting filter for the setpoint and output of the plant, thereby extending the applicability of the predictive controllers. Additionally, robustness aspects are incorporated into the control design of the weighting polynomials, and this implementation involves the filter tuning parameters using a multi-objective optimization algorithm. Numerical simulations for a hydroelectric power and a methanol/water distillation plants show the effectiveness of the proposed control methodology.

In this paper novel tuning settings for Proportional-Integral-Derivative (PID) controllers are proposed. These tunings are based on approximations of the generalized predictive controller (GPC) resulting in pseudo predictive PID controllers. It is possible in this way to embed industrial PID controllers as predictive controllers without hardware changes being necessary. In order to obtain this pseudo predictive tuning the PID structure is matched to the GPC structure and approximations are taken in the cases where the structures are not equivalent. A first order model implies in a PI while a second order model in a PID controller designs, respectively. Depending on which tuning is used, either set-point tracking or closed-loop characteristics is favored. Future work will include results and comparisons of the proposed tuning with other methods from literature.

The purpose of this paper is first to review the standard Generalized Predictive Controller (GPC) design, second, to establish a comparative study between the GPC with a prefilter T f (q −1 ), the GPC with a parameter of the future reference trajectory and the GPC that includes a positional model but with a integral polynomial weighing factor for the error. Simulation results are shown and discussed.

The purpose of this paper is to derive an alternative tuning design from Generalized Predictive Controller (GPC) that is based on both positional plant model and cost function, involving the selection of an integral polynomial weighting filter. The obtained Filter Positional GPC can be transformed into RST structure and next to Filtered Internal Model and PID controllers. Numerical and experimental essays show the effectiveness of the control methodologies.

The paper presents a discrete design method for tuning of proportional-integral-derivative control loops by using the Smith predictor with feedback filter in order to guarantee better dynamic and target loop specifications for setpoint tracking and disturbance rejection acting on the process with long dead-time. Simulation and experimental essays show that the effectiveness of the dead-time compensating PID tuning over standard PID controller tuning methods is improved to meet the desired performance and to decrease the impact of the dead-time over the closed-loop stability.